Improved multistep ahead photovoltaic power prediction model based on LSTM and self-attention with weather forecast data

Applied Energy, Год журнала: 2024, Номер 359, С. 122709 - 122709

Опубликована: Фев. 2, 2024

Язык: Английский

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Elman neural network considering dynamic time delay estimation for short-term forecasting of offshore wind power

Applied Energy, Год журнала: 2024, Номер 358, С. 122671 - 122671

Опубликована: Янв. 21, 2024

Язык: Английский

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Short-term PV power prediction based on meteorological similarity days and SSA-BiLSTM

Systems and Soft Computing, Год журнала: 2024, Номер 6, С. 200084 - 200084

Опубликована: Фев. 23, 2024

Accurate short-term photovoltaic (PV) power forecasting can reduce the un- certainty of PV generation, which is crucial for grid operation as well energy dispatch. Considering influence seasonal and meteorological factors on prediction, a predic- tion method based similarity day sparrow search algo- rithm bi-directional long memory network combination (SSA-BiLSTM) proposed. Firstly, correlation between generation calculated by using Pearson coefficients, getting strongly correlated affecting generation; afterwards,the historical data are clustered fuzzy C-means clustering to achieve similar clustering; then, best selected from according test features data, Forming training set with original BiLSTM network. SSA algorithm was used find optimal parameters. Finally, Using parameters construct prediction. The experiments were conducted plant in Xinjiang, also compared existing prediction algorithms.The results show that accuracy different weather conditions 33.1 %, 31.9 % 24.1 higher than under same intelligent optimization neural networks, 27.9 24.7 18.0 algorithms Therefore, this paper has better seasons conditions.

Язык: Английский

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Short-term integrated forecasting method for wind power, solar power, and system load based on variable attention mechanism and multi-task learning

Energy, Год журнала: 2024, Номер 304, С. 132188 - 132188

Опубликована: Июнь 24, 2024

Язык: Английский

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Advancing smart net-zero energy buildings with renewable energy and electrical energy storage

Journal of Energy Storage, Год журнала: 2025, Номер 114, С. 115850 - 115850

Опубликована: Фев. 22, 2025

Язык: Английский

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Towards energy efficiency: A comprehensive review of deep learning-based photovoltaic power forecasting strategies

Heliyon, Год журнала: 2024, Номер 10(13), С. e33419 - e33419

Опубликована: Июнь 27, 2024

Язык: Английский

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Operational day-ahead photovoltaic power forecasting based on transformer variant

Applied Energy, Год журнала: 2024, Номер 373, С. 123825 - 123825

Опубликована: Июль 10, 2024

Язык: Английский

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Leveraging machine learning algorithms for improved disaster preparedness and response through accurate weather pattern and natural disaster prediction

Frontiers in Environmental Science, Год журнала: 2023, Номер 11

Опубликована: Ноя. 2, 2023

Globally, communities and governments face growing challenges from an increase in natural disasters worsening weather extremes. Precision disaster preparation is crucial responding to these issues. The revolutionary influence that machine learning algorithms have strengthening catastrophe response systems thoroughly explored this paper. Beyond a basic summary, the findings of our study are striking demonstrate sophisticated powers forecasting variety patterns anticipating range catastrophes, including heat waves, droughts, floods, hurricanes, more. We get practical insights into complexities applications, which support enhanced effectiveness predictive models preparedness. paper not only explains theoretical foundations but also presents proof significant benefits provide. As result, results open door for governments, businesses, people make wise decisions. These accurate predictions catastrophes emerging may be used implement pre-emptive actions, eventually saving lives reducing severity damage.

Язык: Английский

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A hybrid PV cluster power prediction model using BLS with GMCC and error correction via RVM considering an improved statistical upscaling technique

Applied Energy, Год журнала: 2024, Номер 359, С. 122719 - 122719

Опубликована: Янв. 31, 2024

Язык: Английский

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Artificial Intelligence Techniques for the Photovoltaic System: A Systematic Review and Analysis for Evaluation and Benchmarking

Archives of Computational Methods in Engineering, Год журнала: 2024, Номер unknown

Опубликована: Май 8, 2024

Abstract Novel algorithms and techniques are being developed for design, forecasting maintenance in photovoltaic due to high computational costs volume of data. Machine Learning, artificial intelligence provide automated, intelligent history-based solutions complex scenarios. This paper aims identify through a systematic review analysis the role systems control. The main novelty this work is exploration methodological insights three different ways. first approach investigate applicability systems. second study data operations, failure predictors, assessment, safety response, installation issues, monitoring etc. All these factors discussed along with results after applying on systems, exploring challenges limitations considering wide variety latest related manuscripts.

Язык: Английский

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