Work

Impact of Different Vegetation Scenarios on Solar Power Plant Performance: An ENVI‐Met and Machine Learning–Based Approach

Year: 2026

Type: article

Abstract: Solar energy systems are highly sensitive to microclimatic conditions, particularly in hot‐summer continental regions where elevated temperatures significantly reduce photovoltaic (PV) efficiency. This study investigates how different vegetation configurations influence the microclimate and performance of a 250 kW operational PV plant located on a 5000 m 2 area at Dicle University, Turkey. A dual‐method approach was applied by combining (i) ENVI‐met three‐dimensional microclimate simulations under four vegetation scenarios grass, broad‐leaved deciduous plants, coniferous plants, and mixed vegetation and (ii) machine learning–based multiple linear regression (MLR) using real operational PV data, including air temperature, relative humidity, wind speed, solar irradiance, and physiological equivalent temperature (PET). The machine learning model showed high predictive capability ( R 2 = 0.9737, RMSE = 378,939.55, and mean absolute error (MAE) = 484.44), revealing strong relationships between climatic factors and PV efficiency. ENVI‐met simulations demonstrated that mixed vegetation reduced summer air temperature by up to 1.7°C, while coniferous vegetation provided the greatest improvements in thermal comfort (PET). These findings confirm that vegetation‐based microclimate design can mitigate thermal stress and support improved PV performance.

Source: International Journal of Energy Research

DOI: 10.1155/er/4663626

Authors: Sülem Şenyiğit Doğan, Ahmet Koç, Gökhan Şahin, Wilfried G. J. H. M. van Sark

Institutions: Dicle University, Utrecht University

Language: English

Topic: Urban Heat Island Mitigation

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,700