Performance Analysis of Machine Learning Techniques for Load Forecasting

Abstract

Load forecasting is a key element in the performance of an electrical power system because of the associated economic constraints. The amount of load consumed depends on a number of factors including the environment, time of the day and day of week etc. Such complex features make the load forecasting task a tricky one, warranting efficient techniques for the problem. In this paper, four machine learning models named: K-Nearest Neighbors (KNN) based regression, Deep Neural Network (DNN) based regression, Random Forest (RF) based regression and Decision Tree (DT) based regression have been employed for the load forecasting purpose on an hourly historical load consumption dataset of a household in Islamabad, Pakistan, aggregated over a period of four years. The results show that the DT model gives the best forecasting results as compared to the RF, DNN and KNN algorithms, yielding a very low Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE).