A multi‐predictor model to estimate solar and wind energy generations. (15th August 2017)

• Record Type: Journal Article
• Authors: Balali, Mohammad Hasan; Nouri, Narjes; Rashidi, Mohammad; Nasiri, Adel; Otieno, Wilkistar
• Is Part Of: International journal of energy research. Volume 42:Number 2(2018)
• Page Start: 696
• Page End: 706
• Publication Date: 2017-08-15
• Subjects: multi‐predictor model -- regression -- renewable energy generation -- solar energy -- statistical analysis -- wind energy; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Power resources -- Research -- Periodicals; 621.042
• DOI: 10.1002/er.3853
• Languages: English
• ISSNs: 0363-907X

Abstract

Summary: Recent technological developments in renewable energy systems and significant growth of solar and wind energy have made these 2 renewable sources potential viable alternatives for conventional energy sources. However, due to intermittent nature, their reliability and availability are not similar to traditional sources. Hence, it is crucial to estimate the solar and wind availability and contribution more accurately. There are various factors affecting the generation capacity of renewable sources. There has been a vast research on the impact of factors related to climate condition such as wind speed, air temperature, and humidity on renewable energy generation. However, there are several other factors with indirect impact on renewable capacity and generation mostly overshadowed by the climate factors. In this study, a multi‐predictor regression model is developed and presented for solar and wind energy generation capacity across the USA. Our study of 50 states shows how the generation capacity can be affected by several indexes including human development index. Variables with the more significant impacts have been chosen using a regression analysis. A recommendation on the best transformation of the response variables and sensitivity analysis of the results has also been presented. The results provide a model to estimate the generation capacity using significant predictors. For instance, the impact of population growth on the wind turbine generation can be explored using these models.