Comprehensive study of the artificial intelligence applied in renewable energy

Energy Strategy Reviews, Год журнала: 2024, Номер 54, С. 101446 - 101446

Опубликована: Июнь 4, 2024

In the innovative domain of sustainable and renewable energy, artificial intelligence incorporation has appeared as a critical stimulant for improving productivity, cutting costs, addressing complex difficulties. However, all reported advancement over recent years, their experimental implementations, challenges associated have not been covered by single source. Hence, this review aims to give data source get recent, advanced detailed outlook on applications in energy technologies systems along with examples implementation. More than 150 research reports were retrieved from different bases keywords selection criteria maintain relevance. This specifically explored diverse approaches wide range sources innovations spanning solar power, photovoltaics, microgrid integration, storage power management, wind, geothermal comprehensively. The current technological advances, outcomes, case studies implications are discussed, potential possible solutions. expected advancements trends near future also discussed which can gateway researchers, investigators engineers look resolve already associated.

Язык: Английский

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Artificial Neural Networks for Photovoltaic Power Forecasting: A Review of Five Promising Models

IEEE Access, Год журнала: 2024, Номер 12, С. 90461 - 90485

Опубликована: Янв. 1, 2024

Solar energy is largely dependent on weather conditions, resulting in unpredictable, fluctuating, and unstable photovoltaic (PV) power outputs. Thus, accurate PV forecasts are increasingly crucial for managing controlling integrated systems. Over the years, advanced artificial neural network (ANN) models have been proposed to increase accuracy of various geographical regions. Hence, this paper provides a state-of-the-art review five most popular ANN forecasting. These include multilayer perceptron (MLP), recurrent (RNN), long short-term memory (LSTM), gated unit (GRU), convolutional (CNN). First, internal structure operation these studied. It then followed by brief discussion main factors affecting their forecasting accuracy, including horizons, meteorological evaluation metrics. Next, an in-depth separate analysis standalone hybrid provided. has determined that bidirectional GRU LSTM offer greater whether used as model or configuration. Furthermore, upgraded metaheuristic algorithms demonstrated exceptional performance when applied models. Finally, study discusses limitations shortcomings may influence practical implementation

Язык: Английский

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Revolutionizing solar energy resources: The central role of generative AI in elevating system sustainability and efficiency

Applied Energy, Год журнала: 2025, Номер 382, С. 125296 - 125296

Опубликована: Янв. 13, 2025

Язык: Английский

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A multi-objective predictive energy management strategy for residential grid-connected PV-battery hybrid systems based on machine learning technique

Energy Conversion and Management, Год журнала: 2021, Номер 237, С. 114103 - 114103

Опубликована: Апрель 8, 2021

Язык: Английский

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Deep learning in electrical utility industry: A comprehensive review of a decade of research

Engineering Applications of Artificial Intelligence, Год журнала: 2020, Номер 96, С. 104000 - 104000

Опубликована: Окт. 9, 2020

Язык: Английский

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