综合能源系统源-荷能量的多时间尺度预测
摘要:为应对可再生能源利用和用户负荷的不确定性，提出一种多时间尺度预测方法。预测过程分日前、日内滚动和实时3个阶段进行，时间尺度分别为1 h、15 min和5 min。首先，采用基于差值统计的预测方法完成气象参数的3个阶段预测；其次，在负荷预测的日前和日内阶段，建立了信号分解与机器学习相结合的回归预测模型，实时阶段建立了机器学习时间序列预测模型；接着，以测试集的预测精度指标为依据确定了日前和日内滚动阶段对典型日负荷的最佳预测方法；最后，将预测方法应用于典型日的能量预测，验证了方法的可行性。研究结果显示：3个阶段典型日气象参数预测结果的决定系数R2都在0.8以上；在日前和日内滚动阶段，多元负荷的预测任务应采用不同的信号分解方法，实时阶段负荷预测结果的R2值均超过0.9，平均绝对误差百分比(mean absolute percentage error, MAPE)接近0。
Abstract：In order to cope with the uncertainty of renewable energy utilization and customer loads, a multi-timescale prediction method is proposed, where the prediction process is carried out in three phases: day-ahead, intra-day rolling and real-time, with timescales of 1 h, 15 min and 5 min, respectively. First, a prediction method based on difference statistics is used to accomplish the three stages of forecasting meteorological parameters. Second, a regression prediction model combining signal decomposition and machine learning is established for the day-ahead and intraday stages of load prediction, and a machine learning time series prediction model is established for the real-time stage. Next, the best prediction methods for typical daily loads in the day-ahead and intra-day rolling stages are determined based on the prediction accuracy metrics of the test set. Finally, the prediction method is applied to the energy forecast of typical days to verify the feasibility of the method. The results show that the determination coefficient R2 of the prediction results of the meteorological parameters for a typical day in all three phases is above 0.8; in the day-ahead and intraday rolling phases, the prediction tasks of multivariate loads should be performed with different signal decomposition methods, and the R2 of the load prediction results in the real-time phase is above 0.9, and the mean absolute percentage error (MAPE) is close to 0.
标题：综合能源系统源-荷能量的多时间尺度预测
title:Multi-Timescale Prediction of Source-Load Energy in Integrated Energy System
作者：张时聪,杨芯岩,韩少锋,吴迪,刘志坚,郭中骏
authors:ZHANG Shicong,YANG Xinyan,HAN Shaofeng,WU Di,LIU Zhijian,GUO Zhongjun
关键词：能量预测,信号分解,机器学习,多时间尺度,
keywords:energy prediction,signal decomposition,machine learning,multi-timescale,
发表日期：2024-09-14
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