One of the biggest challenges is towards ensuring large-scale integration of photovoltaic systems into buildings. This work is aimed at presenting a building integrated photovoltaic system power prediction concerning the building’s various orientations based on the machine learning data science tools. The proposed prediction methodology comprises a data quality stage, machine learning algorithm, weather clustering assessment, and an accuracy assessment. The results showed that the application of linear regression coefficients to the forecast outputs of the developed photovoltaic power generation neural network improved the PV power generation’s forecast output. The final model resulted from accurate forecasts, exhibiting a root mean square error of 4.42% in NN, 16.86% in QSVM, and 8.76% in TREE. The results are presented with the building facade and roof application such as flat roof, south façade, east façade, and west façade.

中文翻译：

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基于机器学习算法的建筑光伏一体化（BIPV）系统短期功率预测

最大的挑战之一是确保将光伏系统大规模集成到建筑物中。这项工作旨在基于机器学习数据科学工具，提供有关建筑物各个方向的建筑物集成光伏系统功率预测。拟议的预测方法包括数据质量阶段，机器学习算法，天气聚类评估和准确性评估。结果表明，将线性回归系数应用于已开发的光伏发电神经网络的预测输出可提高光伏发电的预测输出。最终模型来自准确的预测，NN的均方根误差为4.42％，QSVM的均方根误差为16.86％，TREE的均方根误差为8.76％。