), 曹迪1, 黄琦1,2, 陈哲3 1.电子科技大学 机械与电气工程学院, 成都 611731
2.成都理工大学 核技术与自动化工程学院,成都 610051
3.奥尔堡大学 能源系, 奥尔堡 DK-9110,丹麦
收稿日期:2021-07-07出版日期:2021-12-28发布日期:2021-12-30通讯作者:胡维昊E-mail:whu@uestc.edu.cn基金资助:廖启术(1998-),男,湖南省益阳市人,硕士生,主要从事可再生能源负荷和发电预测研究.Distributed Photovoltaic Net Load Forecasting in New Energy Power Systems
LIAO Qishu1, HU Weihao1(), CAO Di1, HUANG Qi1,2, CHEN Zhe3 1. School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
2. College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu 610051, China
3. Department of Energy Technology, Aalborg University, Aalborg DK-9110, Denmark
Received:2021-07-07Online:2021-12-28Published:2021-12-30Contact:HU Weihao E-mail:whu@uestc.edu.cn摘要/Abstract
摘要： 为响应碳达峰、碳中和的需求,构建一套完整的“源-网-荷-储”的新能源电力系统,提出了一种基于Hamiltonian Monte Carlo推断深度高斯过程(HMCDGP)算法的分布式光伏净负荷预测模型.首先,分别使用直接预测和间接预测两种形式对预测模型的精度进行实验并得到点预测结果;其次,使用所提出的模型进行概率预测实验并得到区间预测结果;最后,通过以澳洲电网记录的300户净负荷数据为基础的对比实验验证所提模型的优越性.在得到准确的净负荷概率预测后,可以通过电力调度充分利用光伏产出,减少化石能源使用,从而减少碳排放.
关键词: 净负荷概率预测, 光伏产出, 深度高斯过程, 点预测, 区间预测
Abstract: To respond to the demand of achieving carbon peaking and carbon neutrality goals, and to construct a complete “source-grid-load-storage” new energy power system, a distributed photovoltaic net load forecasting model based on Hamiltonian Monte Carlo inference for deep Gaussian processes (HMCDGP) is proposed. First, direct and indirect forecasting methods are used to examine the accuracy of the proposed model and to obtain spot forecasting results. Then, the proposed model is used to perform probability forecasting experiments and produce interval prediction results. Finally, the superiority of the proposed model is verified through the comparative experiments based on the net load data of 300 households recorded by Australia Grid. After obtaining the exact net load probabilistic forecasting results, the photovoltaic production can be fully utilized via power dispatch, which can reduce the use of fossil energy and further reduce the carbon emission.
Key words: net load forecasting, photovoltaic production, deep Gaussian process, point forecasting, interval prediction
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