Short-Term Power Prediction of Building Integrated Photovoltaic (BIPV) System Based on Machine Learning Algorithms

Author:

Kabilan R.1,Chandran V.2ORCID,Yogapriya J.3,Karthick Alagar4ORCID,Gandhi Priyesh P.5ORCID,Mohanavel V.6,Rahim Robbi7,Manoharan S.8ORCID

Affiliation:

1. Department of Electronics and Communication Engineering, Francis Xavier Engineering College, Tirunelveli, 627003 Tamil Nadu, India

2. Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Avinashi Road, Arasur, Coimbatore, 641407 Tamil Nadu, India

3. Department of Computer Science and Engineering, Kongunadu College of Engineering and Technology, Trichy, 621215 Tamil Nadu, India

4. Department of Electrical and Electronics Engineering, KPR Institute of Engineering and Technology, Avinashi Road, Arasur, Coimbatore, 641407 Tamil Nadu, India

5. Department of Electronics and Communication Engineering, Sigma Institute of Engineering, Vadodara, 390019 Gujarat, India

6. Centre for Materials Engineering and Regenerative Medicine, Bharath Institute of Higher Education and Research, Chennai-600073, Tamil Nadu, India

7. Department of Informatics Management, Sekolah Tinggi Ilmu Manajemen Sukma, Medan, Sumatera Utara 20219, Indonesia

8. Department of Computer Science, School of Informatics and Electrical Engineering, Institute of Technology, Ambo University, Ambo, Post Box No.: 19, Ethiopia

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.

Publisher

Hindawi Limited

Subject

General Materials Science,Renewable Energy, Sustainability and the Environment,Atomic and Molecular Physics, and Optics,General Chemistry

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