In this article, we present a not fully connected recurrent network applied to the problem of load forecasting. Although many authors have pointed out that Recurrent Networks were able to modelize NARMAX process (Non linear Auto Regressive Moving Average with eXogeneous variables), we present a constructing scheme for the MA part. In addition we present a modification of the learning step which...

Long-term load forecasting aims at predicting the evolution of the electric consumption in a certain area in order to resize the grid in accordance. There are two components to study: the increment in existing consumption and the appearance of new clients. We focus here on the latter. With this purpose, we present an ongoing work that applies agent-based modelling to this end. Representing each...

The accuracy of forecasting of electrical load for the electricity industry has a vital significance in the renewal of economic structure as well as various equations including: purchasing and producing energy, load fluctuation, and the development of infrastructures. Its short-term forecasting has a significant role in designing and utilizing power systems and in the distribution systems and h...

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. ...

This paper presents a straight forward application of Layer Recurrent Neural Network (LRNN) to predict the load of a large distribution network. Short term load forecasting provides important information about the system’s load pattern, which is a premier requirement in planning periodical operations and facility expansion. Approximation of data patterns for forecasting is not an easy task to p...

This paper presents an artificial neural network(ANN) approach to electric load forecasting. The ANN is used to learn the relationship among past, current and future temperatures and loads. In order to provide the forecasted load, the ANN interpolates among the load and temperature data in a training data set. The average absolute errors of the one-hour and 24-hour ahead forecasts in our test o...

This paper aims models electricity load curves for short-term forecasting purposes. A broad class of multivariate dynamic regression model is proposed to model hourly electricity load. Alternative forecasting models, special cases of our general model, include separate time series regressions for each hour and week day. All the models developed include components that represent trends, seasons ...

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...