Day-Ahead Wind Power Forecasting Based on Wind Load Data Using Hybrid Optimization Algorithm

Guangyu Qin, Qingyou Yan, Jingyao Zhu, Chuanbo Xu and Daniel M. Kammen
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Guangyu Qin: School of Economic & Management, North China Electric Power University, Beijing 102206, China
Qingyou Yan: School of Economic & Management, North China Electric Power University, Beijing 102206, China
Jingyao Zhu: School of Economic & Management, North China Electric Power University, Beijing 102206, China
Chuanbo Xu: School of Economic & Management, North China Electric Power University, Beijing 102206, China
Daniel M. Kammen: Renewable and Appropriate Energy Laboratory, University of California, Berkeley, CA 94720, USA

Sustainability, 2021, vol. 13, issue 3, 1-17

Abstract: Accurate wind power forecasting is essential to reduce the negative impact of wind power on the operation of the grid and the operation cost of the power system. Day-ahead wind power forecasting plays an important role in the day-ahead electricity spot trading market. However, the instability of the wind power series makes the forecast difficult. To improve forecast accuracy, a hybrid optimization algorithm is established in this study, which combines variational mode decomposition (VMD), maximum relevance & minimum redundancy algorithm (mRMR), long short-term memory neural network (LSTM), and firefly algorithm (FA) together. Firstly, the original historical wind power sequence is decomposed into several characteristic model functions with VMD. Then, mRMR is applied to obtain the best feature set by analyzing the correlation between each component. Finally, the FA is used to optimize the various parameters LSTM. Adding the forecasting results of all sub-sequences acquires the forecasting result. It turns out that the proposed hybrid algorithm is superior to the other six comparison algorithms. At the same time, an additional case is provided to further verify the adaptability and stability of the proposed hybrid model.

Keywords: wind power forecast; variational mode decomposition; maximum relevance & minimum redundancy algorithm; long short-term memory neural network; firefly algorithm; optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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