Machine Learning in Photovoltaic Systems1st Jorge Felipe Gaviria

dept. name of organization (of Aff.)name of organization (of Aff.)

City, Countryemail address

2nd Gabriel Narvaezdept. name of organization (of Aff.)

name of organization (of Aff.)City, Countryemail address

3rd Camilo Guillendept. name of organization (of Aff.)

name of organization (of Aff.)City, Countryemail address

4th Luis Felipe Giraldodept. name of organization (of Aff.)

name of organization (of Aff.)City, Countryemail address

5th Michael Bressandept. name of organization (of Aff.)

name of organization (of Aff.)City, Countryemail address

Abstract—This paper presents a review of up-to-date Machineand Deep Learning (MDL) techniques applied to Photovoltaic(PV) systems. It examines the use of MDL applied to control,islanding detection, management, fault detection and diagnosis,forecasting irradiance and power generation, sizing, and siteadaptation in PV systems. This review is done by summarizingmore than 100 research articles regarding the fields described.Moreover, one of the paper’s main contributions is providing anopen-source case study for each of the fields based on one ofthe articles being summarized. This open-source will be helpfulfor new researchers to rapidly familiarize themselves with someup-to-date MDL methodologies applied to the field of interest.However, since this article will be published, the case studies areremoved for this version.

Index Terms—Open-source, Machine and Deep Learning, Pho-tovoltaic Systems, Case Studies

I. INTRODUCTION

Renewable energy is the fastest-growing energy sourcein the world. Among the renewable energy sources, solargeneration is perhaps the fastest-growing since it is expectedthat it will climb from 11% of total US renewable generationin 2017 to 48 percent by 2050 [1]. Moreover, according to[2], the Global Solar Photovoltaic (PV) capacity is estimatedto increase from 593.9 GW in 2019 to 1582.9 GW in 2030following capacity additions by China, India, Germany, theUS, and Japan.

However, implementing PV systems still implies high costsand efficiency issues that need to be resolved soon. Effortsare still being made to decrease the costs of implementingPV systems while increasing their efficiency, easing theirimplementation, and coupling to electric grids. Machine andDeep Learning (MDL) algorithms are one of the leadingresources used to enhance the performance of PV systems.

Nevertheless, the rapid growth of this field makes it difficultfor researchers to keep up to date with the current trends beingused. Furthermore, the articles studied usually do not includecode with which new researchers entering the field can rapidlyfamiliarize themselves with the presented topics. A compre-

hensive review on the application of Artificial Intelligence(AI) algorithms applied in PV systems was published in 2017[3]. However, since this is an accelerating field, several newMDL methods have already been implemented in PV systems.Therefore, in this paper, a review regarding the new trendsof MDL being used for PV systems is presented. We firstprovide a brief introduction of the algorithms being applied inthe field in Section II. Then, the different categories are pre-sented. These categories include control methods (Section III),islanding detection (Section IV), management (Section V),fault detection techniques (Section VI), forecasting irradianceand power generation (Section VII), sizing (Section VIII) andsite adaptation (Section IX) as depicted in Fig. 1. Additionally,one of the main contributions of this review is to provide a casestudy for each of the described categories, the code of whichcan be found in several shared GitHub repositories. Each open-source code is based, to some extent, on one of the articles ofeach section. However, in this version, this repositories will notbe found. Afterward, a summary of some of the main open-source resources used in several of the papers is presentedin Section X. Finally, Section XI provides conclusions of thereview carried out.

II. MACHINE AND DEEP LEARNING TECHNIQUES IN PVSYSTEMS

In this section, some brief introductions to the algorithmsapplied to the field are presented.

A. Neural Networks

Artificial Neural Networks (ANN) are electronic modelsbased on the neural structure of the brain [4]. These modelsare designed to learn from experience thanks to their basicunit called neurons which compose each layer. Each input toa neuron is multiplied by connection weights. These productsare then summed, and then a firing action is produced via anactivation function applied to the sum. The training carriedout in supervised scenarios consists of a feed-forward process

Machine learningin photovoltaic

systemsIslandingdetection

Management

Demand response

Planning and stability

Energy

SizingmethodsStorage battery

PV array

Forecasting

Solar radiation

Demand / load

Energy consumption

Energy generationDispatch

Control

Voltage andFrequency

MPPTUniform shade

Partial shade

Fault detection andclassification

Zone identification

Energy consumption

Site adaptation

Centralized

DecentralizedMapping

Fig. 1. Problem categories in PV systems in which MDL has been applied to address them.

in which the model generates some predictions and, basedon a labeled data, an error is calculated. Usually, the back-propagation technique is then used to feedback the errorbetween the predictions and the actual data [5]. This erroris subsequently used to adjust the weights in the neurons and,thus, learning what input should produce what type of output.

B. Reinforcement Learning

Reinforcement learning (RL) is a machine learning controltechnique that enables an agent to learn control mechanismsby trial and error using stimulus (rewards) provided by an in-teracting environment described as a Markov Decision Process[6]. Some essential elements of an RL problem are the state(current situation of the agent), the reward (the feedback ofthe environment), and the policy (the method that maps theagent’s states or states/actions pairs to actions). The primarygoal of the algorithm must be to find a control policy thatmaximizes an expected return received by interactions withits environment. This maximization is done by using eitherthe value function or the action-value function.

Some of the currently used RL methods are Q-learning (anoff-policy algorithm), SARSA (on-policy), and their variantswhich are described by Sutton and Barto in [7]. One approachis defined as using a state and action space discretization tech-nique that divides the continuous state and action space into afinite number of regions (Q-table). Another method uses deepreinforcement learning (DRL) models, where NN is used toapproximate a value function or a policy function, allowing forcontinuous state and action spaces. These algorithms includedeep Q-learning (DQL) [8], where the Q-table is replacedby a NN or more advanced algorithms that include actor-critic networks, which use deep deterministic policy gradients(DDPG) techniques [9].

C. Fuzzy Logic

Fuzzy logic is a multi-valued logic that allows intermediatevalues to be defined as binary evaluations [10]. Its approachaims to imitate the way humans approach decision-making.

D. Support Vector Machines

Support Vector Machine (SVM) is an ML model that canperform linear and nonlinear classification, regressions, andoutlier detection [11]. The main idea of this algorithm is toseparate classes using decision boundaries that may be definedby a hyperplane. When the data classified can not be clearlyseparated, extra dimensions may be added using kernels viamathematical transformations to become separable.

