Hybrid forecasting method for wind power integrating spatial correlation and corrected numerical weather prediction

Hu, Shuai; Xiang, Yue; Zhang, Hongcai; Xie, Shaojun; Li, Jianhua; Gu, Chenghong; Sun, Wei; Liu, Junyong

doi:10.1016/j.apenergy.2021.116951

Cited by 93 publications

(30 citation statements)

References 47 publications

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“…Using the constructed power curve, we transform the corresponding wind speed forecasts from the six competing models into wind power predictions. The resulting power predictions are evaluated using the PCE loss [32], which assigns unequal weights for under-and over-prediction, as in (14). (1 − g) P (s, t c + h) − P (s, t c + h) if f (s, t c + h) > Y (s, t c + h), (14) where P (s, t c + h) and P (s, t c + h) are the normalized power observations and forecasts at t c + h and the sth location, and g is the under-estimation weight, which is typically set at values higher than 0.5.…”

Section: Wind Power Forecasting Resultsmentioning

confidence: 99%

“…One direct approach to loosely inject physics within ML-based wind forecasting is by using NWP outputs as regressors to a statistical-or ML-based formulation. This approach is often referred to as a "hybrid" forecasting model as it attempts to calibrate the physics-based NWPs at a set of target locations and time resolutions by learning a functional mapping that adjusts future NWPs closer to incoming observations [12,13,14]. Our work broadly belongs to this family of hybrid approaches, but departs from the vast majority of the methods therein by bearing on physical knowledge to guide the selection of certain parameters, features, and statistical constructs in the ML-based forecasting model, rather than relying on a purely datadriven correction of NWP outputs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

AIRU-WRF: A Physics-Guided Spatio-Temporal Wind Forecasting Model and its Application to the U.S. North Atlantic Offshore Wind Energy Areas

Ye¹,

Brodie²,

Miles³

et al. 2023

Preprint

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The reliable integration of wind energy into modern-day electricity systems heavily relies on accurate short-term wind forecasts. We propose a spatiotemporal model called AIRU-WRF (short for the AI-powered Rutgers University Weather Research & Forecasting), which fuses numerical weather predictions (NWPs) with local observations in order to make wind speed forecasts that are short-term (minutes to hours ahead), and of high resolution, both spatially (site-specific) and temporally (minute-level). In contrast to purely data-driven methods, we undertake a "physics-guided" machine learning approach which captures salient physical features of the local wind field without the need to explicitly solve for those physics, including: (i) modeling wind field advection and diffusion via physically meaningful kernel functions, (ii) integrating exogenous predictors that are both meteorologically relevant and statistically significant; and (iii) linking the multi-type NWP biases to their driving meso-scale weather conditions. Tested on real-world data from the U.S. North Atlantic where several offshore wind projects are in-development, AIRU-WRF achieves notable improvements, in terms of both wind speed and power, relative to various forecasting benchmarks including physics-based, hybrid, statistical, and deep learning methods.

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Section: Wind Power Forecasting Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

AIRU-WRF: A Physics-Guided Spatio-Temporal Wind Forecasting Model and its Application to the U.S. North Atlantic Offshore Wind Energy Areas

Ye¹,

Brodie²,

Miles³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Due to mature technical equipment, physical methods have good prediction accuracy in long-term wind power forecasting in stable environments. For example, Hu et al proposed a hybrid short-term wind power forecasting method based on NWP data [4]. Kim et al developed a hybrid spatiotemporal prediction model by combining NWP data and satellite images for forecasting solar energy [5].…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Neural Network Based on Variational Mode Decomposition and an Improved Sparrow Search Algorithm for Wind and Solar Power Forecasting

Wu¹,

2021

IEEE Access

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Accurate forecasting methods for wind and solar power are important for power systems due to their potential to improve economic and environmental performance. For this purpose, an ensemble neural network framework composed of the LSTM, SVM, BP neural network and ELM is proposed for wind and solar power forecasting in China. Three common ways to improve prediction accuracy are adopted. First, unstable wind and solar power time series are decomposed into smooth sub-sequences by VMD, which reduces the undesirable effects caused by the volatility of the original series. Then, based on the decomposed sub-sequences, four basic models that are optimized based on the EOSSA algorithm are used for forecasting wind and solar power. Finally, the prediction results of the ENN are reconstructed by weighting the prediction results of the four models. The proposed ENN model is compared with 9 state-ofthe-art prediction models for wind and solar power forecasting. The results show that the ENN model has the lowest MAPE, MAE, MSE, and RMSE in both wind and solar power forecasting. These comparison results also show that the ENN model not only has the best prediction accuracy but also the most reliable prediction performance.

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“…Generally, the atmospheric wind speed information given by the meteorological center will be transformed into the meteorological conditions around the wind farm to be predicted. As a typical physical prediction model, numerical weather prediction (NWP) obtains conclusions by solving complex mathematical models including meteorological data such as temperature, pressure, topography, and geomorphology (Hu et al, 2021b). Due to the large amount of calculation, NWP is generally realized by simulating atmospheric motion by supercomputer, and the algorithm complexity and cost are high.…”

Section: Introductionmentioning

confidence: 99%

Wind power prediction based on wavelet denoising and improved slime mold algorithm optimized support vector machine

Lian

2021

Wind Engineering

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The accuracy of wind power prediction directly affects the operation cost of power grid and is the result of power grid supply and demand balance. Therefore, how to improve the prediction accuracy of wind power is very important. In order to improve the prediction accuracy of wind power, a prediction model based on wavelet denoising and improved slime mold algorithm optimized support vector machine is proposed. The wavelet denoising algorithm is used to denoise the wind power data, and then the support vector machine is used as the prediction model. Because the prediction results of support vector machine are greatly affected by model parameters, an improved slime mold optimization algorithm with random inertia weight mechanism is used to determine the best penalty factor and kernel function parameters in support vector machine model. The effectiveness of the proposed prediction model is verified by using two groups actually collected wind power data. Seven prediction models are selected as the comparison model. Through the comparison between the predicted value and the actual value, the prediction error and its histogram distribution, the performance indicators, the Pearson’s correlation coefficient, the DM test, box-plot distribution, the results show that the proposed prediction model has high prediction accuracy.

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Hybrid forecasting method for wind power integrating spatial correlation and corrected numerical weather prediction

Cited by 93 publications

References 47 publications

AIRU-WRF: A Physics-Guided Spatio-Temporal Wind Forecasting Model and its Application to the U.S. North Atlantic Offshore Wind Energy Areas

AIRU-WRF: A Physics-Guided Spatio-Temporal Wind Forecasting Model and its Application to the U.S. North Atlantic Offshore Wind Energy Areas

An Ensemble Neural Network Based on Variational Mode Decomposition and an Improved Sparrow Search Algorithm for Wind and Solar Power Forecasting

Wind power prediction based on wavelet denoising and improved slime mold algorithm optimized support vector machine

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