A systematic analysis of meteorological variables for PV output power estimation. (June 2020)

• Record Type: Journal Article
• Authors: AlSkaif, Tarek; Dev, Soumyabrata; Visser, Lennard; Hossari, Murhaf; van Sark, Wilfried
• Is Part Of: Renewable energy. Volume 153(2020)
• Page Start: 12
• Page End: 22
• Publication Date: 2020-06
• Subjects: Photovoltaic -- Solar power estimation -- Meteorological variables -- Machine learning -- Regression methods; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2020.01.150
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: While the large-scale deployment of photovoltaics (PV) for generating electricity plays an important role to mitigate global warming, the variability of PV output power poses challenges in grid management. Typically, the PV output power is dependent on various meteorological variables at the PV site. In this paper, we present a systematic approach to perform an analysis on different meteorological variables, namely temperature, dew point temperature, relative humidity, visibility, air pressure, wind speed, cloud cover, wind bearing and precipitation, and assess their impact on PV output power estimation. The study uses three years of input meteorological data and PV output power data from multiple prosumers in two case studies, one in the U.S. and one in the Netherlands. The analysis covers the correlation and interdependence among the meteorological variables. Then, by using machine learning-based regression methods, we identify the primary meteorological variables for PV output power estimation. Finally, the paper concludes that the impact of using a lower-dimensional subspace of meteorological variables per location, as input for the regression methods, results in a similar estimation accuracy in the two case studies. Highlights: An analysis of the correlation and interdependence of 9 different meteorological variables in two case studies. A dimensionality reduction of the input meteorological variables is presented. Several machine learning regression methods are evaluated for estimating PV output power. The importance and ranking of the variables depend on the climate of the area of study. A lower-dimension subspace of meteorological variables can result in a slightly similar estimation accuracy.