Comparative analysis of models for short-term forecasting of electricity consumption

Korablyov, M.; Kobzev, I.; Chubukin, O.; Antonov, D.; Polous, V.; Tkachuk, O.

Публікація:
Comparative analysis of models for short-term forecasting of electricity consumption

dc.contributor.author	Korablyov, M.
dc.contributor.author	Kobzev, I.
dc.contributor.author	Chubukin, O.
dc.contributor.author	Antonov, D.
dc.contributor.author	Polous, V.
dc.contributor.author	Tkachuk, O.
dc.date.accessioned	2024-11-28T22:00:21Z
dc.date.available	2024-11-28T22:00:21Z
dc.date.issued	2024
dc.description.abstract	Forecasting electricity consumption is an urgent task, and the solution significantly affects the efficiency of the use of energy resources. The paper considers short-term forecasting of electricity consumption, which predicts the amount of energy that will be used in a short period, from several hours to several days in advance. There are various short-term forecasting models, so it is important to reasonably choose a model that provides analysis and effective forecasting of electricity consumption to optimize the use of energy resources. The purpose of the work is to analyze the main forecasting models, such as statistical models (autoregressive model, moving average, exponential smoothing, moving average with autoregression and integration) and deep learning models (artificial neural network, recurrent neural network, long short-term memory, transformer), indicating their advantages and disadvantages, and choosing the best of them. The experimental results of a comparative analysis of power consumption forecasting models are presented, which showed that the transformer model was 1.5% - 2% more effective in power consumption forecasting according to various metrics. Its higher level of accuracy, reflected in low error values and high coefficient of determination, indicates its high adaptability to the dynamics of electricity consumption.
dc.identifier.citation	Comparative analysis of models for short-term forecasting of electricity consumption / M. Korablyov, I. Kobzev, O. Chubukin, D. Antonov, V. Polous, O. Tkachuk // Proceedings of the 12th International Conference Information Control Systems & Technologies (ICST 2024), September 23–25, 2024. - pp. 250–261. - https://ceur-ws.org/Vol-3790/paper22.pdf
dc.identifier.issn	1613-0073
dc.identifier.uri	https://openarchive.nure.ua/handle/document/29342
dc.language.iso	en
dc.subject	forecasting
dc.subject	time series
dc.subject	power consumption
dc.subject	neural network
dc.subject	deep learning
dc.title	Comparative analysis of models for short-term forecasting of electricity consumption
dc.type	Conference proceedings
dspace.entity.type	Publication