Performance Prediction of Building Integrated Photovoltaic System Using Hybrid Deep Learning Algorithm

In a grid-connected photovoltaic system, forecasting is a necessary and critical step. Solar Power is very nonlinear; this article develops and analyses building integrated photovoltaic (BIPV) forecasting algorithms for different timeframes, such as an hour, a day, and a week ahead, to manage grid o...

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Main Authors: Manivannan Ragupathi, Rengaraj Ramasubbu
Format: Article
Language:English
Published: Wiley 2022-01-01
Series:International Journal of Photoenergy
Online Access:http://dx.doi.org/10.1155/2022/6111030
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author Manivannan Ragupathi
Rengaraj Ramasubbu
author_facet Manivannan Ragupathi
Rengaraj Ramasubbu
author_sort Manivannan Ragupathi
collection DOAJ
description In a grid-connected photovoltaic system, forecasting is a necessary and critical step. Solar Power is very nonlinear; this article develops and analyses building integrated photovoltaic (BIPV) forecasting algorithms for different timeframes, such as an hour, a day, and a week ahead, to manage grid operation effectively. However, a model built for a certain time scale may improve performance at that time scale but cannot be utilized to make predictions at other time scales. Here, we demonstrate how to use the multitask learning algorithm to create a multitime scale model for solar BIPV forecasting. Effective resource distribution across several tasks is shown. The suggested multitask learning approach is implemented using LSTM neural networks and evaluated over a range of horizons. We employed a modified version of the Chicken Swarm Optimizer (CSO) that takes the best features of the CSO and the GWO algorithms and merges them into one efficient approach to estimate the hyperparameters of the proposed LSTM model. The proposed approach consistently outperformed state-of-the-art single-timescale forecasting algorithms across all time scales.
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institution Kabale University
issn 1687-529X
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publishDate 2022-01-01
publisher Wiley
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series International Journal of Photoenergy
spelling doaj-art-3d7dc8be107a404eb1f598427bd12b9f2025-02-03T01:00:43ZengWileyInternational Journal of Photoenergy1687-529X2022-01-01202210.1155/2022/6111030Performance Prediction of Building Integrated Photovoltaic System Using Hybrid Deep Learning AlgorithmManivannan Ragupathi0Rengaraj Ramasubbu1Department of Electrical and Electronics EngineeringDepartment of Electrical and Electronics EngineeringIn a grid-connected photovoltaic system, forecasting is a necessary and critical step. Solar Power is very nonlinear; this article develops and analyses building integrated photovoltaic (BIPV) forecasting algorithms for different timeframes, such as an hour, a day, and a week ahead, to manage grid operation effectively. However, a model built for a certain time scale may improve performance at that time scale but cannot be utilized to make predictions at other time scales. Here, we demonstrate how to use the multitask learning algorithm to create a multitime scale model for solar BIPV forecasting. Effective resource distribution across several tasks is shown. The suggested multitask learning approach is implemented using LSTM neural networks and evaluated over a range of horizons. We employed a modified version of the Chicken Swarm Optimizer (CSO) that takes the best features of the CSO and the GWO algorithms and merges them into one efficient approach to estimate the hyperparameters of the proposed LSTM model. The proposed approach consistently outperformed state-of-the-art single-timescale forecasting algorithms across all time scales.http://dx.doi.org/10.1155/2022/6111030
spellingShingle Manivannan Ragupathi
Rengaraj Ramasubbu
Performance Prediction of Building Integrated Photovoltaic System Using Hybrid Deep Learning Algorithm
International Journal of Photoenergy
title Performance Prediction of Building Integrated Photovoltaic System Using Hybrid Deep Learning Algorithm
title_full Performance Prediction of Building Integrated Photovoltaic System Using Hybrid Deep Learning Algorithm
title_fullStr Performance Prediction of Building Integrated Photovoltaic System Using Hybrid Deep Learning Algorithm
title_full_unstemmed Performance Prediction of Building Integrated Photovoltaic System Using Hybrid Deep Learning Algorithm
title_short Performance Prediction of Building Integrated Photovoltaic System Using Hybrid Deep Learning Algorithm
title_sort performance prediction of building integrated photovoltaic system using hybrid deep learning algorithm
url http://dx.doi.org/10.1155/2022/6111030
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AT rengarajramasubbu performancepredictionofbuildingintegratedphotovoltaicsystemusinghybriddeeplearningalgorithm