A Prediction Model for Ultra-Short-Term Output Power of Wind Farms Based on Deep Learning

Yongsheng Wang; Jing Gao; Zhiwei Xu; Jidong Luo; Leixiao Li

Authors

Yongsheng Wang 1. College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China 2. Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application for Agriculture and Animal Husbandry, Hohhot 010018, China 3. College of Data Science and Application, Inner Mongolia University of Technology, Hohhot 010080, China 4. Inner Mongolia Autonomous Region Engineering ï¼† Technology Research Center of Big Data Based Software Service, Hohhot 010080, China
Jing Gao 1. College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China 2. Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application for Agriculture and Animal Husbandry, Hohhot 010018, China
Zhiwei Xu 1. College of Data Science and Application, Inner Mongolia University of Technology, Hohhot 010080, China 2. Inner Mongolia Autonomous Region Engineering ï¼† Technology Research Center of Big Data Based Software Service, Hohhot 010080, China 3. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China
Jidong Luo Haohan Data Technology Co., Ltd, Beijing 100080, China
Leixiao Li 1. College of Data Science and Application, Inner Mongolia University of Technology, Hohhot 010080, China 2. Inner Mongolia Autonomous Region Engineering ï¼† Technology Research Center of Big Data Based Software Service, Hohhot 010080, China

Keywords:

wind power, output power, ultra-short-term prediction, deep learning (DL), long short-term memory (LSTM) model

Abstract

The output power prediction of wind farm is the key to effective utilization of wind energy and reduction of wind curtailment. However, the prediction of output power has long been a difficulty faced by both academia and the wind power industry, due to the high stochasticity of wind energy. This paper attempts to improve the ultra-short-term prediction accuracy of output power in wind farm. For this purpose, an output power prediction model was constructed for wind farm based on the time sliding window (TSW) and long short-term memory (LSTM) network. Firstly, the wind power data from multiple sources were fused, and cleaned through operations like dimension reduction and standardization. Then, the cyclic features of the actual output powers were extracted, and used to construct the input dataset by the TSW algorithm. On this basis, the TSW-LSTM prediction model was established to predict the output power of wind farm in ultra-short-term. Next, two regression evaluation metrics were designed to evaluate the prediction accuracy. Finally, the proposed TSW-LSTM model was compared with four other models through experiments on the dataset from an actual wind farm. Our model achieved a super-high prediction accuracy 92.7% as measured by d_MAE, an evidence of its effectiveness. To sum up, this research simplifies the complex prediction features, unifies the evaluation metrics, and provides an accurate prediction model for output power of wind farm with strong generalization ability.

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A Prediction Model for Ultra-Short-Term Output Power of Wind Farms Based on Deep Learning

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