ann training

8/6/2019 ANN Training

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ELECTRICAL LOAD FORECASTING USING

AN ARTIFICIAL NEURAL NETWORK

A theoretical approach..


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Introduction:

1. Electrical load forecasting predicts the future load on a

particular generation plant so that spinning reserve

allocation can be done.

2. Load forecasting with lead times, from few minutes to

several days helps to allocate the spinning reserveallocation.

3. Load forecasting is complex and exhibits several levels of

seasonality.4. The load at the given hour not only dependent on the

previous hour, but also on the load at the same hour on the

previous day.


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Continued..

5. Load on a particular plant can be found by using manytechniques like Dynamic programming model, regression

approach, time series approach, artificial neural network

method and etc6. Load forecasting is useful for system security .

7. Load forecasting gives information about the energy

exchange with energy utilities.8. Load forecasting provides vulnerable information to detect

many vulnerable situations in advance .


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Artificial Neural Network(ANN)Artificial Neural Network(ANN)

for Electrical load forecastingfor Electrical load forecasting

y Accurate load forecasts are required for

power tradersy Availability of large amount of historical

data helps in training the ANN.

y

It interpolate among the load data andtemperature data in a training data set.


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How different approaches finds theHow different approaches finds the

future load?future load?y Time series

approach

1.Load pattern as

time seriessignal andpredicts thefuture load byusing timeseries analysis.

y Regressionapproach

1. Future load is

predicted byinserting thepredictedweathervariables intothepredeterminedfunctionalrelationship.

ANN

1.It interpolate

among the

load and

temperature

data in a

training setand predicts

the future

load.


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Time series approachTime series approach

1. It is found out that there is a strong correlationbetween the load and weather variables such as thistemperature, humidity wind speed and cloud coverhence approach fails to predict the load forecast.

2. It results in accuracy of prediction and numericalinstability.

3. The reason behind the inaccurate prediction is that itdoesnt utilize the weather information.

4. Time series approach uses matrix-oriented adaptivealgorithms which, in many unstable.


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Different time series approaches.

1. Kalman filter approach failed because of the possible high

nonstationarity of the load pattern may not allowed an accurate

estimate to be made.

2. Box-Jenkins method fails because of its low performance.

3. The spectrum expansion technique utilizes the Fourier series . Abrupt

changes in the weather causes fast variations in the load pattern which

results in high frequency components in frequency domain. Hence it

does not provide accurate forecasting for the case of fast weather

change.


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Regression approachRegression approach

1. Finds the functional relationship between the weather

variables system load.

2. It uses the linear or piecewise-linear representations for

the forecasting functions.

3. Functional relationship between the load and variables ,

however is not stationary ,but depends on spatio-temporal

elements.

4. It does not provide the accurate result but produce the

averaged result.


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ANN approachANN approach1. ANN approach traces the previous load patterns and

predicts i.e. extrapolates a load pattern using recent loaddata.

2. ANN can able to perform the non-linear modeling and

adaption.

3. It does not require assumption of any functionalrelationship between load and weather variables inadvance.

4. ANN is also currently being investigated as a tool inother power system problems such as securityassessment , harmonic load identification, alarm

processing , fault diagnosis and topological observability


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A layered ANN.A layered ANN.

Architecture:1. Widely used multi layered perceptron layer consists of one input layer,

hidden layer and output layer.

2. Each layer consists of many neurons and each neuron in the adjacent

layer is connected to the neurons in the adjacent layer with different

weights .

3. Except input layer , each neuron receives the signals from the neurons

from the previous layer linearly weighted by interconnect values

between neurons.


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Continued..

4. Let us assume Q set of data are assumed to be available . Inputs of

{i1, i2, i3..iQ} are imposed on the top layer.

5. The ANN is trained to respond to the corresponding target

vectors,{t1, t2, t3tQ} on the bottom layer .

6. Training continues until a certain stop criterion is satisfied.

7. Training is halted when threshold of error is below the

predetermined value.

8. The training time required is dictated by various elements including

the complexity of the problem, the number of data, the structure

of network, and the training parameters used.


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ANN TrainingANN Training1. Generalized Delta Rule is described for training the multilayered

perceptron type ANN.2. Output vector is produced by presenting an input pattern to the

network.3. Difference between the produced and target outputs , the networks

weights {Wij} are adjusted to reduce the output error.

4. The error at the output layer propagates backward to the hidden layeruntil it reaches the input layer.

5. The output from neuron i, Oi , is connected to the input neuron throughj through the interconnection weight {Wij}.

6. Unless neuron k is one of the input neurons k is one of the inputneurons, the state of the neuron k is given by,

Ok=f(i Wik Oi)Where f(x)=1/(1+ex ), and the sum is over all neurons in the adjacentlayer. Thus error at the output neuron is can be given by

E=(1/2)*(tk-ok)2

Where neuron k is the output neuron.


