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P.Uday Kiran M.Ajay Kumar M.Suresh
By
4/4 B.Tech(EEE)
IMPLEMENTATION OF SHORT-TERM LOAD
FORECASTING USING NEURAL NETWORKS AND ANFIS
Predicting the load
It is important for maintaining the Power Plant
Forecaster ascertains the estimated load for required hour
What is Load Forecasting?
Importance of Load Forecasting
• Load Forecasting has always been the essential part of an efficient Power System planning and operation
• Several Electric Power companies are now forecasting load power based on conventional methods
• However, since the relationship between load power and factors influencing the load power is non-linear, it is difficult to identify its non-linearity by using conventional methods
Short-Term Load Forecasting Load prediction period may be a week or
shorter period than a week
Medium –Term Load ForecastingLoad prediction period may be few months
Long-Term Load Forecasting Load prediction period may be more than a
year
Types of Load Forecasting
Factors affecting the load Forecasting
• Seasonal changes
• Daily changes
• Temperature
• Humidity
• Clouds
• Random event
• Any economical or environmental change
Need for Forecasting the load
• Planning of power generation
• Scheduling of fuel supplies and maintenance
• For minimizing the operation costs
• Important for supplier: With the forecasted load number of generations in operation can be controlled
Artificial Neural Network
• A Neural network is a massively parallel-distributed processor made up of simple processing units, know as neurons.
• It resembles brain in two aspects: 1.Knowledge is acquired by the network from its environment
through learning process. 2.Inter-neuron connection strengths, known as synaptic
weights, are used to store the acquired knowledge.
• The procedure used to set the connection strengths is called learning
Basic Elements In Neural Network Structure
• ANN performs fundamentally like a human brain. The cell body in the human neuron receives incoming impulses via dendrites.
• Neurons of ANN consists of 3 main components; weights connecting the nodes, the summation function within the node, transfer function.
COMPONENTS OFNEURON
THE NEURON MODEL
• After the training process, ANN generalizes it. Generalization refers to the neural network producing reasonable outputs for inputs not encountered during training (learning).
• These two information-processing capabilities make it possible for neural networks to solve complex problems.
Model of Artificial neuron
Properties of neural networks
Non-linearity
Input-Output mapping
Adaptivity
Fault tolerance
These are the properties that are most desirable for Solving the problems at hand.
Taxonomy
Neural networks
Feed-forward networks
Feed back networks
Feed-Forward Neural Network
Back Propagation Network
• Back Propagation is a systematic method for training multi-layer artificial neural networks using back propagation of errors rule.
• The aim of this network is to train the net to achieve a balance between the ability to respond correctly to the input patterns that are used for training and ability to provide good responses that are similar
Back Propagation neural network
• The BP network consists of one input layer, one or more hidden layers, one output layer.
• The learning process includes two courses, one is the input information transmitting in the forward direction and another is the error transmitting in the backward direction.
Training Algorithm
The training algorithm of back propagation involves 4 stages:
Initialization of weights
Feed-forward
Back propagation of errors
Updation of weights and biases
APPLICATION OF ANN’S
• The application of ANN’ s to short-term load forecasting has gained a lot of attention recently.
• The availability of historical data is most important for ANN’ s to apply to this field.
MATLAB
An interactive system whose basic element is an array that does not require dimensioning
It allows
• Graphics
• Computation
• External interface
It has in built tool boxes, which can also be extended by user programming (SIMULINK)
NEURAL NETWORK TOOL BOX
STEPS INVOLVED
• Assemble the training data
• Create the network object
• Train the network
• Simulate the network response to new inputs
Preprocessing and post processing
• It’s useful to scale inputs & outputs such that they fall in specified range
• So, the NN output needs de-scaling to generate forecasted loads
LOAD FORECASTING USING ANN
• In this work NPDCL Warangal distribution system load data is considered for forecasting.
• Temperature data was taken from the NITW weather station.
• Feed Forward Back propagation 2-layered network structure with non-linear sigmoid function as transfer function is chosen.
