Short-term wind power probabilistic forecasting using a new neural computing approach: GMC-DeepNN-PF

Wang, Qianchao; Pan, Lei; Wang, Haitao; Wang, Xinchao; Zhu, Ying

doi:10.1016/j.asoc.2022.109247

Cited by 11 publications

(2 citation statements)

References 56 publications

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“…The prediction type of wind speed has different definitions according to the length of the cycle, and different researchers have different classifications as shown in Table 2, mainly including ultra-short-term, short-term, medium-term, and long-term prediction. [15,28,30,36,37,43,55,63,64,70,72,81,[95][96][97]103,107,112,113] Ultra short term 15 min [12,15,20,21,23,30,38,42,52,53,56,57,59,60,71,74,88,93,[98][99][100]103,108,110,114] Ultra short term 30 min [15,16,26,30,…”

Section: Reviews For Technologies and Applicationsmentioning

confidence: 99%

A Review of Modern Wind Power Generation Forecasting Technologies

Tsai

Hong

et al. 2023

Sustainability

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The prediction of wind power output is part of the basic work of power grid dispatching and energy distribution. At present, the output power prediction is mainly obtained by fitting and regressing the historical data. The medium- and long-term power prediction results exhibit large deviations due to the uncertainty of wind power generation. In order to meet the demand for accessing large-scale wind power into the electricity grid and to further improve the accuracy of short-term wind power prediction, it is necessary to develop models for accurate and precise short-term wind power prediction based on advanced algorithms for studying the output power of a wind power generation system. This paper summarizes the contribution of the current advanced wind power forecasting technology and delineates the key advantages and disadvantages of various wind power forecasting models. These models have different forecasting capabilities, update the weights of each model in real time, improve the comprehensive forecasting capability of the model, and have good application prospects in wind power generation forecasting. Furthermore, the case studies and examples in the literature for accurately predicting ultra-short-term and short-term wind power generation with uncertainty and randomness are reviewed and analyzed. Finally, we present prospects for future studies that can serve as useful directions for other researchers planning to conduct similar experiments and investigations.

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Section: Reviews For Technologies and Applicationsmentioning

confidence: 99%

A Review of Modern Wind Power Generation Forecasting Technologies

Tsai

Hong

et al. 2023

Sustainability

View full text Add to dashboard Cite

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“…For instance, Yang et al [15] and Zhang et al [16] utilized the copula quantile regression model and nonparametric probabilistic forecasting method, respectively, to describe the uncertainty in wind power outputs. Moreover, researchers introduced novel models and techniques for uncertainty forecasting [17,18] to enhance the reliability and accuracy of forecasting by characterizing and quantifying the uncertainty associated with wind and solar power generation. Yu et al [19] introduced the Parzen window approach to estimate the error distribution in forecasting and determine the minimum confidence interval for optimal interval forecasting of wind farms.…”

Section: Introductionmentioning

confidence: 99%

Joint Forecasting Method of Wind and Solar Outputs Considering Temporal and Spatial Correlation

Yuan,

Zhang,

Ming

et al. 2023

Sustainability

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In response to the problem of low forecasting accuracy in wind and solar power outputs, this study proposes a joint forecasting method for wind and solar power outputs by using their spatiotemporal correlation. First, autocorrelation analysis and causal testing are used to screen the forecasting factors. Then, a convolutional neural network–long short-term memory (CNN-LSTM) is constructed and trained to extract features effectively. Finally, the independent, ensemble, and joint forecasting effects are compared, using a certain clean energy base as the research object. Results show that the forecasting accuracy of the ensemble wind and solar power outputs is better than that of independent forecasting. The joint forecasting method can improve the forecasting accuracy of wind power by 20% but slightly affects the forecasting accuracy of solar power.

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