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Study Data from Central South University Update Understanding of Wind Farms (An Evolution-dependent Multi-objective Ensemble Model of Vanishing Moment With Adversarial Auto-encoder for Short-term Wind Speed Forecasting In Xinjiang Wind Farm, ...)

Source: 
Daily China News

2019 NOV 13 (NewsRx) -- By a News Reporter-Staff News Editor at Daily China News -- Data detailed on Energy - Wind Farms have been presented. According to news originating from Hunan, People’s Republic of China, by NewsRx correspondents, research stated, “Wind speed forecasting can enhance the safety and economy of wind energy integration and conversion. The characteristic of the wind speed evolves over time.”

Financial supporters for this research include National Natural Science Foundation of China, Training Program for Excellent Young Innovators of Changsha, innovation driven project of the Central South University, Shenghua Yu-ying Talents Program of the Central South University, Changsha Science & Technology Project.

Our news journalists obtained a quote from the research from Central South University, “In this paper, a novel evolution-dependent multi-objective ensemble model of vanishing moment is proposed to solve the above problem. The proposed model can assign time-varying ensemble weights to base predictors with the different vanishing moments, so as to achieve better forecasting performance. In this model, the deep Adversarial Auto-Encoder (AAE) is firstly utilized to convert the wind speed into a two-dimension Gaussian distribution feature space. The Bat Algorithm (BA) is applied to partition the feature space into several sectors. Each sector represents a cluster of wind speed. Outlier Robust Extreme Learning Machines (ORELMs) improved by Maximal Overlap Discrete Wavelet Packet Transform (MODWPT) with the different vanishing moments are applied as base predictors, and the Multi-Objective Feasible Enhanced Particle Swarm Optimization (MOFEPSO) is used to obtain the best ensemble weights for each cluster. At last, the suitable ensemble model is applied for forecasting at each time. Four actual wind speed series collected from Xinjiang wind farm are used to verify the effectiveness of the proposed model.”

According to the news editors, the research concluded: “The experimental results indicate the proposed model outperforms other benchmark models.”

For more information on this research see: An Evolution-dependent Multi-objective Ensemble Model of Vanishing Moment With Adversarial Auto-encoder for Short-term Wind Speed Forecasting In Xinjiang Wind Farm, China. Energy Conversion and Management, 2019;198():. 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/)

The news correspondents report that additional information may be obtained from H. Liu, Central South University, Key Lab Traff Safety Track, School of Traffic and Transportation Engineering, Ministry of Education, Iair, Changsha 410075, Hunan, People’s Republic of China.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.enconman.2019.111914. 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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