Fuzzy based day ahead prediction of electric load using Mahalanobis distance

Abstract

Prediction of electric load is very important issue for modern day power system engineers and a very good day ahead prediction of electric load is required for efficient performance of various Energy Management System (EMS) functions such as unit commitment, economic dispatch, fuel scheduling, and unit maintenance. A fuzzy based approach for day ahead prediction of electric load using Mahalanobis distance has been chosen in this work. Mahalanobis distance provides the similar characteristic days from the historical data set based on some independent variables generally of climate and time (such as temperature, day of the week, month etc.) and that are used to predict the dependent variable, i.e., day ahead electric load demand. The independent variables considered for the distance measure include the hourly humidity values, hourly temperatures values, and the day type variable. The similarity between the load on the day of prediction and that on similar characteristic days, which is evaluated using fuzzy system.