Wind Energy Prediction in Highly Complex Terrain by Computational Fluid Dynamics

Tabas, Daniel; Fang, Jiannong; Porté‐Agel, Fernando

doi:10.3390/en12071311

Cited by 23 publications

(15 citation statements)

References 18 publications

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“…For example, turbine 2 is located at the edge of the forest and on the leeward side of the hill Mont-Soleil when wind blows from the predominant sector (south-west). It was shown in [18] that high-fidelity modeling of the recirculating zone and the forest is necessary for accurately predicting the wind field around turbine 2. That is why the baseline model performs the worst at this location.…”

Section: Resultsmentioning

confidence: 99%

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Numerical Weather Prediction and Artificial Neural Network Coupling for Wind Energy Forecast

2021

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In the past two decades, wind energy has been under fast development worldwide. The dramatic increase of wind power penetration in electricity production has posed a big challenge to grid integration due to the high uncertainty of wind power. Accurate real-time forecasts of wind farm power outputs can help to mitigate the problem. Among the various techniques developed for wind power forecasting, the hybridization of numerical weather prediction (NWP) and machine learning (ML) techniques such as artificial neural networks (ANNs) are attracting many researchers world-wide nowadays, because it has the potential to yield more accurate forecasts. In this paper, two hybrid NWP and ANN models for wind power forecasting over a highly complex terrain are proposed. The developed models have a fine temporal resolution and a sufficiently large prediction horizon (>6 h ahead). Model 1 directly forecasts the energy production of each wind turbine. Model 2 forecasts first the wind speed, then converts it to the power using a fitted power curve. Effects of various modeling options (selection of inputs, network structures, etc.) on the model performance are investigated. Performances of different models are evaluated based on four normalized error measures. Statistical results of model predictions are presented with discussions. Python was utilized for task automation and machine learning. The end result is a fully working library for wind power predictions and a set of tools for running the models in forecast mode. It is shown that the proposed models are able to yield accurate wind farm power forecasts at a site with high terrain and flow complexities. Especially, for Model 2, the normalized Mean Absolute Error and Root Mean Squared Error are obtained as 8.76% and 13.03%, respectively, lower than the errors reported by other models in the same category.

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Section: Resultsmentioning

confidence: 99%

“…The site features the combined presence of three complexities: topography, heterogeneous vegetation varying from grassy to forested, and interactions between wind turbine wakes. Hence, physics-based wind power forecasting at such a site is very challenging [18].…”

Section: Introductionmentioning

confidence: 99%

Numerical Weather Prediction and Artificial Neural Network Coupling for Wind Energy Forecast

2021

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“…Two of Copernicus regional products, the Digital Elevation Model (DEM) over Europe (EU-DEM), and the CORINE land cover (CLC) maps are relevant examples of pan-European cooperation [22,23]. CLC maps have been used to derive surface roughness classes in numerous wind resource studies [24,25]. The EU-DEM is a hybrid product based on the larger SRTM and ASTER GDEM datasets produced by NASA Earthdata and Japan Space Systems [26], two of many global and freely available DEM's.…”

Section: Identifying Suitable Open Geospatial Datasets and Previous Workmentioning

confidence: 99%

“…Figure24. Air infiltration sheltering (a) for the GBORO case-building; (b) and for the UKULE case building.…”

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confidence: 99%

Utilising Open Geospatial Data to Refine Weather Variables for Building Energy Performance Evaluation—Incident Solar Radiation and Wind-Driven Infiltration Modelling

Skeie

Gustavsen

2021

Energies

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In building thermal energy characterisation, the relevance of proper modelling of the effects caused by solar radiation, temperature and wind is seen as a critical factor. Open geospatial datasets are growing in diversity, easing access to meteorological data and other relevant information that can be used for building energy modelling. However, the application of geospatial techniques combining multiple open datasets is not yet common in the often scripted workflows of data-driven building thermal performance characterisation. We present a method for processing time-series from climate reanalysis and satellite-derived solar irradiance services, by implementing land-use, and elevation raster maps served in an elevation profile web-service. The article describes a methodology to: (1) adapt gridded weather data to four case-building sites in Europe; (2) calculate the incident solar radiation on the building facades; (3) estimate wind and temperature-dependent infiltration using a single-zone infiltration model and (4) including separating and evaluating the sheltering effect of buildings and trees in the vicinity, based on building footprints. Calculations of solar radiation, surface wind and air infiltration potential are done using validated models published in the scientific literature. We found that using scripting tools to automate geoprocessing tasks is widespread, and implementing such techniques in conjunction with an elevation profile web service made it possible to utilise information from open geospatial data surrounding a building site effectively. We expect that the modelling approach could be further improved, including diffuse-shading methods and evaluating other wind shelter methods for urban settings.

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“…2014), turbulence models (Tabas et al, 2019) aside of various numerical parameters. Remaining uncertainties and long computation times make measurements for sites in complex terrain mandatory for a bankable site assessment.…”

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confidence: 99%

Wind speed deviations in complex terrain

Ingenhorst¹,

Jacobs²,

Stößel

et al. 2020

Preprint

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Abstract. Wind farm sites within complex terrain are subject to local wind phenomena, which have a huge impact on a wind turbine's annual energy production. To reduce investment risk, an extensive site evaluation is therefore mandatory. Stationary long-term measurements are supplemented by CFD simulations, which are a commonly used tool to analyse and understand the three-dimensional wind flows above complex terrain. Though being under heavy research, such simulations still show a huge sensitivity for various input parameters like terrain, atmosphere and numerical setup. Within this paper, a different approach aims to measure instead of simulate wind speed deviations above complex terrain by using a flexible, airborne measurement system. An unmanned aerial vehicle is equipped with a standard ultrasonic anemometer. The uncertainty of the system is evaluated against stationary anemometer at different heights and shows very good agreement, especially in mean wind speed (

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Wind Energy Prediction in Highly Complex Terrain by Computational Fluid Dynamics

Cited by 23 publications

References 18 publications

Numerical Weather Prediction and Artificial Neural Network Coupling for Wind Energy Forecast

Numerical Weather Prediction and Artificial Neural Network Coupling for Wind Energy Forecast

Utilising Open Geospatial Data to Refine Weather Variables for Building Energy Performance Evaluation—Incident Solar Radiation and Wind-Driven Infiltration Modelling

Wind speed deviations in complex terrain

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