Comprehensive study of the artificial intelligence applied in renewable energy

Energy Strategy Reviews, Journal Year: 2024, Volume and Issue: 54, P. 101446 - 101446

Published: June 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.

Language: Английский

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Forecasting Solar Photovoltaic Power Production: A Comprehensive Review and Innovative Data-Driven Modeling Framework

Energies, Journal Year: 2024, Volume and Issue: 17(16), P. 4145 - 4145

Published: Aug. 20, 2024

The intermittent and stochastic nature of Renewable Energy Sources (RESs) necessitates accurate power production prediction for effective scheduling grid management. This paper presents a comprehensive review conducted with reference to pioneering, comprehensive, data-driven framework proposed solar Photovoltaic (PV) generation prediction. systematic integrating comprises three main phases carried out by seven modules addressing numerous practical difficulties the task: phase I handles aspects related data acquisition (module 1) manipulation 2) in preparation development scheme; II tackles associated model 3) assessment its accuracy 4), including quantification uncertainty 5); III evolves towards enhancing incorporating context change detection 6) incremental learning when new become available 7). adeptly addresses all facets PV prediction, bridging existing gaps offering solution inherent challenges. By seamlessly these elements, our approach stands as robust versatile tool precision real-world applications.

Language: Английский

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Deep Learning and Machine Learning in Hydrological Processes Climate Change and Earth Systems a Systematic Review

Lecture notes in networks and systems, Journal Year: 2020, Volume and Issue: unknown, P. 52 - 62

Published: Jan. 1, 2020

Language: Английский

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An Effective Hybrid NARX-LSTM Model for Point and Interval PV Power Forecasting

IEEE Access, Journal Year: 2021, Volume and Issue: 9, P. 36571 - 36588

Published: Jan. 1, 2021

This paper proposes an effective Photovoltaic (PV) Power Forecasting (PVPF) technique based on hierarchical learning combining Nonlinear Auto-Regressive Neural Networks with exogenous input (NARXNN) Long Short-Term Memory (LSTM) model. First, the NARXNN model acquires data to generate a residual error vector. Then, stacked LSTM model, optimized by Tabu search algorithm, uses correction associated original produce point and interval PVPF. The performance of proposed PVPF was investigated using two real datasets different scales locations. comparative analysis NARX-LSTM twelve existing benchmarks confirms its superiority in terms accuracy measures. In summary, has following major achievements: 1) Improves prediction models; 2) Evaluates uncertainties forecasts high accuracy; 3) Provides generalization capability for PV systems scales. Numerical results comparison method real-world Australia USA demonstrate improved accuracy, outperforming benchmark approaches overall normalized Rooted Mean Squared Error (nRMSE) 1.98% 1.33% respectively.

Language: Английский

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Ultra-short-term exogenous forecasting of photovoltaic power production using genetically optimized non-linear auto-regressive recurrent neural networks

Renewable Energy, Journal Year: 2021, Volume and Issue: 171, P. 191 - 209

Published: Feb. 21, 2021

Language: Английский

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