A novel DWTimesNet-based short-term multi-step wind power forecasting model using feature selection and auto-tuning methods

Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 301, P. 118045 - 118045

Published: Jan. 5, 2024

Language: Английский

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A Review of Modern Wind Power Generation Forecasting Technologies

Sustainability, Journal Year: 2023, Volume and Issue: 15(14), P. 10757 - 10757

Published: July 8, 2023

The prediction of wind power output is part the basic work grid dispatching and energy distribution. At present, mainly obtained by fitting regressing historical data. medium- long-term results exhibit large deviations due to uncertainty generation. In order meet demand for accessing large-scale into electricity further improve accuracy short-term prediction, it necessary develop models accurate precise based on advanced algorithms studying a generation system. This paper summarizes contribution current forecasting technology delineates key advantages disadvantages various models. These have different capabilities, update weights each model in real time, comprehensive capability model, good application prospects forecasting. Furthermore, case studies examples literature accurately predicting ultra-short-term with randomness are reviewed analyzed. Finally, we present future that can serve as useful directions other researchers planning conduct similar experiments investigations.

Language: Английский

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Minutely multi-step irradiance forecasting based on all-sky images using LSTM-InformerStack hybrid model with dual feature enhancement

Renewable Energy, Journal Year: 2024, Volume and Issue: 224, P. 120135 - 120135

Published: Feb. 12, 2024

Language: Английский

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An intelligent hybrid approach for photovoltaic power forecasting using enhanced chaos game optimization algorithm and Locality sensitive hashing based Informer model

Journal of Building Engineering, Journal Year: 2023, Volume and Issue: 78, P. 107635 - 107635

Published: Aug. 21, 2023

Language: Английский

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A hybrid methodology using VMD and disentangled features for wind speed forecasting

Energy, Journal Year: 2023, Volume and Issue: 288, P. 129824 - 129824

Published: Nov. 27, 2023

Language: Английский

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A unified deep learning framework for water quality prediction based on time-frequency feature extraction and data feature enhancement

Journal of Environmental Management, Journal Year: 2023, Volume and Issue: 351, P. 119894 - 119894

Published: Dec. 27, 2023

Language: Английский

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Hybrid LSTM-Based Fractional-Order Neural Network for Jeju Island’s Wind Farm Power Forecasting

Fractal and Fractional, Journal Year: 2024, Volume and Issue: 8(3), P. 149 - 149

Published: March 5, 2024

Efficient integration of wind energy requires accurate power forecasting. This prediction is critical in optimising grid operation, trading, and effectively harnessing renewable resources. However, the wind’s complex variable nature poses considerable challenges to achieving forecasts. In this context, accuracy parameter forecasts, including speed direction, essential enhancing precision predictions. The presence missing data these parameters further complicates forecasting process. These values could result from sensor malfunctions, communication issues, or other technical constraints. Addressing issue ensuring reliability predictions stability grid. paper proposes a long short-term memory (LSTM) model forecast direction tackle issues. A fractional-order neural network (FONN) with fractional arctan activation function also developed enhance generated prediction. predictive efficacy FONN demonstrated through two comprehensive case studies. first case, are used, while second used for predicting power. proposed hybrid improves addresses gaps. model’s performance measured using mean errors R2 values.

Language: Английский

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Improving ultra-short-term photovoltaic power forecasting using advanced deep-learning approach

Measurement, Journal Year: 2024, Volume and Issue: 239, P. 115405 - 115405

Published: July 27, 2024

Language: Английский

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NSGA-II based short-term building energy management using optimal LSTM-MLP forecasts

International Journal of Electrical Power & Energy Systems, Journal Year: 2024, Volume and Issue: 159, P. 110070 - 110070

Published: June 3, 2024

To conduct analysis on the field of electricity management in buildings is crucial to contribute clean energy promotion, efficiency, and resilience against climate change. This manuscript proposes a methodology for modeling predictive calibrated system (EMS) using hybrid that combines long short-term memory multilayer perceptron models (LSTM-MLP) optimized by non-dominated sorting genetic algorithm II (NSGA-II). The proposed approach utilizes global forecast (GFS) data anticipate consumption fluctuations optimize use distributed sources, such as photovoltaic (PV) production, based knowledge prices free market one day ahead. trade-off building conducted with NSGA-II, guaranteeing exploration exploitation while minimizing costs wastes. research carried out demonstrates effectiveness LSTM-MLP model advantages NSGA-II hyperparameter tuning balance sustainable practices. tested an existing building, Industrial Engineering School located Campus Lagoas-Marcosende Universidade de Vigo, Spain.

Language: Английский

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A novel multi-step ahead solar power prediction scheme by deep learning on transformer structure

Renewable Energy, Journal Year: 2024, Volume and Issue: 230, P. 120780 - 120780

Published: June 12, 2024

Language: Английский

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