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 ...

The electric power industry is currently undergoing an unprecedented reform. One of the most exciting and potentially profitable recent developments is increasing usage of artificial intelligence techniques. The intention of this paper is to give an overview of using neural network (NN) techniques in power systems. According to the growth rate of NNs application in some power system subjects, t...

Providing reliable electrical service has always been a key focus for distribution planners. Traditional forecasting and planning approaches have historically used average load growth over wide areas, basic analysis of weather impacts, and little, if any, attention to changing economic, resource deployment, or network topology changes. Exactly how resources and loads operate, interact, and prov...

With the prevalence of computer and development of information technology, Geographic Information Systems (GIS) have long used for a variety of applications in electrical engineering. GIS are designed to support the analysis, management, manipulation and mapping of spatial data. This paper presents several usages of GIS in power utilities such as automated route selection for the construction o...

Abstract- In a typical competitive electricity market, a large number of short-term and long-term contracts are set on basis of energy price by an Independent System Operator (ISO). Under such circumstances, accurate electricity price forecasting can play a significant role in improving the more reasonable bidding strategies adopted by the electricity market participants. So, they cannot only r...

The worldwide electric power industry has seen many changes over the last 20 years. During this period many regulated or state-owned monopoly markets have been deregulated. In an electricity market, electricity price is decided based on demand and supply bids from the market participants; therefore, the importance of ShortTerm Load Forecasting (STLF) has been rising in these markets [1]. Load f...

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 fac...

Short-Term Load Forecasting (STLF) plays an important role for the economic and secure operation of power systems. In this paper, Continuous Genetic Algorithm (CGA) is employed to evolve the optimum large neural networks structure and connecting weights for one-day ahead electric load forecasting problem. This study describes the process of developing three layer feed-forward large neural netwo...

This paper develops medium term electric load forecasting using neural networks, based on historical series of electric load, economic and demographic variables. The neural network chosen for this work is the Time Lagged Feedforward Network (TLFN), which combines conventional network topology (multilayer perceptron) with good handling of time dependencies by means of gamma memory. This is a ver...