Energy consumption and carbon emissions forecasting for industrial processes: Status, challenges and perspectives

Renewable and Sustainable Energy Reviews, Journal Year: 2023, Volume and Issue: 182, P. 113405 - 113405

Published: May 25, 2023

Language: Английский

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A review of the applications of artificial intelligence in renewable energy systems: An approach-based study

Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 306, P. 118207 - 118207

Published: March 16, 2024

Language: Английский

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Prediction of wind and PV power by fusing the multi-stage feature extraction and a PSO-BiLSTM model

Energy, Journal Year: 2024, Volume and Issue: 298, P. 131345 - 131345

Published: April 17, 2024

Language: Английский

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Peer-to-peer energy trading optimization for community prosumers considering carbon cap-and-trade

Applied Energy, Journal Year: 2024, Volume and Issue: 358, P. 122611 - 122611

Published: Jan. 11, 2024

Language: Английский

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A cohesive structure of Bi-directional long-short-term memory (BiLSTM) -GRU for predicting hourly solar radiation

Renewable Energy, Journal Year: 2024, Volume and Issue: 222, P. 119943 - 119943

Published: Jan. 2, 2024

Language: Английский

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Data-driven energy management system for flexible operation of hydrogen/ammonia-based energy hub: A deep reinforcement learning approach

Energy Conversion and Management, Journal Year: 2023, Volume and Issue: 291, P. 117323 - 117323

Published: June 24, 2023

Language: Английский

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Enhancement of LSTM models based on data pre-processing and optimization of Bayesian hyperparameters for day-ahead photovoltaic generation prediction

Computers & Electrical Engineering, Journal Year: 2024, Volume and Issue: 116, P. 109162 - 109162

Published: March 7, 2024

Language: Английский

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Harnessing AI for solar energy: Emergence of transformer models

Applied Energy, Journal Year: 2024, Volume and Issue: 369, P. 123541 - 123541

Published: June 1, 2024

Language: Английский

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BO-STA-LSTM: Building energy prediction based on a Bayesian Optimized Spatial-Temporal Attention enhanced LSTM method

Developments in the Built Environment, Journal Year: 2024, Volume and Issue: 18, P. 100465 - 100465

Published: April 1, 2024

In predicting building energy (affected by seasons), there are issues like inefficient hyperparameter optimization and inaccurate predictions, it is unclear whether spatial temporal attention improves performance. This study proposes a method based on Bayesian Optimization (BO), Spatial-Temporal Attention (STA), Long Short-Term Memory (LSTM). Seven improved LSTM models (BO-LSTM, SA-LSTM, TA-LSTM, STA-LSTM, BO-SA-LSTM, BO-TA-LSTM, BO-STA-LSTM) compared with the impacts of seasonal variations BO-STA-LSTM analysed using different sample types time domain analysis. To further demonstrate efficiency proposed method, comparisons convolutional neural network (CNN) (TCN) performed, followed validation new datasets. The findings indicate that adding STA BO to enhances average prediction performance 0.0885. alone contributes 0.0717, while 0.0560. achieves higher accuracy for similar test training samples or size 14016, effectively capturing seasonal, trend, peak patterns. Additionally, outperforms CNN TCN, demonstrating superior accuracy.

Language: Английский

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Enhancing solar irradiance prediction for sustainable energy solutions employing a hybrid machine learning model; improving hydrogen production through Photoelectrochemical device

Applied Energy, Journal Year: 2025, Volume and Issue: 382, P. 125280 - 125280

Published: Jan. 13, 2025

Language: Английский

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