Load forecasting is an important component for power system energy management system. Precise load forecasting helps the electric utility to make unit commitment decisions, reduce spinning reserve capacity and schedule device maintenance plan properly and it also reduces the generation cost and increases reliability of power systems. In this work, a fuzzy logic approach for short term load fore...

Short-term Load Forecast (STLF) is a load forecasting that very important to study because it determines the operating pattern of electrical system. Forecasting errors, both positive and negative, result in considerable losses costs increase ultimately lead waste. STLF research Indonesia, especially State Electricity Company (PLN Sulselrabar), has yet be widely used. Methods mainly used are man...

This paper presents the Bayesian Combined Predictor (BCP), a probabilistically motivated predictor for Short Term Load Forecasting (STLF) based on the combination of an artificial neural network (ANN) predictor and two linear regression (LR) predictors. The method is applied to STLF for the Greek Public Power Corporation dispatching center of the island of Crete, using 1994 data, and daily load...

Prediction of peak loads in Iran up to year 2011 is discussed using the Radial Basis Function Networks (RBFNs). In this study, total system load forecast reflecting the current and future trends is carried out for global grid of Iran. Predictions were done for target years 2007 to 2011 respectively. Unlike short-term load forecasting, long-term load forecasting is mainly affected by economy...

Based on Wavelet and Reconstructed Phase Space Zunxiong Liu, Zhijun Kuang, Deyun Zhang 1.Dept. of Information and Communication Eng, Xi’an Jiaotong University. Xi’an, Shanxi, China. 2.Dept. of Information Eng, East China Jiaotong University. Nanchang, Jiangxi, China Abstract: This paper proposed wavelet combination method for short-term forecasting, which makes merit of wavelet decomposition an...

Energy is a major driver of human activity. Demand response the utmost importance to maintain efficient and reliable operation smart grid systems. The short-term load forecasting (STLF) method particularly significant for electric fields in trade energy. This model has several applications everyday operations utilities, namely switching, energy-generation planning, contract evaluation, energy p...

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 mak...

review and classification of electric load forecasting (lf) techniques based on artificial neuralnetworks (ann) is presented. a basic anns architectures used in lf reviewed. a wide range of annoriented applications for forecasting are given in the literature. these are classified into five groups:(1) anns in short-term lf, (2) anns in mid-term lf, (3) anns in long-term lf, (4) hybrid anns inlf,...

13 Selection of input features plays an important role in developing models for short14 term load forecasting (STLF). Previous studies along this line of research have focused 15 pre-dominantly on filter and wrapper methods. Given the potential value of a hybrid 16 selection scheme that includes both filter and wrapper methods in constructing an 17 appropriate pool of features, coupled with the...