E. Convolutional Neural Networks

Convolutional Neural Networks (CNNs) are a type of NNthat specialize in processing data that has grid-like topology,such as the one found in images [12]. The weights forthis type of NN are filters or convolutional kernels of acertain length that are smaller than the matrix or vector fromwhich information is extracted [11]. Convolutional layers areusually followed by pooling layers which reduce the spatialsize of the representation made. The most popular poolinglayers are maximum, average, and global pooling layers [13].Nonlinearity layers, which use sigmoid, tanh, or reLU, arealso placed directly after convolutional layers to introducenonlinearity to the activation map.

F. Recurrent Neural Networks

Recurrent Neural Networks (RNN) are a class of net thatcan work on data sequences of arbitrary length to predictfuture outcomes or classify events. This is done by inferringrelationships between the frame through its connections thatpoint backwards [11]. Each neuron in an RNN layer receivesan element of the analyzed frame as input as well as theoutput of the previous prediction (which was based on the

last analyzed element of the frame). This structure implies thatRNNs allow previous outputs to be used as inputs while havinghidden states to take into account historical information [14].The main problem with this type of network is the difficultyof accessing information from a long time ago, generating thevanishing/exploding gradient problem.

G. Long Short-Term Memory

Long Short-Term Memory Network (LSTM) is a specialkind of RNN that is capable of learning long-term depen-dencies [15]. An LSTM remembers information for longperiods due to its structure that includes gates and a cell state.It is composed of the forget gate, the input gate, and theoutput gate, which allow information to be passed through toremember past information. The cell state might, for example,store information that occurred in the first part of a frame tothe last analysis of an element of the frame. This cell statesolves the vanishing/exploding gradient problem.

H. Gated Recurrent Units

Gated Recurrent Units (GRU) are similar to LSTM and aimto solve the vanishing gradient problem [16]. GRUs make useof reset and update gates. These gates allow the network todefine what information is passed through its output. However,they do not use a cell state as the LSTM does.

I. Random Forest

Random Forest (RF) consists of a large number of decisiontrees that work jointly. Each tree makes a class prediction, andthe class with the most votes becomes the prediction of themodel [17].

III. MACHINE AND DEEP LEARNING IN CONTROL OF PVSYSTEMS

The need for advanced control techniques to ensure reliableintegration of PV systems to the grid is rapidly rising. Asstated in [18], the implementation of advanced communica-tions infrastructures and power-full electronic devices open upthe possibility to implement advanced control techniques in thepower system field. In this section, the machine learning-basedcontrol techniques applied for Maximum Power Point Track-ing and Voltage/Frequency control for PV electric systems arepresented.

A. MPPT Machine and Deep Learning Algorithms

The total produced power by a PV system is influencedprimarily by two factors, the solar irradiance and the celltemperatures of the solar panel arrays. To consistently obtainthe highest efficiency in solar power production, maximumpower point tracking (MPPT) controllers are implemented.The most commonly applied methods for MPPT have beenthe Perturb and Observe method, and the Incremental Conduc-tance methods, which are practically adopted because of theireasy implementation [19]. However, these methods usuallyneed high calibration levels to prevent high oscillations aroundthe maximum power point during its search. Furthermore,when partial shading conditions are present, these algorithms

tend to get trapped at local maximums leading to lower energyconversions. This obstacle is due to the nonlinear and time-varying characteristics of the PV array affected by changesin the atmospheric and load conditions [20]. Fig. 2 shows anexample of 3 PV modules where partial shading conditions arepresent, and Fig. 3 shows two local maximum and a globalmaximum for different levels of voltage due to the conditions.

Fig. 2. Partial Shading PV array.

Fig. 3. PV array power curve under partial shading conditions.

Recently, machine learning techniques have been appliedto solve these problems, and they achieve superior efficien-cies than traditional methods. A review regarding intelligenttechniques applied for MPPT control in photovoltaic systems

was made in 2014 [20]. However, recent advances in a field,such as using reinforcement learning algorithms for MPPTand artificial neural network models, were not included inthe review. In this section, we explore the different machinelearning algorithms used for MPPT.

For instance, ANNs were used in [21]. The idea was toobtain data from a Perturb and Observe algorithm to train amodel that learns when to increase or decrease the duty cyclebased on this data. The input variables were the output powerderivative and the voltage derivative. The results showed hightracking accuracy, high response time, and low overshoot. Sim-ilar algorithms were proposed in [22], and [23] in which theyachieved higher response times than conventional methods.

In [24], a fuzzy logic control was proposed in which thealgorithm was tested under four different conditions. For themembership function, the researchers used triangular func-tions. The accuracy achieved by the algorithm varied from94.8% to 99.4%. However, the test conditions did not includepartial shading conditions. A similar algorithm using fuzzylogic control was implemented in [25], which achieved similarresults.

Another machine learning algorithm that had achieved ex-cellent results for MPPT is RL. One of the first uses of RL inMPPT was implemented by Kofinas et al. [26]. Arguing thatRL has high convergence stability with shorter computationaltime than other meta-heuristic methods, the authors proposed aQ-Learning tabular approach to track the MPP. The state-spacewas defined by the voltage, the normalized current, and anangle relating to current and voltage changes. The action spacewas defined as a list of steps to change the duty cycle, andthe reward was based on positive or negative changes in theoutput power. The results obtained showed that the algorithmdesigned outperformed the conventional perturb method andobserved in three different scenarios, in which irradiance andtemperature were changed. This research leads to other similartabular Q-learning methods applied to MPPT such as [27],where the authors’ main contributions were comparing theobtained RL control agent to fuzzy logic-sliding mode controland incremental conductance-sliding mode control, achievingbetter results with the RL algorithm. They also performedexperimental tests of the created algorithm and achievedgood results. However, both of these papers did not includecomparisons between different RL algorithms. This is whyin [28], two different reinforcement learning methods appliedfor MPPT were implemented and compared. In the article, thealgorithms used were based on Q-learning and SARSA. Thevariables used as the state space were the open-circuit voltage,the short circuit current, and the maximum power point understandard test conditions. The action space used was definedas discrete changes in the duty cycle, and an epsilon-greedystrategy was used. The reward was defined as 1 if the PVsource operates at the MPP; otherwise, it was defined as thenormalized range of the derivative of the power produced,generating a positive reward if the change was positive anda negative reward if the change was negative.

Moreover, the evolutionary methods of Big Bang-Big

Crunch and Genetic algorithms were also applied to themodels. The algorithms were compared to a fuzzy logic con-troller using Matlab, and the results showed that the SARSAalgorithm outperformed the other two methods. Although thesepapers achieved remarkable results, none of them tested thealgorithms created in partial shading scenarios.

In [29], a tabular Q-learning algorithm was proposed fortracking the Global MPP at partial shading conditions. ABoltzmann exploration policy was used with a similar rewardpolicy as previous papers mentioned. The state-space wasmade of the current duty cycle value, the current power, andthe previous value of the duty cycle. The action space wasa set of discrete increments and decrements in the duty cyclevalue. The results achieved were compared to particle a swarmoptimization algorithm and showed that the RL algorithmreduced by 80.5% to 98.3% the time required for detectingthe global MPP.

