Title
Forecasting the Power Generation of a Solar Power Plant Taking into Account the Statistical Characteristics of Meteorological Conditions
Authors
[ 1 ] Electric Energy Department, Railway Research Institute, 50 Józefa Chłopickiego Street, 04-275 Warsaw, Poland | [ 2 ] Department of Electrical Engineering, Faculty of Electomechanic and Electrometallurgy, Dnipro Metallurgical Institute, Ukrainian State University of Science and Technologies, 2 Lazaryana Street, 49000 Dnipro, DR, Ukraine | [ 3 ] Wydział Bezpieczeństwa Narodowego, Akademia Sztuki Wojennej | [ 4 ] Department of Power Engineering, Faculty of Energy, Transport and Management Systems, Non-Profit Joint-Stock Company «Karaganda Industrial University», Republic Ave., 30, Temirtau City 101400, KR, Kazakhstan | [ 5 ] Department of Electromechanics, Electrotechnical Faculty, Kryvyi Rih National University, Vitaly Matusevich, Street, 11, 50027 Kryvyi Rih, DR, Ukraine | [ 6 ] John Paul II Academy in Biała Podlaska, Rector’s Office, Sidorska Street 95/97, 21-500 Biała Podlaska, Poland | [ 7 ] Department of Electrical Engineering and Cyber-Physical Systems, Y.M. Potebnia Engineering Educational and Scientific Institute, Zaporizhzhia National University, 66 Universytetska Street, 69600 Zaporizhzhia, ZR, Ukraine | [ P ] employee
Scientific discipline (Law 2.0)
Year of publication
2025
Article type
scientific article
Publication language
english
Keywords
EN
Abstract
EN The integration of solar generation into national energy balances is associated with a wide range of technical, economic, and organizational challenges, the solution of which requires the adoption of innovative strategies for energy system management. The inherent variability of electricity production, driven by fluctuating climatic conditions, complicates system balancing processes and necessitates the reservation of capacities from conventional energy sources to ensure reliability. Under modern market conditions, the pricing of generated electricity is commonly based on day-ahead forecasts of day energy yield, which significantly affects the economic performance of solar power plants. Consequently, achieving high accuracy in day-ahead electricity production forecasting is a critical and highly relevant task. To address this challenge, a physico-statistical model has been developed, in which the analytical approximation of daily electricity generation is represented as a function of a random variable—cloud cover—modeled by a β-distribution. Analytical expressions were derived for calculating the mathematical expectation and variance of daily electricity generation as functions of the β-distribution parameters of cloudiness. The analytical approximation of daily generation deviates from the exact value, obtained through hourly integration, by an average of 3.9%. The relative forecasting error of electricity production, when using the mathematical expectation of cloudiness compared to the analytical approximation of daily generation, reaches 15.2%. The proposed forecasting method, based on a β-parametric cloudiness model, enhances the accuracy of day-ahead production forecasts, improves the economic efficiency of solar power plants, and contributes to strengthening the stability and reliability of power systems with a substantial share of solar generation.
Pages (from - to)
1 - 32
Comments
This article belongs to the Section A2: Solar Energy and Photovoltaic Systems, nr artykułu: 5363. Bibliografia, netografia na stronach 30-32.
License type
CC BY (attribution alone)
Open Access Mode
open journal
Open Access Text Version
final published version
Release date
11.10.2025
Date of Open Access to the publication
at the time of publication
Full text of article
Access level to full text
public
Ministry points / journal
140
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