Abstract
Short-term forecasting of wind power plays a critical role in day-ahead dispatching and economic operation of power systems. In this paper, a wind power prediction model based on Convolutional Neural Networks (CNN) and Conditional Generative Adversarial Network (CGAN) is proposed. With weather factor labels helping, the model is proposed to solve the problem of day-ahead prediction. In this model, K-means is applied to divide the historical wind farm data into several parts based on weather factors. At each time, the power of similar weather time can be found through grey relational analysis (GRA), which can be used as a label together with weather information. With the guidance of conditional labels, the generative model can generate samples more purposefully, and the discriminator can identify more accurately. By this way, the generator and the discriminator form a game so that CGAN model can improve the accuracy of wind power forecasting. Finally, three examples of Datang Hongxing Wind farm are given to test the validity of the proposed algorithm.
Original language | English |
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Title of host publication | Proceedings of 2021 IEEE Sustainable Power and Energy Conference |
Publisher | IEEE |
Publication date | 2021 |
Pages | 73-79 |
ISBN (Electronic) | 9781665414395 |
DOIs | |
Publication status | Published - 2021 |
Event | 2021 IEEE Sustainable Power and Energy Conference - Nanjing, China Duration: 23 Dec 2021 → 25 Dec 2021 |
Conference
Conference | 2021 IEEE Sustainable Power and Energy Conference |
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Country/Territory | China |
City | Nanjing |
Period | 23/12/2021 → 25/12/2021 |
Keywords
- Conditional generative adversarial network
- Renewable energy
- Short-term forecasting
- Wind power forecasting