Although these papers achieved remarkable results, none ofthem utilized deep learning methods applied to RL. In thework of Phan et al. [30], a deep Q-learning (DQL) agent anda deep deterministic policy gradient (DDPG) agent [31] wereproposed as ways of conducting an MPPT control, focusingprimarily on partial shading conditions. This methodologyallowed the agents to deal with continuous state spaces. Thestate-space was defined as the combination of voltage, current,duty cycle, and current perturbation. The action space for theDQL agent was defined as a set of discrete steps that changedthe duty cycle while the DDPG handled continuous actionspaces. The reward function was defined using the changesin power from one step to another, the MPP at standard testconditions, and a penalty if the agent actions caused the dutycycle to step out of bounds. The models were trained andtested using Matlab/Simulink. The results obtained by themodels were compared to the commonly used Perturb andObserve, evidencing better results in the eight scenarios inwhich they were tested. The agents tracked the global MPP inalmost all partial shading scenarios except for one. AlthoughMatlab provides an environment for training and testing theRL agents, this is not an open-source software that everyresearcher can access. This is why Avila et al. [32], [33] madea deep learning model-free algorithm based on RL, which wastrained and tested in a custom developed OpenAI Gym [34]environment in Python. The algorithm proposed is based onDDPG in [33].

Furthermore, the inverted gradient and the delay twinsalgorithm were implemented to solve the problem in [32]. Thecontinuous state space for the algorithm proposed in thesepapers was the voltage, power, and the difference betweenthe actual power and the previous power. The action spacewas continuous and defined as the disturbance applied to thecontrollable variable of the voltage. It is important to note thatthe researchers did not simulate a complete boost convertermodel in OpenAI Gym for the environment. The reward wasdefined as the current power divided by a normalization factorif the change of power was positive and minus one if thechange of power was negative. An epsilon-greedy exploration

strategy with linear decay was also used. The results obtainedby the researchers showed that the maximum operating powerpoint using the algorithm was less than 1% of the theoreticalmaximum power point.

B. Voltage and Frequency ML control methods

The integration of PV in distributed networks generates volt-age swings due to the rapid power fluctuations generated byPV. This voltage swing generates voltage quality degradationand voltage stability issues, which may breach establishedregulations [35]. Intelligent voltage control models have beenrecently implemented to maintain the voltage magnitudes ofbuses within a desirable defined range. This regulation is madeby applying the control algorithms to the DC/AC inverterresponsible for generating the three-phase AC voltage [3]. In[36], two voltage control categories defined are centralizedcontrol and decentralized control, which will be further ana-lyzed in the following sections.

1) Centralized Voltage Control: Centralized voltage con-trol schemes use sophisticated communications networks toregulate voltage using a system operator [37], [38]. It requireshighly reliable communication schemes using protocols suchas DNP 3.0 or IEC 61850 [36]. This control is done by usingonly one agent or multiple agents that can regulate multiplezones in a grid with an injection of PV.In [39], the authors used a DDPG algorithm for coordinatingmultiple PV smart inverters to regulate the voltage in a PV-grid system by modulating the real and reactive power in eachpoint of common coupling (PCC). The authors described inoutstanding detail the characteristics of the agent and how itwas trained in the paper. The exploration strategy used wasthe Ornstein-Uhlenbeck exploration. The state-space consistedof voltage magnitudes at each bus, real and reactive powergeneration/consumption, and actual values of the loads. Theaction space was considered as each inverter’s reactive powergenerated. The rewards consisted of large penalties due tobreaching voltage limits and a negative reward proportional tototal reactive power dispatched to minimize the PV productionreduction. For most of the papers presented, the reward valuedepended on the voltage value at each bus according to thezones shown in Fig. 4.

The algorithm was trained and tested in the IEEE 37 bussystem. To simulate the power flow, the solver OpenDSS [40]was used. Compared to the benchmark method of Volt-Var, theDDPG was able to utilize reactive power more efficiently withmuch less curtailment incurred. The DDPG generated a powerloss of just 4.1% of the Volt-Var method losses that resultedfrom reactive power generation while also avoiding over orunder voltages in all nodes. A similar approach was proposedin [41]. A multi-agent DDPG method was tested in theIllinois 200-Bus system with PV integration considering loadgeneration changes, N-1 contingencies, and a weak centralizedcommunication environment. Furthermore, the methodologyconsisted of 3 agents that controlled certain parts of the 200-Bus system (divided into three zones). The state-space wasdefined as system-wide bus voltage magnitudes, phase angles,

Fig. 4. Voltage profile zones in most of the papers.

generations, and power flow. The action space was a vector ofbus voltage magnitudes desired. The rewards took into accounta motivation to reduce the deviation of bus voltage magnitudesfrom a reference value and a penalization when a voltagebreach occurs in the system.

2) Decentralized Voltage Control: In this type of controlscheme, the objective is to provide several agents with localcontrol using low-forms of communication systems [42]. Thisscheme implies that the coordination between agents is done inan automated manner, locally (in each bus where it is needed)and without a centralized operator, which may optimize localgrid operation [43], [44]. In [45], for example, a multi-agentdeep reinforcement learning-based (MADRL) approach for adistribution system with high penetration of PV was proposed.The MADRL was modeled as intelligent agents with differentcontrol strategies. The idea was that the agents interactedwith each other by modeling each other’s policies duringtraining. The state-space of the agents consisted of the activeand reactive power of load demand (where the agent wasconnected) and the active power injection of PV. Moreover,the action space of each agent was the reactive power valueof the corresponding PV inverter to which the agent wasassociated. Lastly, the reward was defined as the negative sumof the absolute value of the voltage deviation at each bus.For training the agents, an attention critic for each agent wasused. The agent critic uses a centralized approach in whichthe critic of a specific node was a function of all agents’states and actions. This approach was made to address theperformance degradation problem due to the input of the criticgrowing linearly with the agent number. The execution wasthen decentralized by removing the critic. The training andtesting of the algorithm were done using the IEEE 33 bussystem [46]. The proposed algorithm was compared to thedroop control method and achieved shorter voltage fluctuationswhile also preventing over and under-voltage.

