Application of artificial neural network to power consumption forecasting for the Sarajevo region

Abstract

This paper presents an innovative method for forecasting power consumption in the power system using an artificial neural network (ANN). The method was validated in the case of predicting power consumption for the Sarajevo region in Bosnia and Herzegovina. Power consumption is planned daily for the day-ahead with hourly resolution. Measured data on air temperature, wind speed, and insolation for 2017 to 2020 were utilized as input variables in the proposed power consumption forecasting method. The influence of these input variables on power consumption was analyzed using the Pearson correlation coefficient. The neural network underwent training with data on input variables and power consumption from 2017 to 2020 and was subsequently applied to forecast day-ahead power consumption for 2021. Due to the implementation of a neural network with a greater number of input variables, a smaller error in the power consumption forecast for 2021 was achieved compared to the forecast performed by the Electric Power Company. Therefore, the proposed method can be used as a more reliable tool for day-ahead power consumption forecasting. Additionally, the continual increase in the historical data on power consumption and influencing variables over time is expected to further enhance the reliability of power consumption forecasting using ANN.