A Hierarchical Approach to Probabilistic Wind Power Forecasting

Gilbert, Ciaran; Browell, Jethro; McMillan, David

doi:10.1109/pmaps.2018.8440571

Cited by 3 publications

(8 citation statements)

References 22 publications

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“…In this study, two methods are investigated to leverage turbinelevel data and are compared to state-of-the-art benchmarks. The first is a feature engineering approach proposed in [25], where deterministic power forecasts for individual turbines are used as predictor variables when producing non-parametric wind farm forecasts. This is a hierarchical method in the sense that information from the turbine-level is used to supplement the available information set.…”

Section: Introductionmentioning

confidence: 99%

“…However, forecast coherency is not guaranteed. This work also expands on [25] by extending the case study to a second wind farm with different site characteristics and testing a second novel approach based on hierarchical coherency. In this second bottom-up approach, density forecasts are produced for all turbines and the spatial dependence between them is modelled in a copula framework to allow aggregation to the wind farm level.…”

Section: Introductionmentioning

confidence: 99%

“…Whereas the bottom-up probabilistic forecasts reflect the physical reality of the problem -that the total wind farm power output is the sum of individual turbine generation -and therefore have the added benefit of coherency. The main contributions of this paper are the proposed bottom-up hierarchical method and its evaluation, plus significantly expanding the evaluation of the feature engineering approach first introduced in [25]. We hypothesise that leveraging information from the turbine-level will enable us to improve forecast performance, particularly since modern utility scale wind farms are often distributed over large areas of complex terrain and as a result, individual turbines can experience different conditions from one another at any given time.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Leveraging Turbine-Level Data for Improved Probabilistic Wind Power Forecasting

Gilbert

Browell

McMillan

2020

IEEE Trans. Sustain. Energy

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This paper describes two methods for creating improved probabilistic wind power forecasts through the use of turbine-level data. The first is a feature engineering approach whereby deterministic power forecasts from the turbine level are used as explanatory variables in a wind farm level forecasting model. The second is a novel bottom-up hierarchical approach where the wind farm forecast is inferred from the joint predictive distribution of the power output from individual turbines. Notably, the latter produces probabilistic forecasts that are coherent across both turbine and farm levels, which the former does not. The methods are tested at two utility scale wind farms and are shown to provide consistent improvements of up to 5%, in terms of continuous ranked probability score compared to the best performing state-of-the-art benchmark model. The bottom-up hierarchical approach provides greater improvement at the site characterized by a complex layout and terrain, while both approaches perform similarly at the second location. We show that there is a clear benefit in leveraging readily available turbine-level information for wind power forecasting.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Leveraging Turbine-Level Data for Improved Probabilistic Wind Power Forecasting

Gilbert

Browell

McMillan

2020

IEEE Trans. Sustain. Energy

Self Cite

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“…Future work should consider this as well as utilising the high spatial and temporal information in a modelling framework more suitable to the data. For instance, the engineered features such as the rolling variance, which quantify the variability of the signal, could be more valuable in probabilistic forecasting for modelling the upper and lower ends of the distribution via quantile regression . Understandably, a hierarchical model where each turbine is used to generate a consistent wind farm forecast could be an optimal way of using the high spatial content of the data .…”

Section: Discussionmentioning

confidence: 99%

“…For instance, the engineered features such as the rolling variance, which quantify the variability of the signal, could be more valuable in probabilistic forecasting for modelling the upper and lower ends of the distribution via quantile regression. 27,45 Understandably, a hierarchical model where each turbine is used to generate a consistent wind farm forecast could be an optimal way of using the high spatial content of the data. 46 Additionally, a more in depth-study focused on extracting value from the temporal content of the wind forecast signal such as deep-learning, 47 instantaneous frequency transforms, 44 or wavelet decomposition 48 techniques should be explored.…”

Section: Discussionmentioning

confidence: 99%

Statistical post‐processing of turbulence‐resolving weather forecasts for offshore wind power forecasting

et al. 2019

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Accurate short‐term power forecasts are crucial for the reliable and efficient integration of wind energy in power systems and electricity markets. Typically, forecasts for hours to days ahead are based on the output of numerical weather prediction models, and with the advance of computing power, the spatial and temporal resolutions of these models have increased substantially. However, high‐resolution forecasts often exhibit spatial and/or temporal displacement errors, and when regarding typical average performance metrics, they often perform worse than smoother forecasts from lower‐resolution models. Recent computational advances have enabled the use of large‐eddy simulations (LESs) in the context of operational weather forecasting, yielding turbulence‐resolving weather forecasts with a spatial resolution of 100 m or finer and a temporal resolution of 30 seconds or less. This paper is a proof‐of‐concept study on the prospect of leveraging these ultra high‐resolution weather models for operational forecasting at Horns Rev I in Denmark. It is shown that temporal smoothing of the forecasts clearly improves their skill, even for the benchmark resolution forecast, although potentially valuable high‐frequency information is lost. Therefore, a statistical post‐processing approach is explored on the basis of smoothing and feature engineering from the high‐frequency signal. The results indicate that for wind farm forecasting, using information content from both the standard and LES resolution models improves the forecast accuracy, especially with a feature selection stage, compared with using the information content solely from either source.

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