A similar approach was designed in [47], where a multi-agent DDPG algorithm was used for mitigating voltage viola-tions with centralized training and decentralized application.However, the researchers determined the dispatch of the on-load tap changers and capacitor banks day-ahead, based on

the hourly PV and load predictions. Then, the agents weretrained offline using this data as their input. The state-spacewas obtained from on-load tap changers and capacitors banks.The actions space was defined as the difference of PV reactivepower output between two neighboring time sections. Like theprevious paper, the agents were trained in a centralized waywhile its implementation was decentralized. It is important tonote that instead of using a critic network, the gradient ofthe action-value function was derived based on the voltagesensitivity method (more details are addressed in the paper).The algorithm was tested in multiple scenarios. It was demon-strated that it could better mitigate voltage violations andreduce power loss than other methods. Another decentralizedmethod was proposed in [48]. In this case, the authors imple-mented a multiple-agent actor-critic RL algorithm with a radialbasis function to determine the reactive power output of the PVinverters. The results were compared to an algorithm that usedparticle swarm optimization, achieving a better performance.

IV. MACHINE AND DEEP LEARNING IN ISLANDINGDETECTION

Islanding is the process through which an on-grid Dis-tributed Generation (DG) system is disconnected from the gridwhile the DG system continues to power the load. When un-planned due to line tripping or equipment errors, this scenariomay generate power quality problems due to voltage/frequencyinstability and even damage electrical instruments [49]. Forthis reason, detecting unplanned islanding conditions is criticalfor DG reliance. According to standards such as the IEEEStd. 1537-2018 [50] and IEC 62116:2014 [51], an unplannedislanding detection condition most be detected and stoppedwithin 2 seconds since the formation of the event. As describedby Manikonda et al. [49], there are five types of islandingdetection techniques, which can be divided into passive, active,hybrid, remote, signal processing methods, and intelligentmethods. Passive methods tend to fail due to the need to setup a threshold value that may omit certain islanding scenarios.Active methods need to inject disturbances into the grid todetect the event that generates power quality issues in thesystem. On the other hand, intelligent methods eliminate boththe need to use a threshold value and inject disturbances intothe system while also presenting low detection times. In thissection, we will focus on presenting some of the machine anddeep learning techniques applied to islanding detection.

Islanding is the process through which an on-grid Dis-tributed Generation (DG) system is disconnected from the gridwhile the DG system continues to power the load. When un-planned due to line tripping or equipment errors, this scenariomay generate power quality problems due to voltage/frequencyinstability and even damage electrical instruments [49]. Forthis reason, detecting unplanned islanding conditions is criticalfor DG reliance. According to standards such as the IEEEStd. 1537-2018 [50] and IEC 62116:2014 [51], an unplannedislanding detection condition most be detected and stoppedwithin 2 seconds since the formation of the event. As describedin [49], there are five types of islanding detection techniques,

which can be divided into passive, active, hybrid, remote,signal processing methods, and intelligent methods. Passivemethods tend to fail due to the need to set up a threshold valuethat may omit certain islanding scenarios. Active methods needto inject disturbances into the grid to detect the event thatgenerates power quality issues in the system. On the otherhand, intelligent methods eliminate both the need to use athreshold value and inject disturbances into the system whilealso presenting low detection times. In this section, we willfocus on presenting some of the machine and deep learningtechniques applied to islanding detection.

In [52], an islanding detection technique based on tunableQ-factor wavelet transform, and an ANN is proposed. Thistechnique was done by simulating in Matlab different scenariosconsidering all positive switching transients, islanding events,and faults from the grid side. By using the wavelet transform,features such as the range and the log energy entropy werecomputed. Then, using a Kruskal–Wallis test, the best featureswere selected and used to train the ANN. Two differentscenarios were considered in the classification: different loadsthat matched the DG power production and different loads,whose demand was greater or lesser than the DG powerproduction. The proposed algorithm achieved an accuracy of98%. The algorithm was also tested under noisy conditionsachieving an accuracy of 88.5% in the noisiest scenario.A similar approach was conducted in [53]. The researchersextracted features from a wavelet transform applied to thenegative sequence voltage measurement at the PCC of a DGsystem with a PV and a wind power plant. These features,along with the total harmonic distortion, were used to train anANN. The researchers obtained an accuracy of 95%.

The main difficulty for this model was making voltageswell predictions that were included in the train and testingscenarios. In [54], a Ridgelet Probabilistic NN (ridge functionsare used as activation functions) was used for detecting theevents. First, the Slantlet Transform [55] was applied to thevoltage, the frequency, rate of change of frequency, rate ofchange of voltage, dc-link voltage, and the d-q axis voltageto obtain features from the signal. This data set generatedincluded two islanding scenarios and four none islandingscenarios. Then, the best of these variables were selectedto train the NN. A modified differential evolution algorithmwith a new mutation phase, crossover process, and selectionmechanism was applied for training the NN. The proposedmethod was compared with other classifiers and achievedbetter performance.

Manikonda et al. have published several articles regardingislanding detection in the past years. The use of CNN canbe found in two papers created by them in [56], and [57]. Inboth, the researchers collected a data set based on a simulated100KW grid-connected PV system in Matlab/Simulink. Thevoltage phase signals from the PCC were measured, concate-nated, and then used to generate images using the wavelettransformation method. Different scenarios, including whenthe power mismatch between the DG source generation andwhen the load connected to the PCC is near zero, were added

to the data set to increase the difficulty for the proposed modelto classify the signals. In addition, several cases of suddenswitching of inductance and capacitor loads were also includedin the data set.

Furthermore, in [56], the authors used a transfer learningapproach with the AlexNet [58] to classify the islanding andnon-islanding events with an SVM used for the classificationlayer. On the other hand, in [57], the authors used a custom-designed CNN. The results show a 98.78% accuracy in bothpapers presented. Noise was also added to the data, andthe models achieved the same performance showing that theproposed models were resilient to noise.

SVM methods have also been playing a major role inislanding event predictions. In [59] was proposed an SVMalgorithm that was able to distinguish between islanding andgrid fault events. The work presented in [59] differentiatesfrom others because of several real-life data from a custom-made intelligent electronic device from a micro-grid alongwith data from simulations were used. Seven Gaussian SVMclassifiers were used with a kernel scale of 8 dimensions foreach one. The features used for training and testing includedfrequency, active and reactive power of loads at PCC, theRMS, and total harmonic distortion values of voltage andcurrent. The results obtained by the researchers show highprecision, effectiveness, and selectivity from the proposedalgorithm. The algorithm achieved an accuracy of 100%.In [60], an SVM with a Radial Basis Function Kernel wasproposed for islanding detection. The training and testing werecarried out with a data set with different faults, capacitorswitching, and load switching scenarios. The accuracy of thevalidations was 100%, but it is worth noting that very few datawere used for validation.

