Solar radiation prediction using boosted decision tree regression model: A case study in Malaysia

Jumin, Ellysia; Basaruddin, Faridah Bte; Yusoff, Yuzainee Bte Md; Latif, Sarmad Dashti; Ahmed, Ali Najah

Solar radiation prediction using boosted decision tree regression model: A case study in Malaysia

2021

Jumin, Ellysia | Basaruddin, Faridah Bte | Yusoff, Yuzainee Bte Md | Latif, Sarmad Dashti | Ahmed, Ali Najah

Reliable and accurate prediction model capturing the changes in solar radiation is essential in the power generation and renewable carbon-free energy industry. Malaysia has immense potential to develop such an industry due to its location in the equatorial zone and its climatic characteristics with high solar energy resources. However, solar energy accounts for only 2–4.6% of total energy utilization. Recently, in developed countries, various prediction models based on artificial intelligence (AI) techniques have been applied to predict solar radiation. In this study, one of the most recent AI algorithms, namely, boosted decision tree regression (BDTR) model, was applied to predict the changes in solar radiation based on collected data in Malaysia. The proposed model then compared with other conventional regression algorithms, such as linear regression and neural network. Two different normalization techniques (Gaussian normalizer binning normalizer), splitting size, and different input parameters were investigated to enhance the accuracy of the models. Sensitivity analysis and uncertainty analysis were introduced to validate the accuracy of the proposed model. The results revealed that BDTR outperformed other algorithms with a high level of accuracy. The funding of this study could be used as a reliable tool by engineers to improve the renewable energy sector in Malaysia and provide alternative sustainable energy resources.

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