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A novel clustering based Short Term Load Forecasting (STLF) using Artificial Neural Network (ANN) for forecasting the next day load is presented in this paper. The input parameters considered for prediction are load, temperature and day of the week. The daily average load of each day for all the training patterns and testing patterns is calculated and the patterns are clustered using a threshold value...
A novel clustering based short term load forecasting (STLF) using artificial neural network (ANN) to forecast the 48 half hourly loads for next day is presented in this paper. The proposed architecture uses the historical load and temperature to forecast the next day load. It is trained using back propagation algorithm and tested. The daily average load of each day for all the training patterns and...
Accurate models for electricity power load forecasting are essential to the operation and planning of an electricity company. Neural Networks are considered as a computational model that is capable of doing nonlinear curve fitting. In this research, the application of neural networks to study the design of Short Term load Forecasting (STLF) Systems for Sri Lanka was explored. Three layered neural...
A new hybrid technique using support vector machines (SVM) to forecast the next `24' hours load is proposed in this paper. Four modules consisting of the basic SVM, peak and valley SVM, averager and forecaster and adaptive combiner form the integrated method for load forecasting. The proposed architecture can forecast the next `24' hours load. The basic SVM uses the historical data of load and temperature...
Short term load forecasting is very essential to the operation of electricity companies. It enhances the energy-efficient and reliable operation of power system. Artificial neural networks have long been proven as a very accurate non-linear mapper. ANN based STLF models generally use back propagation algorithm which does not converge optimally & requires much longer time for training, which makes...
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