An SVM method was also proposed in [61]. A similarapproach to previous papers was carried out, like concate-nating the PCC voltage measurements and using the wavelettransform for converting them to images. Data such as therate of change of PCC voltages and negative voltages werealso used for generating the images. To extract features fromthe images, a histogram of oriented gradient features [62] wasapplied. The data set was composed of 1720 images, where860 of them were from islanding scenarios, and 860 weredifferent grid-connected events.

Furthermore, early islanding detection was also incorporatedinto the model. In the carried out tests, the SVM algorithmachieved 100% accuracy predicting the classes with a detectiontime of 218ms. It is important to clear out that the classifierswere tested with the 5-fold cross-validations technique.

Tree-based classifiers have also been used to detect island-ing scenarios as proposed in [63]. The researchers used across-validation technique and also pruning to make the logicalclassification more precise. The results of [63] show that themodel achieved an accuracy of 97.6%, detecting the faultwithin 0.2s.

V. MACHINE AND DEEP LEARNING FOR MANAGEMENTWITH PV

Energy management systems (EMS) have become increas-ingly helpful for consumers to reduce their electricity bills andmaintain efficiency. This improvement is possible due to theinsertion of smart grid technologies such as smart meters anddemand in response algorithms. The main objective of an EMSis to schedule in real-time the system’s energy flows by mini-mizing a defined objective function while maintaining reliable,secure, and safe operations of the system [64]. This objectivefunction is defined as to achieve a desired demand/responsewith load modifications incentives, or it can be price basedwhere it is determined through the prices of electricity atdifferent times. Various studies have been conducted on theEMS optimization formulation. In this section, we will focuson some of the MDL approaches proposed to achieve this goal.

One of the most used ML techniques for solving thisproblem is RL. In [65], the researchers used RL to managethe optimal management for residential homes that use arooftop solar photovoltaic system, an energy storage system(ESS), and smart home appliances. The researchers used aQ-learning table method to control the energy consumptionby controlling appliances consumption (air conditioner orwashing machine) and energy storage system charging anddischarging events. In [65] is also included a prediction ofthe indoor temperature using an ANN to assist the Q-learningalgorithm. The algorithm focuses on reducing the electricitybill using solar PV systems and the energy storage systemwhile also reducing an established dissatisfaction cost. Thestate spaces for the proposed problem were based on theenergy consumption of both the washing machine and theair conditioners, and the state of energy of ESS batteries attime t. The action spaces were: 1) turning on/off the washingmachine; 2) the ten different levels in which the system canset the AC energy consumption of the air conditioner; 3) theESS nine levels that allow the system to discharge and chargethe batteries. The reward evaluates the agents performancein terms of cost, the consumers satisfaction with the currentoperation, the electric cost, and energy under-utilization. Thepreviously mentioned ANN allows the system to accuratelycalculate the dissatisfaction of the consumer. The algorithmwas able to minimize the electricity bill through the energyconsumption scheduling while also maintaining the consumercomfort level and appliance operation characteristics.

Multi-agent-based RL techniques with Q-tables have alsobeen used to address this problem as shown in [66]. In thisarticle, the researchers used a multi-agent RL with ExtremeLearning Machine (ELM) for implementing a home EMS.The two objectives of the agent were to reduce the energyelectricity bill and the demand response induced dissatisfac-tion cost. The structure of the EMS included a refrigerator,an alarm system, air conditioner system, heating, lighting,washing machine, dishwasher, and an electric vehicle. Thestate-space corresponded to both the electricity price and thePV output in an hour ahead slot through a period of time.

The action space corresponded to the energy consumptionscheduling of each home appliance and charging/dischargingof the electric vehicle. The actions on an appliance dependedon its type, which could be non-shift-able, power-shift-able, ortime-shift-able. Lastly, the researchers represented the rewardas the utility cost of each agent and used an epsilon greedyexploration strategy.

Furthermore, the authors used ELM for predicting the futuretrends of the electricity price and PV generations. Then, thisdata was used as part of the state space. The algorithm wasable to reduce the electricity cost by 45% when compared toscenarios where the algorithm was not being used. As futureresearch, the authors proposed including energy storage for thesolar PV system and a more effective uncertainty predictionmodel. A DRL agent was proposed in [67], where the manage-ment problem considered efficiently operating storage devicesin a microgrid featuring panels with short and long-term stor-age capacities. The RL technique used was the DQL algorithm.The state-space was composed of the amount of energy in thebatteries and the amount of energy in a hydrogen tank. Theaction space consisted of the amount of energy transferredin or out from the storage system. The reward function wascomposed of the instantaneous operational revenues, whichconsidered the electric power generated by the PV system.The results gathered showed that the NN representation of thevalue generalized the policy in an efficient manner.

A comparison between several methods described earlierwas made in [68]. Several EMS models were created usingML techniques and compared using six different simulationscenarios. The algorithms to which the model was comparedincluded the Mamdani-type FIS, adaptive neuro-fuzzy inter-face system, support vector regression, echo state network, andmulti-layer perceptron models. The models were compared aswell with solutions obtained through Rolling Time HorizonStrategies. Also, an approach for representing the potentialmicrogrid energy flows as a single point in a planar domainis proposed to assess the microgrid operational configuration.A mathematical formulation of the microgrid model is highlydescribed with the formulation of the objective function. Theresults obtained in [68] showed that the rolling time horizonstrategy led to the best performance followed by the neuro-fuzzy interface system, which in one scenario outperformedthe former strategy.

Although the last presented papers achieved remarkableresults, the lack of standardization in energy managementsystems makes it difficult to compare the different algorithmsused. This is why some papers have been using open environ-ments, which allow researchers to implement, share, replicate,and compare their designed models. An example of this is seenin [69], where a centralized Soft Actor-Critic (SAC) algorithmwas implemented to flatten and smooth the aggregated curveof the electrical demand of a district composed of variousbuildings using the CityLearn environment [70]. The multi-objective cost function used consisted of the peak electricitydemand, the average daily electricity peak demand, ramping,the load factor, and the net electricity consumption of the

district over the evaluation period. Only 9 of the 27 obser-vations that CityLearn provides were used to train and testthe algorithm. This state-space included the hour, the month,the day, direct solar radiation, the non-shift-able load, solargeneration, cooling storage SOC, domestic hot water stockSOC, and the temperature outside of the buildings. The rewardfunction was designed for penalizing peak consumption basedon the total electricity consumption. Moreover, the rewardconsidered the charging and discharging of batteries during theday and the night. The algorithm achieved an average scoreof 0.967 over the multi-objective cost function on the testingdata set. It is worth noting that the testing data included theuse of multiple buildings and different climate zones.

