Title: Analysing a regression model for forecasting of wind power generation

Authors: Siddharth Joshi; Ravirajsinh S. Vaghela

Addresses: Department of Electrical Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India; APA Group, London, UK ' Faculty of Computer Application, Marwadi University, Rajkot, Gujarat, India

Abstract: The power consumption pattern is changed drastically, and renewable energy sources such as wind and solar photovoltaic are remarkable in the power generation sector. The power generated from renewable sources is clean and non-pollutant, but these sources depend upon climate changes. The insolation of the sun is not uniform at each location, and hence wind pattern is not similar throughout the Earth. Due to the variable nature of the wind, the power output and energy output from the wind energy conversion system (WECS) is variable because of the stochastic nature of wind. The maximum power point trackers are installed and interfaced with wind turbine generator assembly and load, enhancing the system's effectiveness. The simulation analysis for standalone WECS with the maximum power point tracking (MPPT) algorithm is performed, and the power generated from WECS is forecasted. The short-term forecasting is performed from the data of WECS used for standalone application. The simple regression (SR) method is adopted considering the time series model for wind power at permutations in wind speed. The data used for the time series forecasting has been extracted from the simulation model of standalone 3kW WECS.

Keywords: forecasting; simple regression; time series analysis; wind power generation.

DOI: 10.1504/IJSSOC.2024.142722

International Journal of Sustainable Society, 2024 Vol.16 No.4, pp.314 - 325

Accepted: 26 Oct 2022
Published online: 19 Nov 2024 *

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