AMI Smart Meter Big Data Analytics for Time Series of Electricity Consumption

被引:20
|
作者
Rashid, Mohammad Harun [1 ]
机构
[1] Pace Univ, New York, NY 10038 USA
关键词
Smart meter; AMI; smart grid; time series; big data; electric consumptions; forcast; data analytics; load profile;
D O I
10.1109/TrustCom/BigDataSE.2018.00267
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
AMI Smart meters are the advanced meters capable of measuring customer energy consumption at a fine-grained time interval, e.g., every 5 minutes, 15 minutes etc. The data are very sizable, and might be from different sources, along with the other social-economic metrics, which make the data management very complex. A smart grid is an intelligent electricity grid that optimizes the generation, distribution and consumption of electricity through the introduction of Information and Communication Technologies on the electricity grid. Deployment of smart grids gives space to an occurrence of new methods of machine learning and data analysis. Smart grids can contain a millions of smart meters, which produce a large amount of data of electricity consumption (long time series). Big Data technologies offers suitable solutions for utilities. This paper presents a thorough analysis of 5-minutes 100 anonymized commercial buildings meter data sets to explore time series of electricity consumption and on the creation of a simple forecast model, which uses similar day approach. Our big data analytics can help energy companies to improve the management of energy and services, support intelligent grid control, make an accurate forecast or to detect anomalies.
引用
收藏
页码:1771 / 1776
页数:6
相关论文
共 50 条
  • [1] Big Data Analytics for Discovering Electricity Consumption Patterns in Smart Cities
    Perez-Chacon, Ruben
    Luna-Romera, Jose M.
    Troncoso, Alicia
    Martinez-Alvarez, Francisco
    Riquelme, Jose C.
    ENERGIES, 2018, 11 (03)
  • [2] A Big Data platform for smart meter data analytics
    Wilcox, Tom
    Jin, Nanlin
    Flach, Peter
    Thumim, Joshua
    COMPUTERS IN INDUSTRY, 2019, 105 : 250 - 259
  • [3] Fast Big Data Analytics for Smart Meter Data
    Mohajeri, Morteza
    Ghassemi, Abolfazl
    Gulliver, T. Aaron
    IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY, 2020, 1 : 1864 - 1871
  • [4] Smart Electricity Meter Data Analytics: A Brief Review
    Pawar, Savita
    Momin, B. F.
    2017 IEEE REGION 10 INTERNATIONAL SYMPOSIUM ON TECHNOLOGIES FOR SMART CITIES (IEEE TENSYMP 2017), 2017,
  • [5] Electricity Consumption Clustering Using Smart Meter Data
    Tureczek, Alexander
    Nielsen, Per Sieverts
    Madsen, Henrik
    ENERGIES, 2018, 11 (04)
  • [6] Regularization in Hierarchical Time Series Forecasting with Application to Electricity Smart Meter Data
    Ben Taieb, Souhaib
    Yu, Jiafan
    Barreto, Mateus Neves
    Rajagopal, Ram
    THIRTY-FIRST AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2017, : 4474 - 4480
  • [7] Advanced Analytics for Harnessing the Power of Smart Meter Big Data
    Alahakoon, Damminda
    Yu, Xinghuo
    2013 IEEE INTERNATIONAL WORKSHOP ON INTELLIGENT ENERGY SYSTEMS (IWIES), 2013, : 40 - 45
  • [8] Big Data Analytics for Electricity Theft Detection in Smart Grids
    Khan, Inam Ullah
    Javaid, Nadeem
    Taylor, C. James
    Gamage, Kelum A. A.
    Ma, Xiandong
    2021 IEEE MADRID POWERTECH, 2021,
  • [9] Analysis and prediction of electricity consumption using smart meter data
    Sauhats, Antans
    Varfolomejeva, Renata
    Linkcvics, Olegs
    Pctrcccnko, Romans
    Kunickis, Maris
    Balodis, Maris
    2015 IEEE 5TH INTERNATIONAL CONFERENCE ON POWER ENGINEERING, ENERGY AND ELECTRICAL DRIVES (POWERENG), 2015, : 17 - 22
  • [10] Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting
    Singh, Shailendra
    Yassine, Abdulsalam
    ENERGIES, 2018, 11 (02)