The problem of short-term electric load forecasting (STLF) is considered. A modified architecture of Elman-type recurrent neural network is proposed. It utilizes a special fuzzification layer to deal with quantitative as well as ordinal and nominal data. The second hidden layer of the network consists of standard Rosenblatt-type neurons with sigmoidal activation functions. The context layer is ...

This paper presents a model for short-term load forecasting using least square support vector machines. Available data are analyzed and appropriate features are selected for the model. Last 24 hours load demands are used for features in combination with day in week and hour in day. It is shown that temperature is not always a very good feature for the model. Appropriate data set is used for the...

The paper presents the regularization procedure for the neural network reduction to obtain the best results of load forecasting in the power system. The OBD pruning method will be applied in the solution. The numerical experiments have been concentrated on the prognosis of the load in the power system. Two kinds of experiments will be described: 24-hour forecast and the forecast of the daily me...

A clustering based technique has been developed and implemented for Short Term Load Forecasting, in this article. Formulation has been done using Mean Absolute Percentage Error (MAPE) as an objective function. Data Matrix and cluster size are optimization variables. Model designed, uses two temperature variables. This is compared with six input Radial Basis Function Neural Network (RBFNN) and F...

Utilities are required to provide reliable power to customers. In the design stages, utilities need to plan ahead for anticipated future load growth under different possible scenarios. Their decisions and designs can affect the gam or loss of millions of dollars for their companies as well as customer satisfaction and future economic growth in their territory. This paper proposes and describe t...

This paper employs chaos theory into power load forecasting. Lyapunov exponents on chaos theory are calculated to judge whether it is a chaotic system. Delay time and embedding dimension are calculated to reconstruct the phase space and determine the structure of artificial neural network ANN . Improved back propagation BP algorithm based on genetic algorithm GA is used to train and forecast. F...

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,...

Load forecasting allows for the utilities to plan their operations to serve their customers with more reliable and economical electric power. With the developments in computer and information technology new techniques to accurately forecast power system loading are emerging. This research culminates in development of modified algorithms for short-term load forecasting (STLF) of a utility grade ...

This paper describes the performance, generated power flow distribution and redistribution for each power plant on the grid based on adapting load and weather forecasting data. Both load forecasting and weather forecasting are used for collecting predicting data which are required for optimizing the performance of the grid. The stability of each power systems on the grid highly affected by load...

Research of short-term load forecasting has important practical application value in the field of power network dispatching. The regession models of least squares support vector machines (LS-SVM) have been applied to load forecasting field widely, and the regression accuracy and generalization performance of the LS-SVM models depend on a proper selection of its parameters. In this paper, a new ...