Electricity Demand Prediction using Data Driven Forecasting Scheme: ARIMA and SARIMA for Real-Time Load Data of Assam

Abstract

Aim of forecasting electrical load focuses in predicting satisfactorily and accurately the demand that might increase or decrease in the future. A large number of engineering applications count on accurate and reliable prediction models for electrical load demand. A precise forecasting of load helps in planning the capacity and operations of power companies to reliably supply energy to the consumers. In this study electrical load (L) in Assam is predicted using a data driven forecasting scheme. The study is carried out using daily 24 hourly L data obtained from SLDC, Kahilipara, Assam. The study focuses mainly on two types of regression model: ARIMA and SARIMA and also provides a performance evaluation of the models. The input data has been split into two groups of training and testing data to build the forecasting model. The correctness of the forecasting models has been assessed using the different error matrices. The final results indicated that the SARIMA model that considers the seasonality of load data provided better prediction with minimum error. MATLABR2016a was used during the entire analysis.