A hybrid solution for offshore wind resource assessment from limited onshore measurements
| Autor: | Donald Giddings, Dong Xu, Jie Ren, Felipe M. Pimenta, Alfredo Peña, Basem Elshafei, Jake Badger, Xuerui Mao |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Artificial neural network
Wind resource assessment 020209 energy Mechanical Engineering 02 engineering and technology Building and Construction Management Monitoring Policy and Law Sensor fusion 7. Clean energy Wind speed Gaussian process regresstion Offshore wind power General Energy 020401 chemical engineering Kriging Weather Research and Forecasting Model 0202 electrical engineering electronic engineering information engineering Continuous simulation Environmental science 0204 chemical engineering Spatiotemporal data fusion Spatial analysis Marine engineering |
| Zdroj: | Elshafei, B, Peña, A, Xu, D, Ren, J, Badger, J, Pimenta, F M, Giddings, D & Mao, X 2021, ' A hybrid solution for offshore wind resource assessment from limited onshore measurements ', Applied Energy, vol. 298, 117245 . https://doi.org/10.1016/j.apenergy.2021.117245 Applied Energy |
| DOI: | 10.1016/j.apenergy.2021.117245 |
| Popis: | In wind resource assessments, which are critical to the pre-construction of wind farms, measurements by LiDARs or masts are a source of high-fidelity data, but are expensive and scarce in space and time, particularly for offshore sites. On the other hand, numerical simulations, using for example the Weather Research and Forecasting (WRF) model, generate temporally and spatially continuous data with relatively low-fidelity. A hybrid approach is proposed here to combine the merit of measurements and simulations for the assessment of offshore wind. Firstly a temporal data fusion using deep Multi Fidelity Gaussian Process Regression (MF-GPR) is performed to combine the intermittent measurement and the continuous simulation data at an onshore location. Then a spatial data fusion using a neural network with Non-linear Autoregression (NAR) and Non-linear Autoregression with external input (NARX) are conducted to project the wind from onshore to offshore. The numerical and measured wind speeds along the west coast of Denmark were used to evaluate the method. We show that the proposed data fusion technique using a gappy onshore measurement results in accurate offshore wind resource assessment within a 2% margin error. |
| Databáze: | OpenAIRE |
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