Comparison of Load Demand Forecasting Using Machine Learning and Classical Multivariate Regression Analysis

Najashi Barau Gafai

Abstract


To avoid an energy catastrophe, variable electrical load and ever-increasing load demand must be predicted or projected. Both suppliers and consumers benefit from forecasting the day-ahead electrical load. With precise load estimates, the reduction of electricity waste and the sensible dispatch of electric generator units can be considerably enhanced. This article is focused on comparing Artificial Neutral Network ANN (nonlinear model) and classical Multivariate linear Regression MLR (linear model) techniques for Short Term Load Forecasting (STLF). The live load data from a sub-station of the Abuja Electricity Distribution Company (AEDC), situated in the Federal Capital of Abuja, and is procured for the presented simulation study. For the simulation study using a neural network and MLR methodology, the obtained live data is divided into three categories: validation, training, and testing. The simulation results were checked against live load data from the chosen site and confirmed to be within allowable limits. The Coefficient of Determinate (R2), Correlation Coefficient (R), Mean Square Error (MSE), and Root-Mean-Square Error (RMSE) were calculated to demonstrate the effectiveness of the proposed machine learning based STLF, and it can be safely concluded that ANN (nonlinear models) gives far more accurate results than MLR (linear model), and is reliable in predicting the load forecast. This would aid in determining the variation in electric load well in advance and providing an opportunity or scope for preparedness to meet the abrupt spike in load demand, thereby achieving the requirements of active load forecasting in the power system arena.


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