A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction

Chu, Yiqi; Li, Chengcai; Wang, Yefang; Li, Jing; Li, Jian

doi:10.3390/en9110894

Cited by 9 publications

(5 citation statements)

References 57 publications

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“…For N predicted and observed scalar pairs at forecast hour i, (U P,i , U O,i ), the following statistics were calculated [27,[51][52][53]: mean predicted wind speed U P , mean observed wind speed U O , mean absolute error (MABE), root-mean-square error (RMSE), mean bias error (MBE) and the standard error in the wind speed (STDE). In addition, the Pearson correlation coefficient (R 2 ) and the index of agreement (IOA) between the predicted hourly wind speeds and their corresponding observed values were computed.…”

Section: In-situ Data and Error Metricsmentioning

confidence: 99%

“…The mean predicted wind speed U P , mean observed wind speed U O , mean absolute error (MABE), root-mean-square error (RMSE), mean bias error (MBE), the standard error in the wind speed (STDE), the Pearson correlation coefficient (R 2 ) and the index of agreement (IOA) between the predicted hourly wind speeds and their corresponding observed values were computed via the following formulae [27,[51][52][53]:…”

Section: Conflicts Of Interestmentioning

confidence: 99%

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Optimizing the Weather Research and Forecasting (WRF) Model for Mapping the Near-Surface Wind Resources over the Southernmost Caribbean Islands of Trinidad and Tobago

2017

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Numerical wind mapping is currently the wind power industry's standard for preliminary assessments for sites of good wind resources. Of the various available numerical models, numerical weather prediction (NWP) models are best suited for modeling mesoscale wind flows across small islands. In this study, the Weather Research and Forecast (WRF) NWP model was optimized for simulating the wind resources of the Caribbean islands of Trinidad and Tobago in terms of spin-up period for developing mesoscale features, the input initial and boundary conditions, and the planetary boundary layer (PBL) parameterizations. Hourly model simulations of wind speed and wind direction for a one-month period were compared with corresponding 10 m level wind observations. The National Center for Environmental Prediction (NCEP)-Department of Energy (DOE) reanalysis of 1.875 • horizontal resolution was found to be better suited to provide initial and boundary conditions (ICBCs) over the 1 • resolution NCEP final analysis (FNL); 86% of modeled wind speeds were within ±2 m/s of measured values at two locations when the coarse resolution NCEP reanalysis was used as compared with 55-64% of modeled wind speeds derived from FNL. Among seven PBL schemes tested, the Yonsei University PBL scheme with topographic drag enabled minimizes the spatial error in wind speed (mean bias error +0.16 m/s, root-mean-square error 1.53 m/s and mean absolute error 1.21 m/s) and is capable of modeling the bimodal wind speed histogram. These sensitivity tests provide a suitable configuration for the WRF model for mapping the wind resources over Trinidad and Tobago, which is a factor in developing a wind energy sector in these islands.

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Section: In-situ Data and Error Metricsmentioning

confidence: 99%

Section: Conflicts Of Interestmentioning

confidence: 99%

Optimizing the Weather Research and Forecasting (WRF) Model for Mapping the Near-Surface Wind Resources over the Southernmost Caribbean Islands of Trinidad and Tobago

2017

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“…WRF offers a wide variety of physical and dynamical elements to choose from; these elements must be put together to form model configurations, with which the model can be run [34]. However, because of imperfect models and uncertain initial boundary atmospheric conditions, errors exist in the NWP output [35,36].…”

Section: Introductionmentioning

confidence: 99%

Development of Stacked Long Short-Term Memory Neural Networks with Numerical Solutions for Wind Velocity Predictions

Wei

2020

Advances in Meteorology

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Taiwan, being located on a path in the west Pacific Ocean where typhoons often strike, is often affected by typhoons. The accompanying strong winds and torrential rains make typhoons particularly damaging in Taiwan. Therefore, we aimed to establish an accurate wind speed prediction model for future typhoons, allowing for better preparation to mitigate a typhoon’s toll on life and property. For more accurate wind speed predictions during a typhoon episode, we used cutting-edge machine learning techniques to construct a wind speed prediction model. To ensure model accuracy, we used, as variable input, simulated values from the Weather Research and Forecasting model of the numerical weather prediction system in addition to adopting deeper neural networks that can deepen neural network structures in the construction of estimation models. Our deeper neural networks comprise multilayer perceptron (MLP), deep recurrent neural networks (DRNNs), and stacked long short-term memory (LSTM). These three model-structure types differ by their memory capacity: MLPs are model networks with no memory capacity, whereas DRNNs and stacked LSTM are model networks with memory capacity. A model structure with memory capacity can analyze time-series data and continue memorizing and learning along the time axis. The study area is northeastern Taiwan. Results showed that MLP, DRNN, and stacked LSTM prediction error rates increased with prediction time (1–6 hours). Comparing the three models revealed that model networks with memory capacity (DRNN and stacked LSTM) were more accurate than those without memory capacity. A further comparison of model networks with memory capacity revealed that stacked LSTM yielded slightly more accurate results than did DRNN. Additionally, we determined that in the construction of the wind speed prediction model, the use of numerically simulated values reduced the error rate approximately by 30%. These results indicate that the inclusion of numerically simulated values in wind speed prediction models enhanced their prediction accuracy.

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“…The essence of uncertainty brought by IPS is the unpredictability due to time advances, which can be represented by forecast error. Several algorithms for day-ahead forecast of IPS power generation have been developed [16][17][18]. However, to the best of our knowledge, obtaining satisfactory forecast accuracy for IPS power generation is still an open problem.…”

Section: Introductionmentioning

confidence: 99%

Quantitative characterization of uncertainty levels of intermittent power sources

Wang

Guo

2018

Journal of Renewable and Sustainable Energy

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This paper designs a statistical quantification towards the intermittent power uncertainty in power systems. A negative-exponential forecast uncertainty function is constructed to represent the relationship between the statistics of forecast error of a single intermittent power source and time advance. Subsequently, other kinds of statistical functions are proposed to characterize the statistical uncertainty of multiple intermittent power sources and all power sources, namely the sum statistical functions, the equivalent statistical functions, and the contour statistical functions. Based on a large amount of historical observations, these functions are employed to statistically quantify the forecast uncertainty of a single intermittent power source, multiple intermittent power sources as well as all power sources. Historical data sampled from real wind farms and solar sites demonstrates the effectiveness of the proposed method.

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A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction

Cited by 9 publications

References 57 publications

Optimizing the Weather Research and Forecasting (WRF) Model for Mapping the Near-Surface Wind Resources over the Southernmost Caribbean Islands of Trinidad and Tobago

Optimizing the Weather Research and Forecasting (WRF) Model for Mapping the Near-Surface Wind Resources over the Southernmost Caribbean Islands of Trinidad and Tobago

Development of Stacked Long Short-Term Memory Neural Networks with Numerical Solutions for Wind Velocity Predictions

Quantitative characterization of uncertainty levels of intermittent power sources

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