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 temperature, maximum day temperature, minimum dew temperature, average dew point temperature, maximum dew temperature, maximum humidity, average humidity and minimum humidity are collected from weather forecasting station in Hamedan city province. By studying these parameters and daily electrical energy consumption registered in Distribution Company of Hamedan city province and using statistical analysis factors, the parameters which affect daily electricity consumption have been recognized. By applying ESN neural network modeling this load with recognized parameters has been carried out and load forecasting has been assessed. Forecasting result indicates high accuracy of ESN network system for load forecasting short term.