Recent Advances in Short-term Wind Power Forecasting: Modeling Methods and Real-world Data

Recent Advances in Short-term Wind Power Forecasting: Modeling Methods and Real-world Data

Edited by Haijian Shao, Xing Deng, Yaming Ren, Xia Wang, Lu Zhang


Energy is an essential material basis for human social progress and economic development. Traditional energy such as coal, oil and gases has not only limited quantity but also brings potential pollution to the human survival environment. In the past decades, the social development is affected by the oil crisis and various climate change factors, so the development of renewable energy ideas gradually becomes the consensus of the international community. Wind energy as a renewable energy is produced by the air flow acting. Improving the forecasting accuracy of the wind power in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. In this book, the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling are given. In addition, the proposed approach based on neural networks combined with wavelet analysis, model structure selection, manifold algorithm, seasonal pattern analysis, and AdaBoosting technique etc. are proposed, and the performance evaluation based on the real data from wind power plant of North China, East China as well as National Renewable Energy Laboratory (NREL) are used to demonstrate the performance and verify the model effectiveness, aiming to provide reference strategies for relevant researchers as well as practical applications.

The monograph will be useful to both graduate and undergraduate students, engineers that are interested in understanding the mechanisms of eddy current induction as well as in quantitative results and research people active not only in this particular field but also in electromagnetic theory in general.

Softcover.r. 320 pages. © 2019. ISBN-978-1-7340206-5-6, DOI:10.32604/9781734020656