Load forecasting allows electric utilities to enhance energy purchasing and generation, load switching, contracts negotiation and infrastructure development [1]. The consumption regions have characteristic consumption profiles which determine a causal relationship between the load and a set of predictors. For short term load forecasting, in which the predictions range from few minutes to some d...

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...

Electric distribution system planning is to provide an economic expansion plan to meet the future demands in its territory. A forecast of the future electric demand and its geographic distribution is a prerequisite for distribution planning. The quality and accuracy of this forecast have large influence on the quality of the electrical distribution system planning. Spatial load forecasting emer...

This paper proposes a novel neural model to the problem of short-term load forecasting. The neural model is made up of two self-organizing map nets | one on top of the other. It has been successfully applied to domains in which the context information given by former events plays a primary role. The model was trained and assessed on load data extracted from a Brazilian electric utility. It was ...

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...

Electric load forecasting is essential for developing a power supply strategy to improve the reliability of the ac power line data network and provide optimal load scheduling for developing countries where the demand is increased with high growth rate. In this paper, a short-term load forecasting realized by a generalized neuron–wavelet method is proposed. The proposed method consists of wavele...

In this paper we present the application of techniques for Correlation Dimension Estimation to estimate the model order of electrical load data. Based on a correct model order, appropriately structured neural nets for load forecasting were designed. Satisfactory results were obtained in one-hour-ahead electrical load prediction on a six months benchmark from an electric utility in the USA.