A novel ensemble probabilistic forecasting system for uncertainty in wind speed

Wang, Jianzhou; Wang, Shuai; Zeng, Bo; Lu, Haiyan

doi:10.1016/j.apenergy.2022.118796

Cited by 61 publications

(6 citation statements)

References 48 publications

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“…According to Section 3.2, two indices, ACE and IS, are used in the literature to measure the probabilistic performance. Regarding ACE and IS, high-confidence levels of PINC 100(1α)% ranging from 90% to 99% are generally considered because of the high reliability required in power system optimization and operation (Wang et al, 2022). These two probabilistic indices and their corresponding PICP are tabulated in Table 2.…”

Section: Experimental Results and Analysis Of The Proposed Probabilis...mentioning

confidence: 99%

Probabilistic Prediction Intervals of Wind Speed Based on Explainable Neural Network

Huang

Hong²,

Wang³

2022

Front. Energy Res.

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With the rapid growth of wind power penetration into modern power grids, wind speed forecasting plays an increasingly significant role in the planning and operation of electric power and energy systems. However, the existing wind speed forecasting methods are modeled as black boxes, which are very complicated and cannot be written down explicitly due to the complex fluctuation characteristics of wind speed series. To this end, this study proposes a novel direct method based on an explainable neural network (xNN) for deterministic and probabilistic wind speed forecasting. It can theoretically extract the nonlinear mapping features in wind speed, thereby providing a clear explanation of the relationship between the input and the output of the forecasting model. Then, the uncertainties in wind speed are statistically synthesized via the kernel density estimation method. Finally, we use wind speed data from real wind farms in Belgium to verify the feasibility and effectiveness of the proposed method. The simulation results demonstrate that it is not only able to accurately extract the non-stationary feature in the wind speed series but also superior to other benchmark algorithms in prediction accuracy. Therefore, the proposed method has a high potential for practical applications in real electric power and energy systems.

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Section: Experimental Results and Analysis Of The Proposed Probabilis...mentioning

confidence: 99%

Probabilistic Prediction Intervals of Wind Speed Based on Explainable Neural Network

Huang

Hong²,

Wang³

2022

Front. Energy Res.

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“…Kou et al [11] summarized the data monitoring and operation and maintenance of offshore wind farms and explained the importance of wind farm data for wind turbine maintenance, and the future research directions in this field were explored. Studies on wind speed prediction can be categorized into short-term prediction and medium-to long-term prediction based on the length of their prediction time [12][13][14][15][16]. Jung et al [17] summarized the knowledge of wind speed and power prediction and proposed methods to improve the prediction accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Sustainable Operation and Maintenance of Offshore Wind Farms Based on the Deep Wind Forecasting

Zhou,

Ke,

Zhu

et al. 2023

Sustainability

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Offshore wind farms are becoming a pivotal solution to address the increasing energy demand worldwide and reduce carbon emissions to achieve a sustainable energy sector. Considering the higher operational and maintenance cost of offshore wind farms, it is important to make a good maintenance plan to guarantee the system’s reliability and reduce the total cost related to maintenance activities at the same time. Because maintenance planning is a long-term decision problem and the wind force is random, long-term wind force prediction is needed to help managers evaluate the loss caused by maintenances to be executed in the future. However, long-term wind force prediction is naturally complicated, which is much harder than the short-term (e.g., day-ahead) prediction widely investigated in the literature. In order to overcome this difficulty, we design a deep learning framework combining variational mode decomposition, a convolution neural network, long short-term memory network, and full-connected network. Using the public data from the city of Leeds, the prediction accuracy of the above framework is validated by comparing it with other prediction techniques. Then, the predicted wind force is input into the established optimization model determining preventive maintenances during a predefined period. Because the uncertainty of wind force is replaced by the prediction value, the optimization model can be established as a mixed-integer linear programing model, which only contains limited variables and can be solved quickly. Lastly, an abundance of numerical experiments are conducted to validate the effectiveness of the proposed optimization model, based on which some managerial insights are provided to the managers of offshore wind farms about the optimal operations and maintenance strategy. The research outcome will greatly promote the development of the wind power industry in the future.

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“…The deep learning models mentioned so far were originally designed to perform point forecasting, which is the prediction of the most likely value, given a particular choice of the loss function. The simplest approach to model uncertainty is to train an ensemble of RNNs and build confidence intervals from the statistics of the predictions in the ensemble [54]. This approach makes strong assumptions about the underlying data distribution and often generates overconfident intervals if the models in the ensemble are not different enough [36].…”

Section: Related Workmentioning

confidence: 99%

Probabilistic Load Forecasting With Reservoir Computing

Guerra,

Scardapane,

Bianchi

2023

IEEE Access

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Some applications of deep learning require not only to provide accurate results but also to quantify the amount of confidence in their prediction. The management of an electric power grid is one of these cases: to avoid risky scenarios, decision-makers need both precise and reliable forecasts of, for example, power loads. For this reason, point forecasts are not enough hence it is necessary to adopt methods that provide an uncertainty quantification. This work focuses on reservoir computing as the core time series forecasting method, due to its computational efficiency and effectiveness in predicting time series. While the RC literature mostly focused on point forecasting, this work explores the compatibility of some popular uncertainty quantification methods with the reservoir setting. Both Bayesian and deterministic approaches to uncertainty assessment are evaluated and compared in terms of their prediction accuracy, computational resource efficiency and reliability of the estimated uncertainty, based on a set of carefully chosen performance metrics.INDEX TERMS Time series forecasting, Reservoir computing, Probabilistic forecasting, Bayesian inference, Quantile regression.

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A novel ensemble probabilistic forecasting system for uncertainty in wind speed

Cited by 61 publications

References 48 publications

Probabilistic Prediction Intervals of Wind Speed Based on Explainable Neural Network

Probabilistic Prediction Intervals of Wind Speed Based on Explainable Neural Network

Sustainable Operation and Maintenance of Offshore Wind Farms Based on the Deep Wind Forecasting

Probabilistic Load Forecasting With Reservoir Computing

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