Short-Term Photovoltaic Power Prediction Modeling Based on AdaBoost Algorithm and Elman
文献类型：会议论文
| 作者 | Sun, Wenxuan5; Zhang T(张涛)1,2,3,4 ; Tao, Ran5; Wang AN(王安娜)5
|
| 出版日期 | 2020 |
| 会议日期 | December 25-27, 2020 |
| 会议地点 | Virtual, Chengdu, China |
| 关键词 | Photovoltaic power prediction AdaBoost algorithm Bat algorithm Elman nerve-network |
| 页码 | 184-188 |
| 英文摘要 | In recent years, with the rapid expansion of the installed capacity of renewable energy systems, the availability, stability and quality of smart grids have become increasingly important[1]. The application of renewable energy production forecasting has also been rapidly developed, especially in the field of solar photovoltaic (PV)[2][3]. In the example of solar PV output prediction, machine learning and hybrid technologies have been implemented for many applications. In this paper, a high-precision PV system output power prediction model based on improved AdaBoost and Elman is proposed. Multiple model using integrated AdaBoost algorithm fusion with the bat algorithm for the parameters of optimized combination of weak Elman neural network predictor to become a higher prediction precision, the method of strong predictor of the model can according to the weather information, such as temperature, solar radiation and the history of the output of the PV system data, the probability of photovoltaic power generation for 12 hours and deterministic prediction. The prediction accuracy of the model is determined by Root Mean Squared Error (RMSE). Experimental results show that the prediction accuracy of this algorithm is better than that of other benchmark models, and the algorithm can effectively predict the volatility and irregularity of complex time series. |
| 源文献作者 | IEEE ; IEEE Power and Energy Society ; University of Electronic Science and Technology of China |
| 产权排序 | 2 |
| 会议录 | 2020 10th International Conference on Power and Energy Systems, ICPES 2020
 |
| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISBN号 | 978-0-7381-4255-5 |
| 源URL | [http://ir.sia.cn/handle/173321/28361]  |
| 专题 | 沈阳自动化研究所_工业控制网络与系统研究室 |
| 通讯作者 | Sun, Wenxuan |
| 作者单位 | 1.School of Electrical Engineering, Dalian University of Technology, Dalian, China 2.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang, China 3.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China 4.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China 5.Northeastern University, College of Information Science and Engineering, Shenyang, China |
| 推荐引用方式 GB/T 7714 | Sun, Wenxuan,Zhang T,Tao, Ran,et al. Short-Term Photovoltaic Power Prediction Modeling Based on AdaBoost Algorithm and Elman[C]. 见:. Virtual, Chengdu, China. December 25-27, 2020. |
入库方式： OAI收割
来源：沈阳自动化研究所
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