Abstract
This paper presents the application of time series forecasting models, namely Forecaster Autoreg and Neural Network, for predicting electricity consumption in the city of Astana. Given the pressing need to address energy efficiency and reduce environmental impact in light of global climate change, employing scientifically sound and effective solutions is crucial. Machine learning methods offer a promising approach to tackle this challenge by predicting electricity demand. In this study, a comprehensive analysis of a time series dataset was conducted, encompassing data on electricity consumption (measured in MW) in Astana from January 1, 2020, to December 31, 2020, at an hourly interval. The dataset was utilized to accurately forecast electricity demand for each hour of the following day. To enhance the precision of predictions, additional factors such as air temperature and wind speed were incorporated. These factors were deemed significant due to Astana’s distinct sharply continental climate and high windiness, making their inclusion essential for accounting for their influence on electricity demand and achieving more accurate forecasts. The neural network model was selected as the chosen methodology, as it has the capability to reveal intricate dependencies and patterns in the data, thereby facilitating more precise predictions of electricity consumption. To evaluate the accuracy and reliability of the forecasts, error indicators such as Average Absolute Error (MAE) and Average Absolute Percentage Error (MAPE) were employed. The results demonstrated that the proposed models could provide accurate forecasts with low errors.