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Energies 2017, 10(1), 7; doi:10.3390/en10010007

Correlation Feature Selection and Mutual Information Theory Based Quantitative Research on Meteorological Impact Factors of Module Temperature for Solar Photovoltaic Systems

1
State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, Hebei, China
2
Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
3
Renewable Energy Department, China Electric Power Research Institute, Beijing 100192, China
4
Fenner School of Environment and Society, The Australian National University, Canberra, ACT 2601, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Pierluigi Siano
Received: 2 September 2016 / Revised: 10 December 2016 / Accepted: 15 December 2016 / Published: 22 December 2016
(This article belongs to the Special Issue Innovative Methods for Smart Grids Planning and Management)
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Abstract

The module temperature is the most important parameter influencing the output power of solar photovoltaic (PV) systems, aside from solar irradiance. In this paper, we focus on the interdisciplinary research that combines the correlation analysis, mutual information (MI) and heat transfer theory, which aims to figure out the correlative relations between different meteorological impact factors (MIFs) and PV module temperature from both quality and quantitative aspects. The identification and confirmation of primary MIFs of PV module temperature are investigated as the first step of this research from the perspective of physical meaning and mathematical analysis about electrical performance and thermal characteristic of PV modules based on PV effect and heat transfer theory. Furthermore, the quantitative description of the MIFs influence on PV module temperature is mathematically formulated as several indexes using correlation-based feature selection (CFS) and MI theory to explore the specific impact degrees under four different typical weather statuses named general weather classes (GWCs). Case studies for the proposed methods were conducted using actual measurement data of a 500 kW grid-connected solar PV plant in China. The results not only verified the knowledge about the main MIFs of PV module temperatures, more importantly, but also provide the specific ratio of quantitative impact degrees of these three MIFs respectively through CFS and MI based measures under four different GWCs. View Full-Text
Keywords: photovoltaic (PV) module temperature; meteorological impact factor (MIF); quantitative influence analysis; correlation-based feature selection (CFS); mutual information (MI) theory photovoltaic (PV) module temperature; meteorological impact factor (MIF); quantitative influence analysis; correlation-based feature selection (CFS); mutual information (MI) theory
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Sun, Y.; Wang, F.; Wang, B.; Chen, Q.; Engerer, N.; Mi, Z. Correlation Feature Selection and Mutual Information Theory Based Quantitative Research on Meteorological Impact Factors of Module Temperature for Solar Photovoltaic Systems. Energies 2017, 10, 7.

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