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浙江大学学报(工学版)
浙江大学学报(工学版)  2020, Vol. 54 Issue (7): 1418-1424    DOI: 10.3785/j.issn.1008-973X.2020.07.021
电气工程     
基于智能家电的短期电力负荷预测与削峰填谷优化
王晨霖1(),杨洁1,居文军2,顾复1,陈芨熙1,*(),纪杨建1
1. 浙江大学 机械工程学院，浙江 杭州 310058
2. 青岛海尔科技有限公司，山东 青岛 266100
Short term load forecasting and peak shaving optimization based on intelligent home appliance
Chen-lin WANG1(),Jie YANG1,Wen-jun JU2,Fu GU1,Ji-xi CHEN1,*(),Yang-jian JI1
1. College of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China
2. Qingdao Haier Technology Limited Company, Qingdao 266100, China
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摘要：

通过可远程控制的联网智能家电，提出对城市群电力负荷的短期预测与削峰填谷优化. 分析某家电企业的智能家电集群运行产生的海量数据，建立城市群智能家电电力负荷预测模型，主要采用3种模型加权组合预测的方式，利用负荷数据中的趋势性、周期性、相关性、节假日特征及外部变量进行智能家电集群电力负荷的短期预测，单月内每日平均相对误差为4%~6%. 通过合理选择特征，该模型可以在不同家电间通用，依据家电类型分类预测后的结果可加和成为用电总负荷. 针对使用方式与用户习惯，提出智能家电电力负荷削峰填谷的控制策略，根据发电成本数据给出预期效益，说明基于智能家电负荷预测的用电调控能够有效降低电力部门发电成本、用户用电成本与电网负荷波动性.
关键词： 负荷预测;  时间序列预测;  智能家电;  削峰填谷    
Abstract:

Short term power load prediction model and peak shaving optimization in city scale were presented with remotely controlled online intelligent home appliances. Mass operational data from intelligent appliance were used. The prediction model was constructed, which ensembles three models, comprising trend, seasonality, autocorrelation, holiday effect and other factors to full the extent. The model can predict with the average daily relative error between 4%~6% per month. The model can be used on other types of home appliance and sum up to total power load through carefully selected features. Solutions for peak shaving were presented according to operational method and user preference. Returns of intelligent home appliances were estimated with power generating cost data. Power control strategy based on power prediction with intelligent appliance can effectively lower electricity generating cost, user cost and network load volatility.
Key words: load forecasting    time series prediction    intelligent home appliance    peak shaving
收稿日期: 2019-12-28 出版日期: 2020-07-05
CLC:  TM 715  
基金资助: 国家重点研发计划资助项目（2017YFB1400302）；宁波市科技创新2025重大专项资助项目(2019B10030)
通讯作者: 陈芨熙     E-mail: chenlinnyx@zju.edu.cn;chenjx@zju.edu.cn
作者简介: 王晨霖（1996—），女，硕士生，从事工业工程、数据挖掘研究. orcid.org/0000-0001-5840-4065. E-mail： chenlinnyx@zju.edu.cn
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王晨霖
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居文军
顾复
陈芨熙
纪杨建

引用本文:

王晨霖,杨洁,居文军,顾复,陈芨熙,纪杨建. 基于智能家电的短期电力负荷预测与削峰填谷优化[J]. 浙江大学学报(工学版), 2020, 54(7): 1418-1424.

Chen-lin WANG,Jie YANG,Wen-jun JU,Fu GU,Ji-xi CHEN,Yang-jian JI. Short term load forecasting and peak shaving optimization based on intelligent home appliance. Journal of ZheJiang University (Engineering Science), 2020, 54(7): 1418-1424.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2020.07.021    或    http://www.zjujournals.com/eng/CN/Y2020/V54/I7/1418

图 1  智能家电负荷预测建模总体流程
图 2  前一小时室内温度与能耗的回归线图
图 3  能耗滚动均值与方差图
图 4  能耗的时间序列分解
图 5  能耗移动平均曲线
图 6  能耗线性回归线
图 7  电力负荷的自相关图与偏自相关图
图 8  融合模型性能曲线
图 9  多变量回归基准模型性能
图 10  联网电热水器负荷控制图
图 11  智能家电数据传输与远程控制系统原理图
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