基于机器学习的太阳能发电预测,有无MPPT控制器

Debottam Mukherjee, Samrat Chakraborty, Pabitra Kumar Guchhait, Joydeep Bhunia
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引用次数: 7

摘要

可再生能源发电对气候条件的依赖程度高,具有不可预测性。在印度,仅次于风能的第二大可再生能源发电是太阳能。因此,预测太阳能发电是必要的,因为它取决于太阳辐照度和温度。在本文中,利用机器学习对太阳能发电进行了预测,并在使用和不使用MPPT控制器的情况下进行了预测。这项研究是在印度奥里萨邦的巴达贝纳库迪进行的。基于机器学习的预测技术一直被证明比基于统计的预测技术更好。不同的机器学习模型被应用于所取的数据集。结果表明:粗树模型是有MPPT控制器的太阳能发电预测的最佳模型,RMSE为1.675;RQGPR模型是无MPPT控制器的太阳能发电预测的最佳模型,RMSE为1.628。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning based Solar Power Generation Forecasting with and without MPPT Controller
The renewable resources based power generation is unpredictable since it highly depends on the conditions of climate. In India, after wind power, the second largest renewable based power generation is solar power. Therefore, forecasting for solar power generation is necessary since it depends on solar irradiance and temperature. In this paper, forecasting for solar power generation using machine learning has been done with and without using MPPT controller. The study has been done on Badabenakudi, Orissa, India. Machine learning based forecasting techniques has always been proved best than statistical based forecasting techniques. Different machine learning models have been applied on the data set taken. The result shows that Coarse Tree is the best model for solar power generating forecasting with MPPT controller having RMSE of 1.675 and Rational Quadratic Gaussian Process Regression (RQGPR) is the best model for solar power generation forecasting without MPPT controller having RMSE of 1.628.
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