Electric load forecasting is a key to the efficient management of power supply system. Load forecasting, which involves estimation of future load according to the previous load data. This paper presents a pragmatic methodology for short term load forecasting (STLF) using proposed hybrid method of wavelet transform (WT) and artificial neural network (ANN). It is a two stage prediction system whi...

One of the important requirements for operational planning of electrical utilities is the prediction of hourly load up to several days, known as Short Term Load Forecasting (STLF). Considering the effect of its accuracy on system security and also economical aspects, there is an on-going attention toward putting new approaches to the task. Recently, Neuro-Fuzzy modelling has played a successful...

An efficient and accurate electrical power Short Term Load forecasting plays a vital role for economic operational planning of both the electricity markets as well as regulated power systems. Till date many techniques and approaches have been presented for STLF in the literature. However there is still an essential need to develop more efficient and accurate load forecast model. This paper uses...

A reinforcement learning algorithm is proposed to improve the accuracy of short-term load forecasting (STLF) in this article. The proposed model integrates radial basis function neural network (RBFNN), support vector regression (SVR), and adaptive annealing learning algorithm (AALA). In the proposed methodology, firstly, the initial structure of RBFNN is determined by using an SVR. Then, an AAL...

Weather information is an important factor in short-term load forecasting (STLF). However, for a long time, more importance has always been attached to forecasting models instead of other processes such as the introduction of weather factors or feature selection for STLF. The main aim of this paper is to develop a novel methodology based on Fisher information for meteorological variables introd...

Electricity is indispensable and of strategic importance to national economies. Consequently, electric utilities make an effort to balance power generation and demand in order to offer a good service at a competitive price. For this purpose, these utilities need electric load forecasts to be as accurate as possible. However, electric load depends on many factors (day of the week, month of the y...

In restructuring the electric power industry, the load had an important role for market managers and participants when they develop strategies or make decisions to maximize their profit. Therefore, accurate short term load forecasting (STLF) becomes more and more vital for all market participants such as customer or producer in competitive electricity markets. In this paper, a new hybrid algori...

The selection of input variables plays a crucial role when modelling time series. For nonlinear models there are not well developed techniques such as AIC and other criteria that work with linear models. In the case of Short Term Load Forecasting (STLF) generalization is greatly influenced by such selection. In this paper two approaches are compared using real data from a Spanish utility compan...

Abstract: A feature selection method based on the generalized minimum redundancy and maximum relevance (G-mRMR) is proposed to improve the accuracy of short-term load forecasting (STLF). First, mutual information is calculated to analyze the relations between the original features and the load sequence, as well as the redundancy among the original features. Second, a weighting factor selected b...

Short term load forecasting (STLF), which aims to predict system load over an internal of one day or one week, plays a crucial role in the control and scheduling operations of a power system. Most existing techniques on short term load forecasting try to improve the performance by selecting different prediction models. However, the performance also rely heavily on the quality of training data. ...