LOAD FORECASTING USING HYBRID CONTROLLERS

7/27/2019 LOAD FORECASTING USING HYBRID CONTROLLERS

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Submitted by: Under the Guidance of: Pradip Niranjan Mr. C. N. Singh M. Tech. 6 th Semester Associate professor Roll No.: 6004520004

S.R. No.: 809/10

DEPARTMENT OF ELECTRICAL ENGINEERING H.B.T.I. KANPUR

PRESENTATION

ONLOAD FORECASTING USING HYBRID CONTROLLERS


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Motivation of Study

Load forecasting is enable to take utility unit commitmentdecision.

The precise forecasting is the basis of electrical energy tradeand spot price establishment for the system to gain theminimum electricity purchasing cost.In the real-time dispatch operation, forecasting error causes

more purchasing electricity cost or breaking-contractpenalty cost to keep the electricity supply and consumptionbalance. Contd..


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Motivation of StudySTLF is Carried out based on historical data anddifferent mathematical techniques are used each

each may have its own accuracy at a particulartime point.It is necessary to use the combination of differentmethods and computational i.e hybrid intelligencealgorithms to result forecast with high accuracy.


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Objective

The objective of project work is to develop newpractical models with computational intelligencealgorithm which have high in load for cast

The research tries to bring forth the advantages of different computational intelligence methods anddevelop a comprehensive method of selection tofulfill the goal


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Artificial Neural Network(ANN) A neural network is a massively parallel distributed processor made up of simple processing units, which has a natural propensity for storingexperimental knowledge and making it available for use.

ANN acquire the information from environment through a learningprocess.Neural networks having remarkable ability to derive meaning fromcomplicated or imprecise data.

A neural network is a machine that is designed to model the way in which the brain perform a particular task. A neural network has remarkable ability to derive meaning fromcomplicated or imprecise data and extract patterns and detect trendsthat are too complex to be noticed by either humans or other computertechniques.


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MathematicalModel of ANN

X1,x2,xp are input fromenvironment to neurons ininput Layer .

Wk1,wk2,...wkp synaptic weights

Model of an ANN


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Network Architectures Single-layer Feed

forward NetworksMultilayer Feedforward Networks


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Learning Processlearning is process to modify the synaptic weights of

network in an orderly fashion to attain a desired designobjective.

Supervised learning

Learning is accomplished undersupervision As the inputs are applied to thenetwork, the network outputsare compared to the targets.

The learning rule is used toadjust the weights and biases of the network in order to move thenetwork outputs closer to thetargets.

Unsupervised learning

There are no target outputsavailableThe weights and biases aremodified in response to networkinputs only


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Introduction to load forecasting

Load forecasting phenomenon of estimating

measures in the coming future . Electrical load forecasting is concern with estimation

of maximum demand. Electrical load forecasting is carried out with historical

load data and whether conditions i.e temperature ,humidity and wind speed.


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Need of load forecasting Ensures secure operation of electrical utility grid. Play important role in generation scheduling ,unitcommitment Hydrothermal coordination. To improve reliability of A.C power line network and

optimal load scheduling .


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Electrical load forecasting

techniques Multiple regression Exponential smoothing

Iterative reweighted least-squares Adaptive load forecasting Stochastic time series Neural network Fuzzy logic


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Multiple regression Multiple regression analysis for load forecasting uses the technique of

weighted least-square s estimation. Based on this analysis, the statistical relationship between total

load and weather conditions as well as the day type influences can becalculated.The model used in multiple regression is given as Y(t)=v(t)a(t) +e(t) Where,t= Sampling time

Y(t)=Measured system load V(t)= Vector adaptive variables time temperature etc A(t)=Transposed vector of regression coefficiente(t)= Model error at time t .

The regression coefficient is computed by equally or exponentially leastsquare estimation


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Iterative reweighted least square The method uses an operator that controls one variable at a time. An optimal starting point is determined using the operator This method utilizes the autocorrelation function and the partial

autocorrelation function of the resulting differenced past loaddata in identifying a sub optimal model of the load dynamics.

