Short-term load forecasting using an artificial neural network

Short-Term Load Forecasting Using Artificial Neural Network

-Artificial neural network (ANN) has been used for many years in sectors and disciplines like medical science, defence industry, robotics, electronics, economy, forecasts, etc. The learning property of ANN in solving nonlinear and complex problems called for its application to forecasting problems. This report present the development of an ANN based short-term load forecasting model for the 132...

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

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...

Comparisons of Short Term Load Forecasting using Artificial Neural Network and Regression Method

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 peaking ...