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

R Kabilan,V Chandran,Priyesh P Gandhi,Robbi Rahim,V Mohanavel,Alagar Karthick,J Yogapriya,S Manoharan,Jurgen Hupkes

doi:10.1155/2021/5582418

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.

Highlights

Economic growth has given rise to increasing global demand for electrical energy production and consumption
State-of-the-art solar power technology will only be established if forecasters can reliably predict how much solar power will be available at a specific place at a particular time
According to the nature of creation, the machine learning models are developed with three training, validation, and test sets

Summary

Introduction

Economic growth has given rise to increasing global demand for electrical energy production and consumption. Solar power plants are very common in renewable energy sources [1,2,3,4]. In addition to being installed on the roof of the building, PV modules will act as stand-alone solar power generators [5–. The installation of photovoltaic panels has increased every year in recent years. 117 gigawatts of solar PV energy are generated in 2019 [8]. Traditional grid-based power distribution operates on stable power supply lines and a consistent load [9]. Solar PV power could interfere with conventional power generation, making conventional generation uncomfortable or even unworkable [10,11,12,13,14]

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Discussion

Conclusion