Forecasting household electric appliances consumption and peak demand based on hybrid machine learning approach
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 app...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2020-12-01
|
Series: | Energy Reports |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484720314967 |
id |
doaj-6f5e2549ea5145cb9ecc7ca3a13df581 |
---|---|
record_format |
Article |
spelling |
doaj-6f5e2549ea5145cb9ecc7ca3a13df5812020-12-23T05:02:12ZengElsevierEnergy Reports2352-48472020-12-01610991105Forecasting household electric appliances consumption and peak demand based on hybrid machine learning approachEjaz Ul Haq0Xue Lyu1Youwei Jia2Mengyuan Hua3Fiaz Ahmad4Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China; University Key Laboratory of Advanced Wireless Communications of Guangdong Province, Southern University of Science and Technology, Shenzhen 518055, ChinaDepartment of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China; University Key Laboratory of Advanced Wireless Communications of Guangdong Province, Southern University of Science and Technology, Shenzhen 518055, ChinaDepartment of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China; University Key Laboratory of Advanced Wireless Communications of Guangdong Province, Southern University of Science and Technology, Shenzhen 518055, China; Corresponding author at: Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China.Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, ChinaDepartment of Electrical and Computer Engineering, Air University, Islamabad, PakistanMachine 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.http://www.sciencedirect.com/science/article/pii/S2352484720314967ClusteringDemand responseSmart meterMachine learningSupport vector machineForecasting |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ejaz Ul Haq Xue Lyu Youwei Jia Mengyuan Hua Fiaz Ahmad |
spellingShingle |
Ejaz Ul Haq Xue Lyu Youwei Jia Mengyuan Hua Fiaz Ahmad Forecasting household electric appliances consumption and peak demand based on hybrid machine learning approach Energy Reports Clustering Demand response Smart meter Machine learning Support vector machine Forecasting |
author_facet |
Ejaz Ul Haq Xue Lyu Youwei Jia Mengyuan Hua Fiaz Ahmad |
author_sort |
Ejaz Ul Haq |
title |
Forecasting household electric appliances consumption and peak demand based on hybrid machine learning approach |
title_short |
Forecasting household electric appliances consumption and peak demand based on hybrid machine learning approach |
title_full |
Forecasting household electric appliances consumption and peak demand based on hybrid machine learning approach |
title_fullStr |
Forecasting household electric appliances consumption and peak demand based on hybrid machine learning approach |
title_full_unstemmed |
Forecasting household electric appliances consumption and peak demand based on hybrid machine learning approach |
title_sort |
forecasting household electric appliances consumption and peak demand based on hybrid machine learning approach |
publisher |
Elsevier |
series |
Energy Reports |
issn |
2352-4847 |
publishDate |
2020-12-01 |
description |
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. |
topic |
Clustering Demand response Smart meter Machine learning Support vector machine Forecasting |
url |
http://www.sciencedirect.com/science/article/pii/S2352484720314967 |
work_keys_str_mv |
AT ejazulhaq forecastinghouseholdelectricappliancesconsumptionandpeakdemandbasedonhybridmachinelearningapproach AT xuelyu forecastinghouseholdelectricappliancesconsumptionandpeakdemandbasedonhybridmachinelearningapproach AT youweijia forecastinghouseholdelectricappliancesconsumptionandpeakdemandbasedonhybridmachinelearningapproach AT mengyuanhua forecastinghouseholdelectricappliancesconsumptionandpeakdemandbasedonhybridmachinelearningapproach AT fiazahmad forecastinghouseholdelectricappliancesconsumptionandpeakdemandbasedonhybridmachinelearningapproach |
_version_ |
1724373390845804544 |