The weighting function, the tuning constants and the weightedsum of the squared residuals form a three-way decision variable inidentifying an optimal model and the subsequent parameterestimates.

Consider the parameter estimation problem involving linearmeasurement equation Y=X +

Y(nx1 )= vector of observation : (px1)=matrix of un knownparameters

X(nxp)= matrix of known coefficient : (nx1)= Vector of randomerror


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Adaptive load forecasting In adaptive load forecasting the model parameter are

automatically corrected to keep track of the changingload conditions

Regression analysis based on Kalman filter theory isused.

The kalman filter normally uses the current predictionerror and the current weather data acquisition

programs to estimate the next state vector . The state vector is determined by analyzing the total

historical data not only recent measured load and weather data.


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Stochastic time series Using the time-series approach, a model is first

developed based on the previous data, then futureload is predicted based on this model.

Some of time series model is listed below-(i) auto regressive model(ii) Auto regressive moving average model

(iii) Auto regressive integrated moving average model


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ANNs controller ANNs needs selection of variable as network input. Develop the relationship between input variables and

predicted load based on training process. Back propagation learning algorithm is used to train ANNs forecasting time series.


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Selection of parameters for ANN

STLF requires the following parameter to beselected as input to ANN

WhetherTimeEconomy Random disturbanceClass of customers


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ANN Controller contd


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Fuzzy Controller

This approach is applied to forecasting where theadvantage of an operators expert knowledge is used.

The fuzzy decision system require detailed analysis of data and fuzzy rule base has to be developedheuristically for each session

The problem with fuzzy system is that the rules fixed

in fuzzy rule base may not always yield the bestforecast.


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Need of hybrid controller Reliance on large historical database with possible

obsolete and irrelevant data, assumptions about staticload shape and parameters.

Neural network is one approach which is able to deal with nonlinear and adaption.


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Hybrid Controller The Hybrid controller consist of artificial neural

networks (ANNs) and fuzzy network . The functionality of fuzzy system and learning

capabilities of neural network can be merged to yield aforecasting system more power then either of systemalone.

Hybrid System has fuzzy neural network(FNN) used inpre forecasting and fuzzy expert system used in finalforecaster .


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Fuzzy neural network (FNN) for

initial forecast Fuzzy neural network model is based on multilayer perception

using gradient descent based on back propagation algorithm by incorporating fuzzy sets at various stage .

Fuzzy sets are defined for load , temperature, humidity parameter.

The input to FNN is comprise the membership value tooverlapping partition of linguistic property of small, mediumand large corresponding to each input feature past loadtemperature , humidity ..etc

The Out put layer consist of the membership value to theoverlapping partition linguistic properties small medium andlarge corresponding to load forecasted.


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Fuzzy Neural network (FNN) FNN is based on the multilayer perception using

back propagation algorithm . The fuzzified input vector consist of the member ship

values of past load and weather parameters i.etemperature, humidity and wind speed.

The output vector is defined in terms of fuzzy class

membership values of the forecast load.


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Fuzzy neural network(FNN)

Input pattern: P(i-1)= average load on (i-1)thday

min(i-1)=minimum

temperature (i-1)th day max(i-1)= maximumtemperature (i-1)th day

min(i)= minimum temperatureof ith day

max(i)= maximum temperatureof ith day

Output pattern:

P(i) = average load on ith day


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Fuzzy expert system (FES) The input to fuzzy expert system consist of differences

in whether parameters between present and forecastedinstant .The output of the FES gives the load correction which when added to the initial forecast, yields the final forecast.Fuzzy network is used to calculate minimum ,maximummean and variance of the estimated load.


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Fuzzy expert system for final forecast

Neural fuzzy network(NFN) produces initial forecast with aset of initial load, temperature and humidity expressed interms of fuggy membership value.

