Advancements in hydrogen production through the integration of renewable energy sources with AI techniques: A comprehensive literature review

Applied Energy, Journal Year: 2025, Volume and Issue: 383, P. 125354 - 125354

Published: Jan. 17, 2025

Language: Английский

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A Comparative Study of AI Methods on Renewable Energy Prediction for Smart Grids: Case of Turkey

Sustainability, Journal Year: 2024, Volume and Issue: 16(7), P. 2894 - 2894

Published: March 30, 2024

Fossil fuels still have emerged as the predominant energy source for power generation on a global scale. In recent years, Turkey has experienced notable decrease in production of coal and natural gas energy, juxtaposed with significant rise renewable sources. The study employed neural networks, ANNs (artificial networks), LSTM (long short-term memory), well CNN (convolutional network) hybrid CNN-LSTM designs, to assess Turkey’s potential. Real-time outcomes were produced by integrating these models meteorological data. objective was design strategies enhancing performance comparing various outcomes. data collected whole are based average values. Machine learning approaches mitigate error rate seen acquired Comparisons conducted across light gradient boosting machine (LightGBM), regressor (GBR), random forest (RF) techniques, which represent models, alongside deep models. Based findings comparative analyses, it determined that model, LightGBM, exhibited most favorable accuracy predictions. Conversely, CNN-LSTM, had greatest inaccuracy. This will serve guide researchers, especially developing countries such not switched smart grid system.

Language: Английский

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Forecasting Solar Energy: Leveraging Artificial Intelligence and Machine Learning for Sustainable Energy Solutions

Journal of Economic Surveys, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 21, 2025

ABSTRACT Integrating solar energy into power grids is essential for advancing a low‐carbon economy, but accurate forecasting remains challenging due to output variability. This study comprehensively reviews models, focusing on how Artificial Intelligence (AI) and Machine Learning (ML) enhance forecast accuracy. It examines the current landscape of forecasting, identifies limitations in existing underscores need more adaptable approaches. The primary goals are analyze evolution AI/ML‐based assess their strengths weaknesses, propose structured methodology selecting implementing AI/ML models tailored forecasting. Through comparative analysis, evaluates individual hybrid across different scenarios, identifying under‐explored research areas. findings indicate significant improvements prediction accuracy through advancements, aiding grid management supporting transition. Ensemble methods, deep learning techniques, show great promise enhancing reliability. Combining diverse approaches with advanced techniques results reliable forecasts. suggests that improving model these integrated methods offers substantial opportunities further research, contributing global sustainability efforts, particularly UN SDGs 7 13, promoting economic growth minimal environmental impact.

Language: Английский

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A review of PV power forecasting using machine learning techniques

Deleted Journal, Journal Year: 2025, Volume and Issue: unknown, P. 100058 - 100058

Published: Jan. 1, 2025

Language: Английский

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Long Short‐Term Memory Wavelet Neural Network for Renewable Energy Generation Forecasting

International Journal of Intelligent Systems, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

Renewable energy forecasting is crucial for pollution prevention, management, and long‐term sustainability. In response to the challenges associated with forecasting, simultaneous deployment of several data‐processing approaches has been used in a variety studies order improve energy–time‐series analysis, finding that, when combined wavelet deep learning techniques can achieve high accuracy applications. Consequently, we investigate implementation various wavelets within structure long short‐term memory neural network (LSTM), resulting new LSTM (LSTMW) network. addition, as an improvement phase, modeled uncertainty incorporated it into forecast so that systemic biases deviations could be accounted (LSTMW luster: LSTMWL). The models were evaluated using data from six renewable power generation plants Chile. When compared other approaches, experimental results show our method provides prediction error acceptable range, achieving coefficient determination ( R 2 ) between 0.73 0.98 across different test scenarios, consistent alignment forecasted observed values, particularly during first 3 steps.

Language: Английский

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Advancing Sustainability Through Machine Learning: Modeling and Forecasting Renewable Energy Consumption

Sustainability, Journal Year: 2025, Volume and Issue: 17(3), P. 1304 - 1304

Published: Feb. 6, 2025

This research provides a thorough examination of the industrial sector’s forecasting renewable energy consumption, utilizing sophisticated machine learning techniques to enhance accuracy and reliability predictions. LASSO regression, random forest (RF), Support Vector Regression (SVR), eXtreme Gradient Boosting (XGBoost 2.1.3), LightGBM, multilayer perceptron (MLP) were all selected due their ability effectively handle large datasets. Our primary goal was demonstrate utility Energy Uncertainty Index (EUI) within commonly accepted models ensure replicability relevance broad audience. The integration EUI as an independent variable is critical innovation this research, it addresses challenges presented by fluctuations in markets. A more nuanced comprehension consumption trends presence uncertainty achieved through inclusion. We evaluate performance these context forecasting, identifying strengths limitations. results indicate that prognostic potential considerably improved inclusion EUI, providing valuable insights for policymakers, investors, industry stakeholders. These advancements emphasize role achieving efficient resource allocation, guiding infrastructure development, minimizing risks, supporting global transition toward sustainability.

Language: Английский

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Navigating the AI-powered transformation of renewable energy supply chains: A strategic roadmap to digitainability

Energy Sustainable Development/Energy for sustainable development, Journal Year: 2025, Volume and Issue: 85, P. 101663 - 101663

Published: Feb. 8, 2025

Language: Английский

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RES Curtailments in Cyprus: A Review of Technical Constraints and Solutions

Solar Energy Advances, Journal Year: 2025, Volume and Issue: unknown, P. 100097 - 100097

Published: Feb. 1, 2025

Language: Английский

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Short-Term Solar Irradiance Forecasting Model Based on Hyper-parameter Tuned LSTM via Chaotic Particle Swarm Optimization Algorithm

Case Studies in Thermal Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 105999 - 105999

Published: March 1, 2025

Language: Английский

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Advancing organic photovoltaic cells for a sustainable future: The role of artificial intelligence (AI) and deep learning (DL) in enhancing performance and innovation

Solar Energy, Journal Year: 2025, Volume and Issue: 291, P. 113378 - 113378

Published: March 6, 2025

Language: Английский

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