Studies from Shanghai Jiao Tong University Provide New Data on Energy (Probabilistic Wind Power Forecasting Approach Via Instance-based Transfer Learning Embedded Gradient Boosting Decision Trees)
- April 12, 2019
- 263 views
2019 APR 12 (NewsRx) -- By a News Reporter-Staff News Editor at Energy Daily News -- A new study on Energy is now available. According to news reporting out of Shanghai, People’s Republic of China, by NewsRx editors, research stated, “With the high wind penetration in the power system, accurate and reliable probabilistic wind power forecasting has become even more significant for the reliability of the power system. In this paper, an instance-based transfer learning method combined with gradient boosting decision trees (GBDT) is proposed to develop a wind power quantile regression model.”
Financial supporters for this research include National Key Research and Development Program of China, Key Project of Shanghai Science and Technology Committee.
Our news journalists obtained a quote from the research from Shanghai Jiao Tong University, “Based on the spatial cross-correlation characteristic of wind power generations in different zones, the proposed model utilizes wind power generations in correlated zones as the source problems of instance-based transfer learning. By incorporating the training data of source problems into the training process, the proposed model successfully reduces the prediction error of wind power generation in the target zone. To prevent negative transfer, this paper proposes a method that properly assigns weights to data from different source problems in the training process, whereby the weights of related source problems are increased, while those of unrelated ones are reduced. Case studies are developed based on the dataset from the Global Energy Forecasting Competition 2014 (GEFCom2014).”
According to the news editors, the research concluded: “The results confirm that the proposed model successfully improves the prediction accuracy compared to GBDT-based benchmark models, especially when the target problem has a small training set while resourceful source problems are available.”
For more information on this research see: Probabilistic Wind Power Forecasting Approach Via Instance-based Transfer Learning Embedded Gradient Boosting Decision Trees. ENERGIES, 2019;12(1):. ENERGIES can be contacted at: Mdpi, St Alban-Anlage 66, Ch-4052 Basel, Switzerland.
Our news journalists report that additional information may be obtained by contacting J.H. Ma, Shanghai Jiao Tong University, School of Electronic Information and Electrical Engineering, Shanghai 200240, People’s Republic of China. Additional authors for this research include L. Cai, J. Gu and Z.J. Jin.
The direct object identifier (DOI) for that additional information is: https://doi.org/10.3390/en12010159. 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.)