Open AccessShort-Term Forecasting of Hourly Electricity Power Demand
Author(s) -
Симона Филипова-Петракиева,
Vencislav Dochev
Publication year - 2022
Publication title -
engineering, technology and applied science research/engineering, technology and applied science research
Language(s) - English
Resource type - Journals
eISSN - 2241-4487
pISSN - 1792-8036
DOI - 10.48084/etasr.4787
Subject(s) - autoregressive integrated moving average , cluster analysis , term (time) , electricity , electric power system , electric power , power (physics) , econometrics , time series , computer science , power demand , meteorology , environmental science , statistics , power consumption , mathematics , engineering , geography , artificial intelligence , electrical engineering , physics , quantum mechanics
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.