Rapid evaluation of micro-scale photovoltaic solar energy systems using empirical methods combined with deep learning neural networks to support systems' manufacturers

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Almeshaiei, E, Al-Habaibeh, A ORCID: https://orcid.org/0000-0002-9867-6011 and Shakmak, B ORCID: https://orcid.org/0000-0003-4534-9196, 2019. Rapid evaluation of micro-scale photovoltaic solar energy systems using empirical methods combined with deep learning neural networks to support systems' manufacturers. Journal of Cleaner Production. ISSN 0959-6526

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Official URL: https://doi.org/10.1016/j.jclepro.2019.118788

Abstract

Solar energy is becoming one of the most attractive renewable sources. In many cases, due to a wide range of financial or installation limitations, off-grid small scale micro power panels are favoured as modular systems to power lighting in gardens or to be integrated together to power small devices such as mobile phone chargers and distributed smart city facilities and services. Manufacturers and systems' integrators have a wide range of options of micro-scale photo voltaic panels to choose from. This makes the selection of the right panel a challenging task and risky investment. To address this and to help manufacturers, this paper suggests and evaluates a novel approach based on integrating empirical lab-testing with short-term real data and neural networks to assess the performance of micro-scale photovoltaic panels and their suitability for a specific application in specific environment. The paper outlines the combination of lab testing power output under seasonal and hourly conditions during the year combined with environmental and operating conditions such as temperature, dust accumulation and tilt angle performance. Based on the lab results, a short in-situ experimental work is implemented and the performance over the year in the selected location in Kuwait is evaluated using deep learning neural networks. The findings of this approach are compared with simulation and long-term real data. The results show a maximum error of 23% of the neural network output when compared with the actual data, and a correlation values with previous work within 87.3% and 91.9% which indicate that the proposed approach could provide an experimental rapid and accurate assessment of the expected power output. Hence, supporting the rapid decision-making process for manufacturers and reducing investment risks.

Item Type:	Journal article
Publication Title:	Journal of Cleaner Production
Creators:	Almeshaiei, E., Al-Habaibeh, A. and Shakmak, B.
Publisher:	Elsevier
Date:	11 October 2019
ISSN:	0959-6526
Identifiers:	Number Type 10.1016/j.jclepro.2019.118788 DOI 1201065 Other
Divisions:	Schools > School of Architecture, Design and the Built Environment
Record created by:	Linda Sullivan
Date Added:	15 Oct 2019 10:10
Last Modified:	31 May 2021 15:15
URI:	https://irep.ntu.ac.uk/id/eprint/37963