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Continued..7. The Gradient Descending algorithm adapts the weights according to the

gradient error i.e.Wij (E/Wij )= - (E/Oi )( Oi / Wij )

8. We define error signal asj = - (E/Oi)

With some manipulation , we get GDR asWij = j Oiwhere is an adaptation gain .j is computed based on whether or not j is in the output layer .

9. If neuron j is one of the output neuronsj = (t- Oj ) Oj (1- Oj )

10. If neuron j is not in the output layer,j = Oj (1- Oj ) k k Wik

11. Momentum term is introduced to improve the convergencecharacteristicsWij (n+1)= j Oi+ Wij (n)

Where n represents the iteration index .


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Test cases and resultsTest cases and results

The ANN was trained to recognize the fallowing cases:

1. Case 1: Peak load of the load

2. Case 2 : Total load of the day

3. case 3 : Hourly load

where

peak load at day d = max{L(1,d),.,l(24,d)}

Total load at day d = h(L,d), h=1to 24

L(h,d)=is the load at hour h on day d.

Error=[ actual load-forecasted load]/[actual load]*100


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Sets Test data from

Set 1 01/23/89 -01/30/88

Set2 11/09/88 11/17/88Set 3 11/18/88 - 11/29/88

Set 4 12/08/88 12/15/88

Set 5 12/27/88 01/04/89

Test data sets

Hourly temperature and load data for seattle/tacomaarea in the interval of nov. 1. 1988- jan. 30, 1989were collected by the puget sound power and lightcompany . This data is used to train ANN and test

its performance.


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Case 1:For peak load forecasting the topology is as fallowsInput neuron : T1(k) , T2(k), and T3(k)Hidden neurons:5 hidden neurons

Output neuron: L(k)WhereK= day of the predicted loadL(k)=peak load at day k,T1(k)= average temperature at day kT2(k)=peak temperature at day kT3(k)=lowest temperature at day k

Table 2 shows the error(%) of each day in test sets .The avg error for all sets is

2.04%


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Case 2:For total load forecasting the topology is fallowsInput neurons : T1(k), t2(k) and T3(k)Hidden neurons :5 hidden neuronsOutput neurons: L(k)Wherek= day of the predicted load,L(k)= total load at day k,T1(k)= average temprature at day k,T2(k)= peak temprature at day k,T3(k)= lowest temperature at day k.

Table 3 show the error(%) of each day in test sets .The avg error for allsets is 1.68%


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Case 3:Topology of the ANN for the hourly load forecasting with one hour oflead time as fallows

Input neurons: k,L(k-2), L(k-1), T(k-2) , T(k-1) and T(k)Hidden neurons:10 hidden neuronsOutput neurons: L(k)K= output of predicted load,L(x)=load at hour x,T(x)=temperature at hour x,T^(x)=predicted temp , for hour x


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Advantages of ANN applied toAdvantages of ANN applied to

power systempower system

1. Supervised and unsupervised learning helps in prediction of short

term and long term load forecast with impressive accuracy.

2. ANN doesnt need additional memory for storing the history of

load patterns .

3. NN are robust in nature for any changes in weather conditions.

4. If input data are incomplete or have some noise, the ANN still give

good results

5. It has adaptivity characteristics and adjust to new values easily.

6. Trained ANNs can be used as robust controllers.


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ANN applications to power systemANN applications to power system

1. Load forecasting .

2. Security assessment.

3. Fault detection/Diagnosis

4. Economic load dispatch

5. Hydro electric generation scheduling

6. Power system stabilizer design.

7. Load flow

8. Voltage and reactive power control.


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Conclusion:1. ANN interpolates among the load and temperature data

of training sets to provide the future load from thetrained ANN.

2. In order to forecast the future load from trained ANN,weneed to use the recent load and temperature data inaddition to the predicted future temperature .

3. Compared to other regression models ANN allows moreflexible relationships between temperature and loadpattern .

4. Since the neural network simply interpolates among thetraining data , it will give high error with the test datathat is not close enough to any one of the training data.

5. With the use of other weather variables like cloud cover,humidity etc can improve the prediction of future load .


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ReferencesReferences

1. D.C.Park, M. A. El-sharkwi , R.j.marks ii , L. E. Atlas and M. S.Damborg, Electrical load forecasting using an artificial neuralnetwork, july , 1990.

2. Prof D. P. Kothari Application of neural network to power system.

3. A. J. Al-Shareef, E.A . Mohamed, and E. Al-judibi, one hour ahead loadforecasting using artificial neural network for the western area ofSaudiarabia, World academy of science , Engineering and technology 372008.

4. R. Nayak , J.D. Sharma , A hybrid neural network and simulatedannealing approach to the unit commitment problem.

5. James . W. Taylor and Roberto Buzzia , Neural Network loadforecasting with weather ensemble predictions, IEEE trans , on powersystem , 2002 vol. 17 ,pp 626-632


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