Network Properties
• The two layers include hidden layer and output layer.
• The connection weights can be real numbers or integers. They are adjustable during the training, but some can be fixed deliberately. When training is completed, all of them should be fixed.
Load Forecasting with only loads as inputs
• Input Variables• Different sets of lagged loads have been proposed as
input features for the load prediction in the electricity market. Bearing in mind the daily and weekly periodicity and trend of the load signal, the set of
• {L(h-1), L(h-2), L(h-3), L(h-4), L(h-5), L(h-6), L(h-24), L(h-25), L(h-26), L(h-48), L(h-49), L(h-50), L(h-72), L(h-73), L(h-74), L(h-96), L(h-97), L(h-98), L(h-120), L(h-121), L(h-122), L(h-144), L(h-145), L(h-146), L(h-168), L(h-169), L(h-170), L(h-192), L(h-193), L(h-194), } total of 30 inputs are used at input layer .
Topology Of ANN
(D-8) (D-7) … (D-2) (D-1) (D)
L(h-192) L(h-193) L(h-194)
L(h-168)
L(h-169)
L(h-170)
……
L(h-48) L(h-49) L(h-50)
L(h-24) L(h-25) L(h-26)
L(h-1) L(h-2) L(h-3) L(h-4) L(h-5) L(h-6)
L(h)
Output at
Interval
‘ h’
Training:
1) The back propagation NN algorithm is used here for learning the neural network.
2) The implementation of Back Propagation involves a forward pass through the network to estimate the error, and then a backward pass modifying the synapses (weights) to decrease the error.
Simulation:
Using the trained neural network, the forecasting output is simulated using the test input patterns.
Simulation Results
• Number of input nodes =30;
• Number of hidden nodes=50;
• Number of output nodes=1;
• Number of training samples=10;
• Number of testing samples=5;
ACUTUAL LOAD
FORECASTED
LOAD
% ERROR
1256 1417.7 12.8742
1307 1390 6.3504
1153 1270.68 10.2064
992 910.73 8.1925
1028 920.87 10.42
Load Forecasting By Considering Temperature Effect
INPUT VARIABLES:
Hourly load data for the month January 2007 was collected from NPDCL (Northern Power Distribution Corporation Limited). Hourly temperature values were from the NITW weather station for the month January 2007.
We used this data to train the network and test its performance.
Topology Of ANN
(D-3) (D-2) (D-1) (D) Output L(h-72)
T(h-72)
L(h-73)
T(h-73)
L(h-74)
T(h-74)
L(h-48)
T(h-48)
L(h-49)
T(h-49)
L(h-50)
T(h-50)
L(h-24)
T(h-24)
L(h-25)
T(h-25)
L(h-26)
T(h-26)
h, T(h)
L(h-1),T(h-1)
L(h-2), T(h-2) L(h-3), T(h-3)
L(h-4), T(h-4) L(h-5), T(h-5) L(h-6), T(h-6)
L(h)
Load to be
forecasted at hour
‘h’.
Simulation Results:
• Number of input nodes=32;
• Number of hidden nodes=50;
• Number of output nodes=1;
• Number of training samples=10;
• Number of testing samples=5;
ACUTUAL LOAD
FORECASTED
LOAD
% ERROR
1028 1059.5 3.06
1200 1164.4 2.697
1185 1192.5 0.632
1159 1208 4.227
1174 1128.9 3.841
Adaptive neuro-fuzzy inference system
• In ANFIS MF parameters are chosen so as tailor them to a set of i/p-o/p data.
• First order sugeno fuzzy model with hybrid learning algorithm is used.
• It constructs a set of fuzzy if-then rules with appropriate membership functions to generate the stipulated input-output pairs.
• The parameters of MF’s and rules change through the learning process.
Fuzzy Inference System
1.Fuzzification interface: transforms input crisp values into fuzzy values
2.Knowledge base : A combination of rule base and data base.
(i) Rule base containing a number of fuzzy if- then rules
(ii) Data base defines the membership functions of fuzzy sets used in the fuzzy rules.
