Short-Term Forecasting of Hourly Electricity Power Demand

Reggresion and Cluster Methods for Short-Term Prognosis

Authors

  • S. K. Filipova-Petrakieva Department of Theory of Electrical Engineering, Technical University of Sofia, Bulgaria https://orcid.org/0000-0001-6430-2698
  • V. Dochev Department of Computer Systems Faculty of Computer Systems and Technologies, Technical University of Sofia, Bulgaria

Abstract

The optimal use of electric power consumption is a fundamental indicator of the normal use of energy resources. Its quantity depends on the loads connected to the electric power grid, which are measured on an hourly basis. This paper examines forecasting methods for hourly electrical power demands for 7 days. Data for the period of 1 January 2015 and 24 December 2020 were processed, while the models' forecasts were tested on actual power load data between 25 and 31 December 2020, obtained from the Energy System Operator of the Republic of Bulgaria. Two groups of methods were used for the prognosis: classical regression methods and clustering algorithms. The first group included "moving window" and ARIMA, while the second examined K-Means, Time Series K-Means, Mini Batch K-Means, Agglomerative clustering, and OPTICS. The results showed high accuracy of the forecasts for the prognosis period.

Keywords:

short-term prognosis, hourly electricity power demand, reggresion analysis, clustering methods

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How to Cite

[1]
S. K. Filipova-Petrakieva and V. Dochev, “Short-Term Forecasting of Hourly Electricity Power Demand: Reggresion and Cluster Methods for Short-Term Prognosis”, Eng. Technol. Appl. Sci. Res., vol. 12, no. 2, pp. 8374–8381, Apr. 2022.

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