Let initial forecasted load by (NFN) be P(i) The actual load be A(i) As we know FES works on deference between predicted

and actual load

P(i)= A(i)-P(i)Similarly temperature and humidity error is given-= (i)- (i-1)H=H( i)-H(i-1)


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Fuzzy expert system(FES)

The errors in the weather parameters andload-correction values are fuzzified usingsix fuzzy sets such as SP(small positive),MP (medium positive), LP (largepositive),SN (small negative), NM (mediumnegative) and LN (large negative).


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Fuzzy expertsystem load correction output sets have sixmember and use linear fuzzificationprinciple for obtaining membershipgrade . the member ship value of load

correction is given as

Where Cmax is slop of load errorcorrection

L d i i f D lhi l d


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Load variation curve of Delhi loaddispatch center for

(01 to 10 Jan 2013)


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Whether variation curve of Delhi

(from 07 to 13 Jan 2013)


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Conclusion Hybrid approaches show a general way of integrating neural

networks and other methodologies to form a more efficientmodel of forecasting.

The hybrid neuro-fuzzy and neuro-symbolic approaches

have been used to forecast loads with better accuracy thanthe conventional ANN approaches when used in a stand-alone mode.

Neuro-fuzzy provides a general method for combining

available numerical information and human linguisticinformation in a common framework. The Neuro-Genetic approach is also another potential

application of hybrid approach which offers its optimizationfeature to be used for the convergence of ANN weights.


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References[1] S. H. Ling , Student Member, IEEE, Frank H. F. Leung, Senior

Member, IEEE, H. K. Lam, Member, IEEE, and Peter K. S. Tam,Member, IEEE Short -Term Electric Load Forecasting Based on aNeural Fuzzy Network IEEE TRANSACTIONS ON INDUSTRIALELECTRONICS, VOL. 50, NO. 6, DECEMBER 2003.[2] Changhao Xia, Jian Wang & keren McMenemy Short, mediumand long term load forecasting model and virtual load forecasterbased on radial basis function neural networks. Electrical Powerand Energy Systems 32 (2010) 743 750.

[3] D K Chaturvedi, P S Satsangi and P K Kalra Fuzzified neuralnetwork approach for load forecasting. engineering intelligentsystems vol 8 no 1 march 2001.

[4] Gaddam.Mallesham A fine load forecasting using neuralnetworks and fuggy neural networks Proceedings of the 5th WSEAS Int. Conf. on Power Systems and ElectromagneticCompatibility, Corfu, Greece, August 23-25, 2005 (pp224-229).


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Contind .. [5] Santos, P.J., Martins, A.G., Pires , A.J., Martins, J. F.,Mendes, R.V Short term load forecast using trendinformation and process reconstruction

[6] Mrs. J. P. Rothe ,Dr. A. K. Wadhwani and Dr. S. Wadhwani Hybrid and integrated approach to shortterm load forecasting. International Journal of EngineeringScience and Technology Vol. 2(12), 2010, 7127-7132

[7] Hesham K. Alfares and Mohammad Nazeeruddin Electric load forecasting: literature survey and classicationof Methods. International Journal of Systems Science,2002, volume 33, number 1, pages 23-34


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Contind .. [8] MING-GUANG ZHANG Short-term load forcstingbased on support vector machines regression.Proceedings of the Fourth International Conference on MachineLearning and Cybernetics, Guangzhou, 18-21 August2005.

[09] C. F. Juang, J. Y. Lin, and C. T. Lin , Geneticreinforcement learning through symbiotic evolution forfuzzy controller design, IEEE Trans. Syst., Man, Cybern. B,

vol. 30, no. 2, pp. 290 302, Apr. 2000.

[10] G. X. Yao and Y. Liu, Evolutionary programming madefaster, IEE Trans. Evol. Comput., vol. 3, pp. 82 102, July 1999.


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Thank You

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LOAD FORECASTING USING HYBRID CONTROLLERS