Short-Term Power Prediction of Building Integrated Photovoltaic (BIPV) System Based on Machine Learning Algorithms. (28th April 2021)

• Record Type: Journal Article
• Authors: Kabilan, R.; Chandran, V.; Yogapriya, J.; Karthick, Alagar; Gandhi, Priyesh P.; Mohanavel, V.; Rahim, Robbi; Manoharan, S.
• Other Names: Hupkes Jurgen Academic Editor.
• Is Part Of: International journal of photoenergy. Volume 2021(2021)
• Publication Date: 2021-04-28
• Subjects: Photochemistry -- Periodicals; Photobiology -- Periodicals; Chemistry, Physical and theoretical -- Periodicals; Photochimie; Photobiologie; Chimie physique et théorique; Chemistry, Physical and theoretical; Photobiology; Photochemistry; Electronic journals; Periodicals; 541.35
• DOI: 10.1155/2021/5582418
• Languages: English
• ISSNs: 1110-662X

Abstract

Abstract : 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.