3.Decision-making logic: performs inference for fuzzy control actions.
4.Defuzzification interface: transforms fuzzy value to a crisp value.
Membership functions for seven linguistic variables
NB NM NS PS PM PB
Xmin
Xmax
ANFIS Architecture
• 1. If x is A1 and y is B1, then f1=p1x+q1y+r1• 2. If x is A2 and y is B2, then f2=p2x+q2y+r2
Layer 1: Every node i in this layer is an adaptive node with a node function.
O 1,i = µ A i (x) , for i=1,2 or
O 1,i = µ B i-2 (y) , for i=3,4,
Where x (or y) is the input to node i and Ai
(or Bi-2) is a linguistic label (“small” or “large”) associated with the node .
Here Gaussian membership function can be used.
Layer 2: Every node in this layer is a fixed node labeled Π, whose output is the product of all the incoming signals.
Layer 3: Here, the ith node calculates the ratio of the ith rule’s firing strength to the sum of all rule’s firing strength.
Layer 4: Every node i in this layer is an adaptive node with a node function.
Where wi is a normalized firing strength from layer 3 and {pi, qi, ri} is the parameter set of the node. These parameters are referred to as consequent parameters.
Layer 5: The single node in this layer is a fixed node labeled Σ, which computes the overall output as the summation of all incoming signals:
Load Forecasting Using ANFIS:
• INPUT VARIABLES: Hourly load data for the month January 2007 was collected from NPDCL (Northern Power Distribution Corporation Limited). Hourly temperature values were from the NITW weather station for the month January 2007.
• We used this data to train the ANFIS and test its performance. Our focus is on a normal weekday.
Topology Of ANFIS
Output
L(h-2)
T(h-2)
Load and temperature
at (h-2)
L(h-1)
T(h-1)
Load and temperature
at (h-1)
‘h’, T(h),
‘h’ is the hour of predicted
load
L(h)
Load to be forecasted at
hour ‘h’
ANFIS editor: anfisedit
Simulation Results:
• Number of input nodes=6;
• Number of Memebership functions for each input=3;
• Number of output nodes=1;
• Number of Membership function for output=1;
• Number of training samples=38;
• Number of testing samples=5;
Simulation Block Diagram
ACUTUAL LOAD
FORECASTED
LOAD
% ERROR
1028 1035.5 0.727
1200 1192.4 0.633
1185 1179.5 0.464
1159 1167 0.69
1174 1169.9 0.349
Conclusions
From the simulation results obtained we observed that the error in the load forecasting was decreased to a great extent when temperature effect was considered.
The error was further decreased when simulated by ANFIS.
References
• [1] G.Gross, F.D.Galiana, "Short-Term Load Forecasting," PrQceedings of IEEE, vo1.75, no.12, Dec. 1987, pp 1558-1 573.
• [2] S.Rahman, R.Bhatnagar, "An Expert System Based Algorithm for Short Term Load Forecast," IEEE Trans. on Power Systems, vo1.3, no.2, May 1988, pp. 392-399
• [3] D.C.Park, M.A.El-Sharkawi, R.J.Mark 11, "Electric Load Forecasting Using An Artificial Neural Network," IEEE Trans. on Power Systems, vo1.6, no.2, May1991, pp.442- 449
[4] K.Y.Lee, Y.T.Cha, J.H.Park, "Short - Term Load Forecasting Using An Artificial Neural Network," IEEE Trans. on Power Systems, vo1.7, no. 1, Feb. 1992, pp. 124-132[5] S.T.Chen, D.C.Yu, A.R.Moghaddamjo, "Weather Sensitive Short-Term Load Forecasting Using Nonfully Connected Artificial Neural Network," IEEE Trans. onPower Systems, vo1.7, no.3, Aug. 1992, pp.1098-1105[6] Y.T.Park, J.K.Park, "An Expert System for Short Term Load Forecasting by Fuzzy Decision," Proc. of 2nd Symp. on Expert System Application to Power Systems, July1989, Washington, USA, pp.244-250
THANK YOU
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