Researchers from School of Automation Provide Details of New Studies and Findings in the Area of Wind Energy (Deep Non-crossing Probabilistic Wind Speed Forecasting With Multi-scale Features)

2022 MAY 12 (NewsRx) -- By a News Reporter-Staff News Editor at Energy Daily News -- Investigators discuss new findings in Energy - Wind Energy. According to news reporting out of Hunan, People’s Republic of China, by NewsRx editors, research stated, “Clean and renewable wind energy has made an outstanding contribution to alleviating the energy crisis. However, the randomness and volatility of wind brings great risk to the integration of wind power to the grid.”

Funders for this research include National Natural Science Foundation of China (NSFC), Key R&D Program of Hunan Province of China, Natural Science Foundation of Hunan Province.

Our news journalists obtained a quote from the research from the School of Automation, “Therefore, it is essential to obtain reliable and efficient wind speed forecasts. Quantile-based machine learning techniques, which usually produce satisfied quantile-based prediction intervals (PIs) for wind energy, have received widespread attention. However, the obtained PIs are usually crossed and violate the monotonicity of different conditional quantiles. In addition, the completeness and quality of features directly affect the forecasting performance of the models. Therefore, mining effective and sufficient information from the limited input data helps to improve the forecasting performance. In this paper, a novel method is developed for probabilistic wind speed forecasting based on deep learning, non-crossing quantile loss, multi-scale feature (MSF) extraction, and kernel density estimation (KDE). In terms of feature extraction, sufficient MSFs with simple pattern will be extracted based on a multi-layer convolutional neural network. Attention-based long short-term memory is used to further extract and encode temporal information for features of each scale and reduce computational cost. The final feature is obtained by concatenating all the encoded feature vectors. Instead of directly outputting different conditional quantiles, this study obtains the positive difference of adjacent conditional quantiles. On this basis, a non-crossing quantile loss is designed to ensure the monotonicity of different conditional quantiles. To understand the forecasting uncertainty comprehensively, KDE is used to estimate the continuous probability distribution function for various PIs. The proposed method is verified on four wind speed datasets collected form South Dakota.”

According to the news editors, the research concluded: “The results demonstrate that the proposed method has an excellent ability of generating high quality, high-precision, and non-crossing probabilistic wind speed forecasts.”

This research has been peer-reviewed.

For more information on this research see: Deep Non-crossing Probabilistic Wind Speed Forecasting With Multi-scale Features. Energy Conversion and Management, 2022;257. Energy Conversion and Management can be contacted at: Pergamon-elsevier Science Ltd, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, England. (Elsevier - www.elsevier.com; Energy Conversion and Management - http://www.journals.elsevier.com/energy-conversion-and-management/)

Our news journalists report that additional information may be obtained by contacting Yun Wang, Cent South Univ, School of Automation, Changsha, Hunan, People’s Republic of China. Additional authors for this research include Runmin Zou, Mengmeng Song, Ji Wang, Kaifeng Yang and Michael Affenzeller.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.enconman.2022.115433. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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