VI. MACHINE AND DEEP LEARNING FOR FAULTDETECTION AND PV DIAGNOSTICS

One of the main challenges that the PV industry facesis its vulnerability to faults due to its exposure to harshenvironmental conditions [71]. Fault diagnosis is essential foroptimizing energy conversion efficiency and improving the lifespan of the PV system. Faults can be discriminated based ontime characteristics, including the incipient, the abrupt, and theintermittent. Incipient faults are low in magnitude and last onlyfor a few microseconds, making them very difficult to detect[72]. Abrupt faults refer to faults that occur instantaneously,often attributed to the results of line-to-line faults or line-to-ground short circuits. Intermittent faults refer to faults thatare cleared over time, such as partial shading [73]. Line toline faults occurs when an unexpected short circuit occursbetween two lines in a PV array [74], whereas the line toground when the short circuit occurs between a point of thearray and ground. These are types of short circuit faults inwhich abnormal connections between two points of differentpotentials occur [75]. Hot spot faults are caused by partial orcomplete shading of a PV module due to dust, which mayincrease the localized temperature in a cell and damage themodule [76]. OC fault occurs when in a PV array, an accidentaldisconnection occurs [77]. Finally, arc faults occur becauseof a loose connection or a cable insulation failure [78]. Inrecent years, MDL techniques have been highly studied forclassifying types of faults according to their fault type and, insome cases, identifying their specific location. In this section,we will study some of these papers.

In [79], three LSTMs with a softmax regression techniqueare proposed for fault classification and detection. The faultsconsidered by the authors only included line-to-line faultsand hot spot faults. The data was acquired using a simulatedmodel of 5.3kW PV array in Simulink. The features usedfor training the model included the PV array output current,voltage, and power. To some of the data collected, noisewas added to simulate real-world scenarios. The data wasalso standardized between 0-1 before training and testing themodel. The classification accuracy for the data was 100% forscenarios with no faults, 100% for scenarios with hot spotfaults, and 99.03% for the line-to-line faults. When adding

the noise, the accuracy of the models was reduced to 99.23%,98.78%, and 97.66%, respectively.

An RF classifier was used in [80] for the detection anddiagnosis of PV array early faults. The data set generatedincluded line-line faults, degradation, open circuit, and partialshading. The features considered for training the model werethe operating voltage and string of current of the PV array.The bootstrap sampling method was used to obtain subsets ofthe training data used for the random forest, which was grownusing the CART algorithm without pruning [81]. To gather thedata, experiments both on Simulink and laboratory PV systemswere conducted. For the results, 10-fold cross-validation andrandom validation were used. The results showed that themodel’s performance was better than other methods withwhich it was tested. Furthermore, the proposed method wasthen implemented in a system prototype for a laboratory PVsystem. The way in which the experiment is done is highlydescribed in [81]. The results showed over 99% accuracy inthe classification and detection of faults in both the simulatedand experimental results.

Although many machine learning algorithms have beenproposed, a comparison between them is helpful for selectingthe best models. This is why in [82], a comparison betweendecision trees, XGBoost, RF, and NN was conducted. Sixdifferent faults that include partial shading, bypass diode fault,bridging fault, temperature fault, complete shading fault, andshort circuit faults were included in the data set with a totalof 1200 readings. The features used for training and testingthe algorithms were the open-circuit voltage, the short circuitcurrent, the maximum peak point voltage, the short circuitcurrent, the maximum peak point current, the temperature, theirradiance, the fill factor, and the maximum power output.The results presented from this work showed that the NNachieved the highest validation performance with an accuracyof 99.91%. A similar approach is presented in [73], wherethe focus of the article was to develop a technique for faultdetection and diagnosis by comparing different models, andalso by using a Principal Component Analysis (PCA) [83] forextracting and selecting the most relevant multivariate features.The models needed to differentiate among five different typesof faults. The models compared included K-nearest-neighbors(KNN), discriminant analysis, random forest, SVM, decisiontree classifier, and Naive Bayes classifier. The classifier thatobtained the most accuracy in the testing set was the RF with99.87% accuracy, followed by the KNN with 99.43% accuracy.For future work, the authors suggested using a nonlinear PCAmethod.

CNNs have also been used to approach this problem. In[84], an approach using a 2D CNN to extract features fromscalograms, generated from PV system data, was used toclassify system faults. Five different faulty scenarios wereused to train the model, including partial shading, line toline fault, open-circuit faults, arc faults, and faults duringpartial shading conditions. The data was collected with theincorporation of an MPPT algorithm in the simulated systemin Simulink. The features transformed to a scalogram were

the irradiance, the temperature, the short circuit current, open-circuit voltage, the PV current, the MPP and boost convertercurrent, voltage, and power. One of the main contributionsof [84] was the different values of irradiance considered forthe fault classification, implying changes in the MPPT modelto generate broader types of scenarios for the predictions.These data were transformed into a scalogram using a wavelettransform.

Moreover, the authors used a transfer learning approach withthe pre-trained AlexNet [58]. For the last layer of the model,the authors used two approaches; the first one included usinga softmax layer, and the other used an SVM and an RF toperform the classification step. The methods proposed in [58]were compared with the other four methods. These methodsincluded a normal SVM with random forest, an LSTM, a bi-directional LSTM, and a multi-resolution signal decompositionwith classification performed by an SVM. The results showedthat the SVM with Random Forest that did not use the CNNoutperformed the other models. However, when noise wasadded to the data set, the proposed algorithm with the pre-trained AlexNet with a softmax classifier in the last layeroutperformed the rest, showing to be more resilient to noiseand achieving an accuracy of 70.45%. A CNN was also usedin [85], the time series data of faults were analyzed and thenused as input fault features to the CNN. The data features usedfor training were the PV voltage and current transformed intoa 2-Dimension electrical time series graph and used as inputsto the designed CNN.

The scenarios used to design the data set were line-to-linefaults and open circuit faults. One of the main contributionsof [58] was the design and implementation of an experimentalplatform to verify the proposed CNN model. The researchersobtained 1400 graphs from the experimental platform thatwere divided into training and testing data sets. Differentsliding windows and graph sizes were tested to obtain thebest results. The proposed algorithm was then compared toclassifiers that used Inception-V3 [86] and a wavelet-transformusing SVM. Compared to the other methods, the proposedmethod achieved higher accuracy under different conditions.The authors achieved a maximum accuracy of 99.51% usingthe created model and a sliding window of 10 seconds.

