Energy Central News

Curated power industry news from thousands of top sources.

News

Researchers from Zhengzhou University Report New Studies and Findings in the Area of Applied Mathematics (A Novel Wind Speed Forecasting Model Based On Moving Window and Multi-objective Particle Swarm Optimization Algorithm)

Source: 
Math Daily News

2019 DEC 02 (NewsRx) -- By a News Reporter-Staff News Editor at Math Daily News -- Investigators discuss new findings in Mathematics - Applied Mathematics. According to news reporting from Henan, People’s Republic of China, by NewsRx journalists, research stated, “Accurate wind speed forecasting is important in power grid security, power system management, operation and market economics. However, most research has focused only on improving either accuracy or stability, with few studies addressing the two issues, simultaneously.”

Funders for this research include National Natural Science Foundation of China, Program for Science & Technology Innovation Talents in University of Henan Province (HASTIT), Program for Young Scholar in University of Henan Province.

The news correspondents obtained a quote from the research from Zhengzhou University, “Therefore, we proposed a novel combined model based on multi-objective particle swarm optimization, which is applied to optimize the key parameters of the echo state network. Most combined wind speed forecasting methods just use the combination theory to combine individual methods, this paper uses echo state network to combine the intermediate wind speed forecasting results of three artificial neural networks. Moreover, a new dataset division mechanism based on the moving window is applied in this paper. Firstly, the length of the input data is changed from 5 to 15 for 1-step, 2-step and 3-step wind speed forecasting, after that, the optimal length of the input vector can be got. And then we apply this optimal length of the input vector to another dataset for further verifying the proposed method. In order to verify the forecasting effectiveness of the proposed forecasting model, the 80/min wind speed data of M2 tower of the National Wind Power Technology Center of the United States were taken as an example.”

According to the news reporters, the research concluded: “The experimental results indicate that the proposed algorithm is superior to the other ten comparative models in prediction accuracy and stability, and it also performs better than the combined model that we have proposed before.”

For more information on this research see: A Novel Wind Speed Forecasting Model Based On Moving Window and Multi-objective Particle Swarm Optimization Algorithm. Applied Mathematical Modelling, 2019;76():717-740. Applied Mathematical Modelling can be contacted at: Elsevier Science Inc, Ste 800, 230 Park Ave, New York, NY 10169, USA. (Elsevier - www.elsevier.com; Applied Mathematical Modelling - http://www.journals.elsevier.com/applied-mathematical-modelling/)

Our news journalists report that additional information may be obtained by contacting Y.H. Chen, Zhengzhou University, School of Information Engineering, Zhengzhou 450000, Henan, People’s Republic of China. Additional authors for this research include Z.S. He, Z.H. Shang, C.H. Li, L. Li and M.L. Xu.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.apm.2019.07.001. 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.

 

(Our reports deliver fact-based news of research and discoveries from around the world.)

 

Discussions

No discussions yet. Start a discussion below.

Get Published - Build a Following

The Energy Central Power Industry Network is based on one core idea - power industry professionals helping each other and advancing the industry by sharing and learning from each other.

If you have an experience or insight to share or have learned something from a conference or seminar, your peers and colleagues on Energy Central want to hear about it. It's also easy to share a link to an article you've liked or an industry resource that you think would be helpful.

                 Learn more about posting on Energy Central »