Short-term Electrical Energy Consumption Forecasting Using GMDH-type Neural Network

GMDH: An R Package for Short Term Forecasting via GMDH-Type Neural Network Algorithms

Group Method of Data Handling (GMDH)-type neural network algorithms are the heuristic self organization method for the modelling of complex systems. GMDH algorithms are utilized for a variety of purposes, examples include identification of physical laws, the extrapolation of physical fields, pattern recognition, clustering, the approximation of multidimensional processes, forecasting without mo...

Neural Networks in Forecasting Electrical Energy Consumption

This paper presents an artificial neural network (ANN) approach to electric energy consumption (EEC) forecasting. In order provide the forecasted energy consumption, the ANN interpolates between the EEC and its determinants in a training data set. In this study, two ANN models are presented and implemented on real EEC data. The first model is a univariate model based on past consumption values....

Efficient Short-Term Electricity Load Forecasting Using Recurrent Neural Networks

Short term load forecasting (STLF) plays an important role in the economic and reliable operation ofpower systems. Electric load demand has a complex profile with many multivariable and nonlineardependencies. In this study, recurrent neural network (RNN) architecture is presented for STLF. Theproposed model is capable of forecasting next 24-hour load profile. The main feature in this networkis ...

Short Term Load Forecasting by Using ESN Neural Network Hamedan Province Case Study

Abstract Forecasting electrical energy demand and consumption is one of the important decision-making tools in distributing companies for making contracts scheduling and purchasing electrical energy. This paper studies load consumption modeling in Hamedan city province distribution network by applying ESN neural network. Weather forecasting data such as minimum day temperature, average day temp...

Short-term Electrical Load Forecasting for an Institutional/Industrial Power System Using an Artificial Neural Network