Deep learning-enabled integration of renewable energy sources through photovoltaics in buildings

Munusamy Arun; Thanh Tuan Le; Debabrata Barik; Prabhakar Sharma; Sameh M. Osman; Van Kiet Huynh; Jerzy Kowalski; Van Huong Dong; Viet Vinh Le

doi:10.1016/j.csite.2024.105115

Deep learning-enabled integration of renewable energy sources through photovoltaics in buildings

Abstrakt

Installing photovoltaic (PV) systems in buildings is one of the most effective strategies for achieving sustainable energy goals and reducing carbon emissions. However, the requirement for efficient energy management, the fluctuating energy demands, and the intermittent nature of solar power are a few of the obstacles to the seamless integration of PV systems into buildings. These complexities surpass the capabilities of rule-based systems, necessitating innovative solutions. The research proposes a deep learning-based optimal energy management system designed specifically for home micro-grids that incorporate PV systems with battery energy storage, Enhanced Long Short-Term Memory (LSTM)-Based Optimal Home Micro-Grid Energy Management (OHM-GEM). Integrating an improved type of LSTM neural network called LSTM into the energy management system improves the reliability of PV power output predictions. The dependability of PV power production forecasts is increased by including a refined version of the LSTM neural network in the energy management system. The efficiency of the OHM-GEM system in maximizing PV system integration into buildings is shown by the authors using simulated data. With considerable gains in energy efficiency, cost savings, and decreased reliance on non-renewable energy sources, the results highlight the possibility of this approach to forward sustainable energy practices.

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Autorzy (9)

Munusamy Arun
Thanh Tuan Le
Debabrata Barik
Prabhakar Sharma
Sameh M. Osman
Van Kiet Huynh
Jerzy Kowalski dr hab. inż.
Van Huong Dong
Viet Vinh Le

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Słowa kluczowe

DEEP LEARNING RENEWABLE ENERGY SOURCES PHOTOVOLTAICS BUILDINGS LONG SHORT-TERM MEMORY MICRO-GRIDS

Informacje szczegółowe

Kategoria:

Publikacja w czasopiśmie

Typ:

artykuły w czasopismach

Opublikowano w:

Case Studies in Thermal Engineering nr 61,
ISSN: 2214-157X

Język:

angielski

Rok wydania:

2024

Opis bibliograficzny:

Arun M., Le T. T., Barik D., Sharma P., Osman S. M., Huynh V. K., Kowalski J., Dong V. H., Le V. V.: Deep learning-enabled integration of renewable energy sources through photovoltaics in buildings// Case Studies in Thermal Engineering -Vol. 61, (2024), s.105115-

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