Electricity is of great vital and indispensable to national economies. A new short-term load forecasting for micro grid is proposed in this paper. After comparing and analyzing all load characteristic in the time domain and frequency domain, we apply wavelet transform to decompose the load signal. After that, the training set and text set are selected in consideration of the effects generated b...

Managing electrical energy supply is a complex task. The most important part of electric utility resource planning is forecasting of the future load demand in the regional or national service area. This is usually achieved by constructing models on relative information, such as climate and previous load demand data. In this paper, a genetic programming approach is proposed to forecast long term...

Smart grids, or intelligent electricity grids that utilize modern IT/communication/control technologies, become a global trend nowadays. Forecasting of future grid load (electricity usage) is an important task to provide intelligence to the smart gird. Accurate forecasting will enable a utility provider to plan the resources and also to take control actions to balance the supply and the demand ...

Problem statement: Forecasting of electricity load demand is an essential activity and an important function in power system planning and development. It is a prerequisite to power system expansion planning as the world of electricity is dominated by substantial lead times between decision making and its implementation. The importance of demand forecasting needs to be emphasized at all level as...

A review and categorization of electric load forecasting techniques is presented. A wide range of methodologies and models for forecasting are given in the literature. These techniques are classi®ed here into nine categories: (1) multiple regression, (2) exponential smoothing, (3) iterative reweighted least-squares, (4) adaptive load forecasting, (5) stochastic time series, (6) ARMAX models bas...

As accurate Short Term Load Forecasting (STLF) is very important for improvement of the management performance of the electric industry, various short term loads forecasting methods have been developed. This paper addresses an issue of the optimal design of a neural network based short term load forecaster. A new hybrid evolutionary algorithm combining the Particle Swarm Optimization (PSO) algo...