Forecasting household electric appliances consumption and peak demand based on hybrid machine learning approach

Haq，Ejaz Ul1,2; Lyu，Xue1,2; Jia，Youwei1,2; Hua，Mengyuan1; Ahmad，Fiaz3

2020-12-01

10.1016/j.egyr.2020.11.071

Energy Reports   影响因子和分区

2352-4847

2352-4847

Machine learning approaches have diverse applications in forecasting electrical energy consumption using smart meter data. Various classification techniques and clustering methods analyze smart meter data for accurately forecasting the electrical appliance consumption and peak demand. Electrical appliance forecasting and peak demand forecasting play a vital and key role in planning, maintenance and automation development for electrical power system. However, there is always a variation between electrical appliance consumption and appliance energy demand due to certain parameters including losses in lines and appliance and mismanagement of appliance energy demand. Detail scrutiny of smart meter data is required to identify the decisive attributes and major cause of variation between electrical appliance consumption and customers’ peak demand. This paper proposed a hybrid method based on Machine learning for forecasting appliance consumption and peak demand. We have deployed faster k-medoids clustering, support vector machine and artificial neural network for forecasting appliance consumption and customers’ peak demand. The proposed algorithm achieves 99.2% accuracy in forecasting electrical appliance consumption which is much better compared to state-of-the-art in same field. Experimental results validate the effectiveness of the proposed method in forecasting the electrical appliance consumption using smart meter data.

Clustering Demand response Forecasting Machine learning Smart meter Support vector machine

SCI ; EI

英语

第一 ; 通讯

Guangdong Basic and Applied Basic Research Fund, China[2019A1515111173] ; High-level University Fund, China[G02236002] ; Young Talent Program[2018KQNCX223]

Energy & Fuels

Energy & Fuels

WOS:000604392100149

ELSEVIER

20205209695161

Electric power systems ; Energy utilization ; Support vector machines ; Energy management ; Neural networks ; Smart meters

Energy Management and Conversion:525 ; Energy Utilization:525.3 ; Electric Power Systems:706.1 ; Computer Software, Data Handling and Applications:723 ; Electric and Electronic Measuring Instruments:942

2-s2.0-85098223749

Scopus

1.Department of Electrical and Electronic Engineering,Southern University of Science and Technology,Shenzhen,China
2.University Key Laboratory of Advanced Wireless Communications of Guangdong Province,Southern University of Science and Technology,Shenzhen,518055,China
3.Department of Electrical and Computer Engineering,Air University,Islamabad,Pakistan

Haq，Ejaz Ul,Lyu，Xue,Jia，Youwei,et al. Forecasting household electric appliances consumption and peak demand based on hybrid machine learning approach[J]. Energy Reports,2020,6:1099-1105.

Haq，Ejaz Ul,Lyu，Xue,Jia，Youwei,Hua，Mengyuan,&Ahmad，Fiaz.(2020).Forecasting household electric appliances consumption and peak demand based on hybrid machine learning approach.Energy Reports,6,1099-1105.

Haq，Ejaz Ul,et al."Forecasting household electric appliances consumption and peak demand based on hybrid machine learning approach".Energy Reports 6(2020):1099-1105.