Enhancement of ANN-based wind power forecasting by modification of surface roughness parameterization over complex terrain
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View abstract on PubMed
Summary
This summary is machine-generated.Improving wind power forecasts for complex terrains is crucial for renewable energy. This study enhanced wind energy prediction by refining atmospheric models and using machine learning, boosting accuracy by 14%.
Area Of Science
- Atmospheric Science
- Renewable Energy Systems
- Computational Meteorology
Background
- Accurate wind energy resource assessment and forecasting are vital for integrating wind power into the grid, particularly for onshore wind farms situated in complex terrains.
- Challenges in forecasting arise from the intricate interactions between surface heterogeneities and turbulent atmospheric flows within the planetary boundary layer.
- Existing numerical weather prediction (NWP) models often require enhanced surface roughness parameterizations to accurately capture wind dynamics over varied landscapes.
Purpose Of The Study
- To enhance the assessment of wind energy resources and improve medium-term wind power forecasts for complex hilly terrain.
- To investigate the impact of incorporating realistic surface roughness effects into NWP models for better wind speed prediction.
- To evaluate the effectiveness of combining NWP model outputs with machine learning techniques for day-ahead wind power forecasting.
Main Methods
- Utilized a numerical weather prediction (NWP) model with advanced parameterizations for subgrid-scale topography, roughness sublayer, and canopy height.
- Incorporated machine learning, specifically an artificial neural network (ANN), to process NWP model outputs.
- Validated model performance against observed hub-height wind speed data from 24 wind turbines in onshore wind farms.
Main Results
- The NWP model successfully reproduced observed wind speed distributions, durations, and spatio-temporal variabilities when enhanced surface roughness effects were included.
- The study identified beneficial features within the atmospheric model for machine learning applications, representing surface roughness heterogeneities.
- Combining NWP model output with an ANN improved day-ahead wind power forecasts by 14% in annual normalized mean absolute error.
Conclusions
- Improved parameterization of surface friction in atmospheric models is critical for accurate wind power forecasting and resource assessment.
- The integration of NWP models with machine learning techniques offers a significant advancement for onshore wind power forecasting, especially in complex mountainous regions.
- This research provides valuable insights for optimizing wind energy assessment and prediction in challenging geographical areas.
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