Wind power, as the main renewable energy source, has become the core proposition for the construction of new power systems in terms of large-scale grid connection and efficient consumption based on the background of the accelerated transformation of the global energy structure towards "low-carbon, intelligent, and market-oriented". However, the inherent intermittency, volatility, and spatial heterogeneity of wind power make power forecasting technology not only related to the safe and stable operation of the power grid, but also directly determine the profitability and risk control level of power generation enterprises in the electricity market. Faced with the policy orientation of full participation of new energy in market transactions (such as China's "14th Five Year Plan" for modern energy system planning), the bottleneck of prediction accuracy under complex meteorological conditions, and the engineering demand for centralized grid connection of wind power bases with a capacity of tens of millions of kilowatts, traditional prediction methods have shown deficiencies in spatial and temporal resolution, multi scenario adaptability, and market-oriented coupling dimensions. The randomness, intermittency, and volatility of wind power seriously affect the safety and stability of the power grid. In current increasingly serious energy problem, safety and energy conservation have always been key issues that need to be urgently addressed in the power system. This book studies methods to improve the accuracy of wind power prediction from the perspectives of numerical weather prediction correction, switching mechanism, spatiotemporal information mining of wind, and fluctuation characteristics, in order to enhance the dispatchability of wind power and promote its consumption. Since 2010, I have been researching wind power prediction technology, and wind power prediction has become a hot research direction from the beginning to the present, achieving a large number of research results. The book is based on accumulated experience and insights in conducting research on wind power prediction technology, and it focuses on showcasing the research achievements of the laboratory in the past three years. This book can serve as a reference for undergraduate and graduate students majoring in electrical engineering and automation, as well as new energy in higher education institutions. It can also be used as a reference for researchers and engineering technicians in related research fields.

Mao Yang, a professor and doctoral supervisor in Northeast Electric Power University. He is a top innovative talent in Jilin Province, an expert who enjoys special allowances from the Jilin Provincial Government and he have been selected as one of the top 2% scientists in the world in 2024. His research interests include new energy power forecasting, microgrid optimization scheduling, and comprehensive energy load forecasting technology. Over 80 papers published are recruited by Web of science or Ei Compendex as well as over 10 invention patents are authorized by China National Intellectual Property Administration. He has led or participated in 2 National Natural Science Foundation projects, 2 National Key R&D Program projects, and over 20 provincial and ministerial level funding and science and technology research projects of State Grid Corporation of China. He has received 2 Science and Technology Progress Awards Jilin Province and 1 First Prize of Electric Power Science and Technology Progress of Jilin Province. He is Serving as a member of the New Energy Grid Connection and Operation Professional Committee of the Chinese Society of Electrical Engineering, a member of the Energy Meteorology Professional Committee, an executive director of the Jilin Automation Society, an editorial board member of the "Renewable Energy" magazine, and a network evaluation expert for the China Electric Power Science and Technology Award.