COA-CNN-LSTM: Coati optimization algorithm-based hybrid deep learning model for PV/wind power forecasting in smart grid applications

Applied Energy, Journal Year: 2023, Volume and Issue: 349, P. 121638 - 121638

Published: July 27, 2023

Language: Английский

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State-of-the-art review on energy and load forecasting in microgrids using artificial neural networks, machine learning, and deep learning techniques

Electric Power Systems Research, Journal Year: 2023, Volume and Issue: 225, P. 109792 - 109792

Published: Sept. 8, 2023

Language: Английский

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The usage of 10-fold cross-validation and grid search to enhance ML methods performance in solar farm power generation prediction

Cleaner Engineering and Technology, Journal Year: 2023, Volume and Issue: 15, P. 100664 - 100664

Published: July 28, 2023

It is essential to have accurate projections of the quantity solar energy that will be generated in future improve competitiveness power plants market and reduce dependence both economy society on fossil fuels. This can accomplished by having a better understanding amount future. We used databases containing information about California span 2019 through 2021. These years encompass state's forecast. data were analysis. The 10-fold cross-validation Grid search has been enhance performance decision tree, light gradient boosting machine, an extra tree Solar Farm Power Generation Prediction.

Language: Английский

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Achieving wind power and photovoltaic power prediction: An intelligent prediction system based on a deep learning approach

Energy, Journal Year: 2023, Volume and Issue: 283, P. 129005 - 129005

Published: Sept. 9, 2023

Language: Английский

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Solar and wind power data from the Chinese State Grid Renewable Energy Generation Forecasting Competition

Scientific Data, Journal Year: 2022, Volume and Issue: 9(1)

Published: Sept. 21, 2022

Accurate solar and wind generation forecasting along with high renewable energy penetration in power grids throughout the world are crucial to days-ahead scheduling of systems. It is difficult precisely forecast on-site due intermittency fluctuation characteristics energy. Solar data from sources beneficial for development data-driven models. In this paper, an open dataset consisting collected stations, including six farms eight stations China, provided. Over two years (2019-2020), weather-related were at 15-minute intervals. The was used Renewable Energy Generation Forecasting Competition hosted by Chinese State Grid 2021. process collection, processing, potential applications described. use promising models optimization electricity demand response (DR) programs grid.

Language: Английский

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A novel interval forecasting system based on multi-objective optimization and hybrid data reconstruct strategy

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 217, P. 119539 - 119539

Published: Jan. 12, 2023

Language: Английский

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Simultaneous sizing and scheduling optimization for PV-wind-battery hybrid systems with a modified battery lifetime model: A high-resolution analysis in China

Applied Energy, Journal Year: 2024, Volume and Issue: 360, P. 122812 - 122812

Published: Feb. 10, 2024

Language: Английский

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Combined electricity load-forecasting system based on weighted fuzzy time series and deep neural networks

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 132, P. 108375 - 108375

Published: April 16, 2024

Language: Английский

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Hybrid prediction method for solar photovoltaic power generation using normal cloud parrot optimization algorithm integrated with extreme learning machine

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 22, 2025

As the energy crisis environmental concerns rise, harnessing renewable sources like photovoltaics (PV) is critical for sustainable development. However, seasonal variability and random intermittency of solar power pose significant forecasting challenges, threatening grid stability. Therefore, this paper proposes a novel hybrid method, NCPO-ELM, to adequately capture spatial temporal dependencies within meteorological data crucial accurate predictions. To effectively optimize performance Extreme Learning Machine (ELM), Normal Cloud Parrot Optimization (NCPO) algorithm developed, inspired by Pyrrhura Molinae parrots' flock behavior cloud model theory. NCPO integrates five unique search strategies utilizes structure explore exploit. By introducing normal generate samples with specific distributions, enhances solution space coverage. subsequently employed Single-Layer Feedforward Network (SLFN) hidden layer hyperparameters, yielding optimal weights biases output layer, thereby reducing benchmark ELM's sensitivity noise instability from initialization. The actual results PV stations across different regions demonstrate that proposed NCPO-ELM shows superior prediction accuracy compared existing approaches, particularly time series diverse characteristics variations.

Language: Английский

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Review of Strategies to Mitigate Dust Deposition on Solar Photovoltaic Systems

Energies, Journal Year: 2022, Volume and Issue: 16(1), P. 109 - 109

Published: Dec. 22, 2022

In recent years, there has been an increased focus on developing and utilizing renewable energy resources due to several factors, including environmental concerns, rising fuel costs, the limited supply of conventional fossil fuels. The most appealing green conversion technology is solar energy, its efficient application can help world achieve Sustainable Development Goal 7: Access affordable, clean energy. Irradiance, latitude, longitude, tilt angle, orientation are a few variables that affect functioning photovoltaic (PV) system. Additionally, factors like dust accumulation soiling panel surfaces impact cost maintaining producing electricity from PV Dust characteristics (kind, size, shape, meteorological elements), one largest affecting performance, need be investigated devise specific solutions for efficiently harnessing essential findings ongoing investigations deposition surface structures various mitigating measures tackle issues presented in this review study. This comprehensive assessment critically evaluates current research effect system performance improvement techniques determine academic community’s future priorities.

Language: Английский

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