Furthermore, in [87], another fault PV array identificationtechnique based on CNN was proposed. Instead of using a 2D-CNN as in previous papers, the authors used a 1D-CNN witha ResGRU and a fully connected module at the end. The inputfeatures used for the designed model were the I-V curve (thisis collected in real-life scenarios using inverters equipped withI-V curve scanning), the irradiance, and the temperature. Thefaulty scenarios considered for this research included short-circuit faults, partial shading faults, abnormal aging faults,and hybrid faults. The data set gathered for training andtesting the model was first gathered using the Matlab/Simulinksoftware. The PV array system was made up of 13 modules inseries. To verify the effectiveness of the proposed method, theauthors used the t-distributed stochastic neighbor embedding[88] to visualize the models distribution effect over the features

extracted. The results achieved in the testing data showed a100% accuracy. The overall accuracy of the model using thedata gathered through the experimental platform was 98.41%.The proposed model was also compared to a CNN model, aResGru model, and a CNN-GRU model, achieving the bestperformance among these algorithms. However, its executiontime for classifying the data was the second-longest.

In addition, the algorithm was compared to NN, ELM withkernel function, fuzzy C-mean clustering, and the stage-wiseadditive modeling using multi-class exponential loss functionbased on the classification and regression tree. The modelproposed by the authors outperformed every other method towhich it was compared. Finally, the proposed model showedresilience to noise when incorporated into the data set andexhibited a 95.23% accuracy when meteorological data wasnot added.

Abnormal operating conditions due to dirt on the surfaceof modules, cell breakage, delamination, or hot spots maycause PV modules to decrease their efficiency. This decreasein efficiency raises the need for diagnostics to PV modules tobe done by MDL techniques that could detect these abnormalconditions. In [89], the authors used CNNs to automaticallyclassify thermographic images. The aim of the model wasto distinguish between hot spot conditions from those ofdust. The data set was constructed using different thermalcamera technologies, resolution, and color scales with legacyPV systems from different characteristics through the Italianterritory. Several techniques were applied for pre-processingthe images, which included normalization, homogenizationof the number of pixels, grayscaling, thresholding, box blur,and Sobel-Feldman filters. Furthermore, data augmentationtechniques were also used for training the model. The resultsshowed that a maximum accuracy of 98% was achieved withan actuation time between milliseconds and about 2 minutes.A similar approach was performed in [90] where the authorsproposed a method based on semantic segmentation and theuse of CNNs for failure mode classification. The authors firstused semantic segmentation applying a U-net [91] to identifythe PV modules in an image, obtaining a mask of the image.The obtained mask and the original image were then usedas input for a classification network that used CNNs. Theclassification networks considered two classification scenarios:binary classification (failure mode and non-failure mode) andqua-ternary classification (included cracks, shadows, dust, andnon-failure mode). The binary classification and qua-ternaryclassification model achieved an accuracy of 90% and 74%,respectively.

VII. MACHINE AND DEEP LEARNING FOR IRRADIANCEFORECASTING AND PV OUTPUT POWER ESTIMATION

PV energy production is highly dependant on the weatherconditions such as solar irradiance and temperature. Therefore,production levels of this energy source fluctuate, making itdifficult for power companies to balance the production andconsumption of electricity when using PV systems. Hence,

several MDL algorithms have been implemented to forecastsolar irradiation and the output power from PV systems.

In [92], an RNN approach was used as a PV power short-term forecaster algorithm. This approach was made using on-site weather IoT data set and power data collected in real-time. The collected data was the solar irradiation, the moduletemperature, the ambient temperature, the wind speed, and thehumidity. Then, a Pearson Correlation Coefficient analysis wasused to select the data that would be used for training andtesting. The set of experiments for defining hyperparameters,training, and testing the algorithm were divided into veryshort forecasting, which consisted of 5-min and 15-min aheadpredictions, and short-term forecast experiments, which wereconducted considering 1h and 3h ahead forecast. Experimentswere conducted using different sets of time steps to definethe length of the used window. The time interval for thesetime steps was 5min for the very short predictions and30min for the short predictions. The experiments showed thatthe prediction accuracy was best when using three hiddenRNN layers and 12 time-steps for both very short and shortpredictions. The experimental results showed that the pro-posed model achieved higher prediction accuracy comparedwith Autoregressive integrated moving average and SVR-RFmodels for the short-term forecast. The RNN model achieved99.1% (nMAE) and 98.6% (nMAE) regarding 5 min and 15min ahead forecasting, respectively. Furthermore, the modelachieved 97.4% (nMAE) and 96.2% (nMAE) for the 1 h aheadand 3 h ahead forecasting, respectively. For future work, theresearchers will use features such as cloud image and dustsensors with abnormality detection methods, considering itmay be helpful for floating or marine PV forecasting. AnLSTM model approach for solving this task was conducted in[93], using synthetic weather forecast and integrating statisticalknowledge of historical solar irradiance. A KNN algorithmwas used for classifying the historical irradiance into differenttypes of sky groups.

The results of the conducted research indicated that thesynthetic radiance forecast achieved a 33% improvement inaccuracy compared to an hourly categorical approach and thatthe LSTM-NN model outperformed models such as RNN,GRNN, and ELM. Another approach using LSTMs is pre-sented in [94], where a short-term solar generation forecastingmethod based on LSTM with temporal attention mechanismis proposed. The attention mechanism implemented in [94]allowed the model to focus on a particular feature whileneglecting others at a particular time. They used the data setof site number 31 TDG from the Desert Knowledge Australiacenter [95] to forecast the solar generation. The method wasthen compared with normal LSTM, ANN, and Persistencemethods, outperforming the rest. A similar approach was donein [96]. One of the main goals of this article was to developa highly accurate solar radiation forecasting model usingdata that captured the relationship between meteorologicalstation measurements and satellite data. The authors testedthe Encoder-Decoder LSTM and GRU networks for daily andweekly time horizons predictions. The LSTMs outperformed

the GRU networks both in daily and weekly horizons. Thebest MSE metrics achieved by the LSTM model were 0.1113for daily forecasting and 0.1299 for weekly forecasting.

In [97], the objective was to forecast the solar PV outputusing CNNs. The approach included using regular CNN,multi-headed CNN, and a CNN-LSTM for a short-term andmedium-term forecast. The models were then compared tomultiple linear regression models and an auto-regressive mov-ing average. The features used for prediction included theirradiation, the wind speed, the ambient temperature, andthe PV module temperature measured in 15-minute windows.These features were chosen according to the IEA report in”Photovoltaic and solar Forecasting” [98]. The results showedthat the simple CNN and the CNN-LSTM methods performedbest for the 1h (around 0.06 MAE in summer), 1-day (around0.034 MAE in summer), and 1 week (around 0.030 MAEin summer) ahead predictions made. A similar approach wasconducted in [99], where a five-layer CNN-LSTM model wasused for PV power forecast using data from Temixo [100],[101]. The forecasting horizons ranged from 10 minutes to180 minutes. The method’s performance was then comparedwith a single LSTM model, a CNN-LSTM hybrid model withtwo layers, a Lasso regression, and a Ridge regression. Theresults showed that the hybrid model outperformed the rest ofthe algorithms.

