This work examines recent publications in forecasting in various fields, these include: wind power forecasting; electricity load forecasting; crude oil price forecasting; gold price forecasting energy price forecasting etc. In this review, categorization of the processes involve in forecasting are divided into four major steps namely: input features selection; data pre-processing; forecast mode...

⎯ In the process of power load forecasting, electricity experts always divide the forecasting situation into several categories, and the same category uses the same forecasting model. There exists such a situation that some load curve which domain experts consider belonging to the same category has shown the different characteristics, but some load curve which belongs to different category see...

This paper presents a new approach for short-term load forecasting (STLF). Curve fitting prediction and time series models are used for hourly loads forecasting of the week days. The curve fitting prediction (CFP) technique combined with genetic algorithms (GAs) is used for obtaining the optimum parameters of Gaussian model to obtain a minimum error between actual and forecasted load. A new tec...

This paper presents an optimization algorithm to solve the short-term load forecasting problem more quickly and accurately in progress of smart grid development. The new approach employs generalized regression neural network (GRNN) to select influence factors of short-term load, and then a least squares-support vector machine (LS-SVM) based on harmony search algorithm (HS) optimization algorith...

− This paper describes a neural network system for power electric load forecasting of telecommunication station. Getting an accuracy useful for contractual purpose a separately daily forecast of both main load and its oscillation is proposed. For the mean daily forecast we used a three layers multilayer perceptron (MLP), while to the oscillation forecasting we realized a system composed by a ML...

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...

In this paper we have proposed a control technique for the automatic generation control of multi generating power unit of the interconnected power system. This technique established the relationship between the economic load dispatch and load forecasting mechanism to the classical concepts of the load frequency control (LFC). The LFC system monitors to keep the power system frequency at nominal...

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 ...

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...

— In power systems the next day’s power generation must be scheduled every day, day ahead short-term load forecasting (STLF) is a necessary daily task for power dispatch. Its accuracy affects the economic operation and reliability of the system greatly. Under prediction of STLF leads to insufficient reserve capacity preparation and in turn, increases the operating cost by using expensive peakin...