A hybrid Bayesian Kalman filter and applications to numerical wind speed modeling

Galanis, George; Papageorgiou, Evgenia H.; Liakatas, Aristotelis

doi:10.1016/j.jweia.2017.04.007

Cited by 27 publications

(12 citation statements)

References 23 publications

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“…More details on the Kalman filtering theory can be found in [27][28][29]. The filter has been tested successfully in wind speed and wind gust prediction Stathopoulos et al [30], Louka et al [34], Patlakas et al [53], Galanis et al [35] with results that ensure the reduction of the systematic errors induced by numerical weather models.…”

Section: Hybrid Bayesian Kalman Filtermentioning

confidence: 99%

“…Further details concerning Bayesian theory and models can be found in the studies of Box [54] and Bernardo and Smith [55], while the combination of the two approaches is fully described by Galanis et al [35].…”

Section: Hybrid Bayesian Kalman Filtermentioning

confidence: 99%

“…Despite the overall good performance, in many cases, such filters only reduce the systematic mean bias [34], especially in wind speed, due to the nature and variability of the parameter. A proposed solution for this drawback is described analytically in Galanis et al [35]. The suggested optimization technique is emerging from the conjunction of a non-linear Kalman filter and a Bayesian model (K-B model).…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we develop and validate a combined system of WRF high-resolution simulations and the aforementioned K-B filter. The main novelty proposed is the application of the hybrid filter to the two-dimensional field of horizontal wind speed components instead of the 1-dimensional filter in Galanis et al [35]. In this way, an improved prediction of wind speed and direction is produced for a very dense wind farm cluster located in Galicia (NW Spain).…”

Section: Introductionmentioning

confidence: 99%

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Development of a High-Resolution Wind Forecast System Based on the WRF Model and a Hybrid Kalman-Bayesian Filter

et al. 2019

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Regional microscale meteorological models have become a critical tool for wind farm production forecasting due to their capacity for resolving local flow dynamics. The high demand for reliable forecasting tools in the energy industry is the motivation for the development of an integrated system that combines the Weather Research and Forecasting (WRF) atmospheric model with an optimization obtained by the conjunction of a Kalman filter and a Bayesian model. This study focuses on the development and validation of this combined system in a very dense wind farm cluster located in Galicia (Northwest of Spain). A period of one year is simulated at 333 m horizontal resolution, with a daily operational forecasting set-up. The Kalman-Bayesian filter was tested both directly on wind speed and on the U-V (zonal and meridional) components for nowcasting periods from 10 min to 6 h periods, all of them with important applications in the wind industry. The results are quite promising, as the main statistical error indices are significantly improved in a 6 h forecasting horizon and even more in shorter horizon cases. The Mean Annual Error (MAE) for 1 h nowcasting horizon is 1.03 m/s for wind speed and 12.16 ° for wind direction. Moreover, the successful utilization of the integrated system in test cases with different characteristics demonstrates the potential utility that this tool may have for a variety of applications in wind farm operations and energy markets.

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Section: Hybrid Bayesian Kalman Filtermentioning

confidence: 99%

Section: Hybrid Bayesian Kalman Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Development of a High-Resolution Wind Forecast System Based on the WRF Model and a Hybrid Kalman-Bayesian Filter

et al. 2019

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“…The coefficient of efficiency can go as high as 1, with this value indicating a perfect fit. According to Galanis et al [33], when the NS value is higher than 0.75, the performance of the model is considered good. For NS values between 0.36 and 0.75, the performance is considered acceptable, while NS values less than 0.36 deem the model unacceptable.…”

Section: Accuracy Measurementsmentioning

confidence: 99%

Innovative Hybrid Modeling of Wind Speed Prediction Involving Time-Series Models and Artificial Neural Networks

et al. 2018

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This work proposes hybrid models combining time-series models (using linear functions) and artificial intelligence (using a nonlinear function) that can be used to provide monthly mean wind speed predictions for the Brazilian northeast region. These might be useful for wind power generation; for example, they could acquire important information on how the local wind potential can be usable for a possible wind power plant through understanding future wind speed values. To create the proposed hybrid models, it was necessary to set the wind speed variable as a dependent variable of exogenous variables (i.e., pressure, temperature, and precipitation). Thus, it was possible to consider the meteorological characteristics of the study regions. It is possible to verify the hybrid models' efficiency in providing perfect adjustments to the observed data. This statement is based on the low values found in the error statistical analysis, i.e., an error of approximately 5.0% and a Nash-Sutcliffe coefficient near to 0.96. These results were certainly important in predicting the wind speed time-series, which was similar to the observed wind speed time-series profile. Great similarities of maximums and minimums between the series were evident and showed the capacity of the models to represent the seasonality characteristics.

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A two-step hybrid system towards optimized wave height forecasts

Androulakis

Galanis

2021

Stoch Environ Res Risk Assess

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A hybrid Bayesian Kalman filter and applications to numerical wind speed modeling

Cited by 27 publications

References 23 publications

Development of a High-Resolution Wind Forecast System Based on the WRF Model and a Hybrid Kalman-Bayesian Filter

Development of a High-Resolution Wind Forecast System Based on the WRF Model and a Hybrid Kalman-Bayesian Filter

Innovative Hybrid Modeling of Wind Speed Prediction Involving Time-Series Models and Artificial Neural Networks

A two-step hybrid system towards optimized wave height forecasts

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