Solar radiation prediction using boosted decision tree regression model: A case study in Malaysia
Solar radiation prediction using boosted decision tree regression model: A case study in Malaysia
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DOI：
10.1007/s11356-021-12435-6
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发表时间：
2021-01-23
影响因子：
5.8
通讯作者：
Ahmed, Ali Najah
中科院分区：
文献类型：
--
作者：
Jumin, Ellysia;Basaruddin, Faridah Bte;Ahmed, Ali Najah
在发电和可再生无碳能源行业中，可靠、准确地捕捉太阳辐射变化的预测模型是必不可少的。马来西亚位于赤道地区，气候特点是太阳能资源丰富，因此具有发展这一产业的巨大潜力。然而，太阳能仅占总能源利用的2-4.6%。最近，在发达国家，基于人工智能（AI）技术的各种预测模型已被应用于预测太阳辐射。在这项研究中，最新的人工智能算法之一，即提升决策树回归（BDTR）模型，被应用于根据在马来西亚收集的数据预测太阳辐射的变化。将该模型与线性回归、神经网络等传统回归算法进行了比较。研究了两种不同的归一化技术（高斯归一化器合并归一化器），分裂大小和不同的输入参数，以提高模型的准确性。通过灵敏度分析和不确定性分析验证了模型的准确性。结果表明，BDTR优于其他算法具有较高的准确性。这项研究的资金可以被工程师用作改善马来西亚可再生能源部门的可靠工具，并提供替代的可持续能源。
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.