Comparison of Machine Learning Methods for Electricity Demand Forecasting in Bosnia and Herzegovina

Abstract

Electricity demand forecasting is one of the most important components in the power system analysis. Furthermore, it is difficult and complicated process to forecast energy consumption. This study deals with modeling of the electrical energy consumption in Bosnia and Herzegovina in order to forecast future consumption of electrical loads based on temperature variables using machine learning methods. We used three different  machine learning methods for analyzing short term forecasting. The methods were trained using historical load data, collected from JP Elektroprivreda electrical power utility in BiH, and also considering weather data which is known to have a big impact on the use of electric power. Comparing the results it was seen that prediction for 500 hours is pretty good in range from 92,92% for reactive power till 98.84% for active power. Four different parameters were analyzed mean absolute error, root mean squared error, relative absolute error and root relative square error. The best results for apparent power were gotten with linear regression and are presented as for mean absolute error 9.84, root mean squared error 13.62, relative absolute error 14.06%, root relative squared error 14.39%. It is also seen from the results that,  the short term power consumption can be predicted which is important for maintaining of the voltage at the consumer side.