VIII. MACHINE AND DEEP LEARNING IN SIZINGMETHODS

ML techniques have been used for defining the optimalnumber of panels, storage capacity of batteries, the tilt, andazimuth angles required in PV systems. Moreover, severalstrategies have been developed to estimate the installationsmade by residential customers to size PV systems. Further-more, the constant growth in PV panel installations by resi-dential customers can cause grid instabilities, reverse powerflows, and infrastructure damage.

In [52], a NN approach was proposed for estimating the PVsize, tilt, and azimuth, using behind-the-meter data. The dataset used originated from Pecan Street data set [102], whichconsists of more than 1300 customer loads over one year. PVgeneration was generated using the System Advisor Modelsimulator [103] and the PCLib toolbox. The input valueswere the minimum yearly day and night net load values. Themodels used for predicting PV size were tested under differentgenerated scenarios created by the researchers, including PVestimations of 1000 test customers, estimation with varyingtilt and azimuth considering errors in the data set, different netload data resolution, and mislabeled data added on purpose.The results showed that the proposed NN could estimate thesize of the installed PV system with a MAPE of 2.09%. For theestimation of the tilt and azimuth, data was constructed usingthe irradiance gathered in NREL MIDC [104]. Values such asthe direct normal irradiance and the diffuse sky radiation werecalculated and used to perform simulations. Results showed animprovement in accuracy for the NN approach compared tothe linear regression model.

A generalized RNN was used in [105], where the mainobjective was to estimate the PV array and battery sizing ratio.This sizing was done by using the loss of load probabilityindex, latitude, and longitude. The paper aimed to predictthe sizing curves without running any iterative simulation andabandoning the need to calculate model coefficients. Using asimulation-based on hourly solar radiation and load demand,the model was validated, achieving a MAPE of 0.6%.

A DL algorithm to map PV arrays in high-resolution over-head imagery by identifying individual PV arrays, their size,and the power generation capacities over large geographicalareas was proposed in [106]. It was created using CNNsfor semantic segmentation, which provides pixel-wise labelsof an input image. It was called SolarMapper [107] and ispublicly available. It was trained on the Duke California SolarArray data set [108], which comprises over 400km2 imageryand has 16000 hand-labeled solar arrays. The metrics used toevaluate the performance were related to pixel-wise labelingaccuracy and object-wise detection accuracy. An estimate tocalculate the installed solar capacity of the panels is alsoproposed based on the identification realized by the classifier.The model was accurate with a precision of 0.76 in theobject-based performance metric. The technique used to assessthe pixel-wise performance was the intersection-over-union(IOU) metric, which was 0.67. To calculate the installed solarcapacity, the researchers first estimated the surface area ofthe PV array installed based on the segmentation carried out.Then, the parameters of a simple linear regression based onthe surface area to predict the PV array capacity installed wasused. The correlation coefficient achieved by the model was0.91, using color imagery to estimate the parameters for eacharray.

IX. MACHINE AND DEEP LEARNING IN SITE-ADAPTATION

Site-adaptation is the process in which satellite-based mea-surements are calibrated with in-situ measurements for a targetsite. The most common approaches to solve this problem arestatistical analyses that attempt to fit satellite estimates to in-situ data [109]. To the authors knowledge, only one article hasaddressed this problem using ML techniques. In [96], severalML regression techniques were tested to construct a modelthat could accurately capture the point-to-point relationshipbetween meteorological station measurements and satellitedata. The input variables used were the satellite-based data,while the output was the estimation of in-situ Global Hor-izontal Irradiance (GHI) measurements. The input variableswere the diffuse horizontal irradiance, the Global NormalIrradiance (GNI), the direct normal irradiance (DNI), the solarzenith angle, the temperature, the wind speed, and the time ofday. The in-situ database used was provided by the Colom-bian Institute of Hydrology, Meteorology and EnvironmentalStudies (IDEAM) [110]. To collect satellite data, the authorsused the National Solar Radiation Database (NSRDB) [111].The models tested included linear regressions, NN, RF, andAdaBoost. To compare the results of the different methods, thesite-adaptation method quantile mapping [112] was computed

by the authors. When comparing the RMSE, R2, and MAEmetrics of the models, it was clear that almost every ML modelsurpassed the commonly used quantile mapping technique(except for AdaBoost). The model with the best performancewas the random forest with 100 estimators, obtaining thehighest R2 (0.96), the lowest RMSE (66), and the lowest MAE(33).

X. MACHINE AND DEEP LEARNING RESOURCES FOR PVRESEARCH

In this section, a summary of some of the resources usedin the papers reviewed is presented.

Besides using Matlab for testing MPPT strategies, an Ope-nAI gym environment was created for [32], [33] and isavailable in [113]. The state spaces are the current valuesof voltage, power, and the difference between the actual andprevious power. The action space consists of disturbancesapplied to the output voltage.

For the simulation of management systems, there are twomain sources from which a simulation of an environmentcan be found to easily compare designed models. One isCityLearn, created with the OpenAI Gym environment thatallows the implementation of centralized or multi-agent controlfor building energy coordination and demand response in cities[70], [114]. Another useful environment would be pymgrid[115], which is an open-source package to generate andsimulate a large number of micro-grids, allowing users to focuson applying control algorithms.

Regarding some of the data-sets used in several of the papersreviewed, a summary is presented in Table ??.

XI. CONCLUSIONS

In this paper, a thorough review of the recent advancesin MDL regarding PV systems is presented. The papersconsidered cover the period from 2014 to 2021. Furthermore,case studies regarding the different fields covered were made,allowing new researchers to rapidly catch up with the mostsignificant advances in the subject they wish to cover. MLis highly influential over PV and will continue to be with theincorporation of new techniques. Some overall conclusions arenow presented:

• RL seems to be the most prominent technique overPV control and management systems. However, morephysical tests should be carried out to further ratify itsusefulness in MPPT control while also making morecomparisons against fuzzy logic methods. Moreover,RL techniques for MPPT and voltage/frequency controlshould be tested together.

• In forecasting methods, new techniques such as trans-formers that are based solely on attention mechanism[116] should be tested and compared more with thealgorithms mentioned.

• Since the applications of ML techniques towards site-adaptation are recently being developed, more pre-processing techniques should be tested and compared toachieve better results. This pre-processing could be done

by performing, for example, PCA over the data set usedfor correcting the satellite data.

• For islanding detection, more physical trials should becarried out to test the new models proposed.

• For fault detection and classification, data sets that in-volve different MPPT control algorithms should be madeand classified with the methods proposed. This method-ology could be done using an RL algorithm for MPPTwhile also collecting the data as proposed in [84].

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