Use of a Big Data Analysis in Regression of Solar Power Generation on Meteorological Variables for a Korean Solar Power Plant

• Published in: Applied sciences Vol. 11; no. 4; p. 1776
• Main Authors: Kim, Young Seo, Joo, Han Young, Kim, Jae Wook, Jeong, So Yun, Moon, Joo Hyun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-02-2021
• Subjects: Alternative energy; Big Data; Data analysis; Daylight; Dependent variables; Electricity; Energy management; Evaporation; Humidity; Hypotheses; Independent variables; Insolation; Meteorological data; Multiple regression models; Neural networks; Power plants; Regression analysis; regression model; Relative humidity; Renewable resources; Samcheonpo; Solar energy; Solar power; solar power plant; Variables; Weather; Web portals
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app11041776

This study identified the meteorological variables that significantly impact the power generation of a solar power plant in Samcheonpo, Korea. To this end, multiple regression models were developed to estimate the power generation of the solar power plant with changing weather conditions. The meteorological data for the regression models were the daily data from January 2011 to December 2019. The dependent variable was the daily power generation of the solar power plant in kWh, and the independent variables were the insolation intensity during daylight hours (MJ/m2), daylight time (h), average relative humidity (%), minimum relative humidity (%), and quantity of evaporation (mm). A regression model for the entire data and 12 monthly regression models for the monthly data were constructed using R, a large data analysis software. The 12 monthly regression models estimated the solar power generation better than the entire regression model. The variables with the highest influence on solar power generation were the insolation intensity variables during daylight hours and daylight time.