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Article

LSTM Networks for Overcoming the Challenges Associated with Photovoltaic Module Maintenance in Smart Cities

1
BISITE Research Group, Edificio Multiusos I+D+i, University of Salamanca, 37007 Salamanca, Spain
2
Department of Electronics, Information and Communication, Faculty of Engineering, Osaka Institute of Technology, Osaka 535-8585, Japan
3
Pusat Komputeran dan Informatik, Universiti Malaysia Kelantan, Karung Berkunci 36, Pengkaan Chepa, Kota Bharu 16100, Kelantan, Malaysia
*
Authors to whom correspondence should be addressed.
Electronics 2021, 10(1), 78; https://doi.org/10.3390/electronics10010078
Received: 9 December 2020 / Revised: 30 December 2020 / Accepted: 31 December 2020 / Published: 4 January 2021
(This article belongs to the Special Issue Data Analytics Challenges in Smart Cities Applications)
Predictive maintenance is a field of research that has emerged from the need to improve the systems in place. This research focuses on controlling the degradation of photovoltaic (PV) modules in outdoor solar panels, which are exposed to a variety of climatic loads. Improved reliability, operation, and performance can be achieved through monitoring. In this study, a system capable of predicting the output power of a solar module was implemented. It monitors different parameters and uses automatic learning techniques for prediction. Its use improved reliability, operation, and performance. On the other hand, automatic learning algorithms were evaluated with different metrics in order to optimize and find the best configuration that provides an optimal solution to the problem. With the aim of increasing the share of renewable energy penetration, an architectural proposal based on Edge Computing was included to implement the proposed model into a system. The proposed model is designated for outdoor predictions and offers many advantages, such as monitoring of individual panels, optimization of system response, and speed of communication with the Cloud. The final objective of the work was to contribute to the smart Energy system concept, providing solutions for planning the entire energy system together with the identification of suitable energy infrastructure designs and operational strategies. View Full-Text
Keywords: predictive maintenance; LSTM networks; solar panels; PV degradation; edge computing; smart renewable energy predictive maintenance; LSTM networks; solar panels; PV degradation; edge computing; smart renewable energy
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MDPI and ACS Style

Vicente-Gabriel, J.; Gil-González, A.-B.; Luis-Reboredo, A.; Chamoso, P.; Corchado, J.M. LSTM Networks for Overcoming the Challenges Associated with Photovoltaic Module Maintenance in Smart Cities. Electronics 2021, 10, 78. https://doi.org/10.3390/electronics10010078

AMA Style

Vicente-Gabriel J, Gil-González A-B, Luis-Reboredo A, Chamoso P, Corchado JM. LSTM Networks for Overcoming the Challenges Associated with Photovoltaic Module Maintenance in Smart Cities. Electronics. 2021; 10(1):78. https://doi.org/10.3390/electronics10010078

Chicago/Turabian Style

Vicente-Gabriel, Jorge, Ana-Belén Gil-González, Ana Luis-Reboredo, Pablo Chamoso, and Juan M. Corchado. 2021. "LSTM Networks for Overcoming the Challenges Associated with Photovoltaic Module Maintenance in Smart Cities" Electronics 10, no. 1: 78. https://doi.org/10.3390/electronics10010078

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