FORECASTING ELECTRICITY CONSUMPTION: CASE STUDY IN ASTANA
DOI:
https://doi.org/10.37943/14TRMF1662Keywords:
Machine learning, time series, prediction, electricity, consumptionAbstract
This paper presents time series forecasting models Forecaster Autoreg and Neural Network for predicting electricity consumption in the city of Astana. Given the limited natural resources and the need to reduce the impact on the environment due to global climate change, energy efficiency remains an urgent problem requiring the search for scientifically sound and effective solutions. One of the ways to address this problem is the use of machine learning methods to predict electricity demand. In this study, a time series dataset was explored containing data on electricity consumption (measured in MW) in the city of Astana during the period from January 1, 2020 to December 31, 2020 in an hourly interval. These data were used to utilize a model that predicts the electricity demand for the next day with an accuracy of every hour. To improve the accuracy of forecasting, additional factors such as air temperature and wind speed were included. This is since Astana experiences a sharply continental climate and high windiness. Including these factors allows for accounting for their influence on the electricity demand and thus achieving more accurate forecasting. A neural network model was utilized for this purpose, as it can uncover complex dependencies and patterns in the data, thereby aiding in achieving more precise predictions of electricity consumption. The accuracy and reliability of forecasts were evaluated by error indicators such as Average Absolute Error (MAE) and Average Absolute Percentage Error (MAPE), and the results showed that the models can provide accurate forecasts with low errors.
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