Published on April 2021 | Image Processing and Machine Learning

Short-Term Power Prediction of Building Integrated Photovoltaic (BIPV) System Based on Machine Learning Algorithms
Authors: S Manoharan, R Kabilan, V Chandran, J Yogapriya, Alagar Karthick, Priyesh P Gandhi, V Mohanavel, Robbi Rahim
Journal Name: Hindawi - International Journal of Photoenergy
Volume: 2021 Issue: 1 Page No: 1-11
Indexing: SCI/SCIE,SCOPUS,Web of Science,Google Scholar,DOAJ

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.

Download PDF
View Author/Co-Author
Copyright © 2022 All rights reserved