Cloud-Based Software Platform for Smart Meter Data Management

Xiufeng Liu; Per Sieverts Nielsen

Abstract

Today smart meters are increasingly used in the worldwide. Smart meters are the advanced meters capable of measuring customer energy consumption at a fine-grained time in- terval, e.g., every 15 minutes. The data are very sizeable, and might be from different sources, along with the other social-economic metrics such as the geographic information of meters, the information about users and their property, geographic location and others, which make the data management very complex. On the other hand, data-mining and the emerging cloud computing technologies make the collection, management, and analysis of the so-called big data possible. This can improve energy management, e.g., help utility companies to forecast energy loads and improve services, and help households to manage energy usage and save money. As this regard, the proposed paper focuses on building an innovative software platform for smart meter data analytics using cloud technologies, aiming to maximize the information assets in demand-side energy management and relieving peak load. The proposed platform will oer information integration pipeline to ingest smart meter time-series data; a secure repository for researchers sharing their knowledge; scalable data analytics platform for data mining over big data sets for energy demand forecasting and consumption discovering; data as the service for other applications using smart meter data; and a portal for visualizing data analytics results. The design will incorporate hybrid clouds, including Infrastructure as a Service (IaaS) and Platform as a Service (PaaS), which are suitable for on-demand provisioning, massive scaling, and manageability. Besides, the design will impose extensibility, eciency, and high availability on the system. The paper will evaluate the system comprehensively, and compare with other similar works. This paper will provide a proof of concept for building the data management system expanding from the data management of energy sector to the entire sectors of smart cities.

Original language	English
Publication date	2015
Publication status	Published - 2015
Event	10th Conference on Sustainable Development of Energy, Water and Environment Systems - Dubrovnik, Croatia Duration: 27 Sep 2015 → 2 Oct 2015 Conference number: 10 http://www.dubrovnik2015.sdewes.org/index.php

Conference

Conference	10th Conference on Sustainable Development of Energy, Water and Environment Systems
Number	10
Country/Territory	Croatia
City	Dubrovnik
Period	27/09/2015 → 02/10/2015
Internet address	http://www.dubrovnik2015.sdewes.org/index.php

Keywords

Cloud-based
Platform
Smart meter
Data managment

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

1 Participating in or organising a conference

10th Conference on Sustainable Development of Energy, Water and Environment Systems
Xiufeng Liu (Participant)
1 Feb 2015
Activity: Attending an event › Participating in or organising a conference

Cite this

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title = "Cloud-Based Software Platform for Smart Meter Data Management",

abstract = "Today smart meters are increasingly used in the worldwide. Smart meters are the advanced meters capable of measuring customer energy consumption at a fine-grained time in- terval, e.g., every 15 minutes. The data are very sizeable, and might be from different sources, along with the other social-economic metrics such as the geographic information of meters, the information about users and their property, geographic location and others, which make the data management very complex. On the other hand, data-mining and the emerging cloud computing technologies make the collection, management, and analysis of the so-called big data possible. This can improve energy management, e.g., help utility companies to forecast energy loads and improve services, and help households to manage energy usage and save money. As this regard, the proposed paper focuses on building an innovative software platform for smart meter data analytics using cloud technologies, aiming to maximize the information assets in demand-side energy management and relieving peak load. The proposed platform will oer information integration pipeline to ingest smart meter time-series data; a secure repository for researchers sharing their knowledge; scalable data analytics platform for data mining over big data sets for energy demand forecasting and consumption discovering; data as the service for other applications using smart meter data; and a portal for visualizing data analytics results. The design will incorporate hybrid clouds, including Infrastructure as a Service (IaaS) and Platform as a Service (PaaS), which are suitable for on-demand provisioning, massive scaling, and manageability. Besides, the design will impose extensibility, eciency, and high availability on the system. The paper will evaluate the system comprehensively, and compare with other similar works. This paper will provide a proof of concept for building the data management system expanding from the data management of energy sector to the entire sectors of smart cities.",

keywords = "Cloud-based, Platform, Smart meter, Data managment",

author = "Xiufeng Liu and Nielsen, {Per Sieverts}",

year = "2015",

language = "English",

note = "10th Conference on Sustainable Development of Energy, Water and Environment Systems ; Conference date: 27-09-2015 Through 02-10-2015",

url = "http://www.dubrovnik2015.sdewes.org/index.php",

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TY - ABST

T1 - Cloud-Based Software Platform for Smart Meter Data Management

AU - Liu, Xiufeng

AU - Nielsen, Per Sieverts

N1 - Conference code: 10

PY - 2015

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N2 - Today smart meters are increasingly used in the worldwide. Smart meters are the advanced meters capable of measuring customer energy consumption at a fine-grained time in- terval, e.g., every 15 minutes. The data are very sizeable, and might be from different sources, along with the other social-economic metrics such as the geographic information of meters, the information about users and their property, geographic location and others, which make the data management very complex. On the other hand, data-mining and the emerging cloud computing technologies make the collection, management, and analysis of the so-called big data possible. This can improve energy management, e.g., help utility companies to forecast energy loads and improve services, and help households to manage energy usage and save money. As this regard, the proposed paper focuses on building an innovative software platform for smart meter data analytics using cloud technologies, aiming to maximize the information assets in demand-side energy management and relieving peak load. The proposed platform will oer information integration pipeline to ingest smart meter time-series data; a secure repository for researchers sharing their knowledge; scalable data analytics platform for data mining over big data sets for energy demand forecasting and consumption discovering; data as the service for other applications using smart meter data; and a portal for visualizing data analytics results. The design will incorporate hybrid clouds, including Infrastructure as a Service (IaaS) and Platform as a Service (PaaS), which are suitable for on-demand provisioning, massive scaling, and manageability. Besides, the design will impose extensibility, eciency, and high availability on the system. The paper will evaluate the system comprehensively, and compare with other similar works. This paper will provide a proof of concept for building the data management system expanding from the data management of energy sector to the entire sectors of smart cities.

AB - Today smart meters are increasingly used in the worldwide. Smart meters are the advanced meters capable of measuring customer energy consumption at a fine-grained time in- terval, e.g., every 15 minutes. The data are very sizeable, and might be from different sources, along with the other social-economic metrics such as the geographic information of meters, the information about users and their property, geographic location and others, which make the data management very complex. On the other hand, data-mining and the emerging cloud computing technologies make the collection, management, and analysis of the so-called big data possible. This can improve energy management, e.g., help utility companies to forecast energy loads and improve services, and help households to manage energy usage and save money. As this regard, the proposed paper focuses on building an innovative software platform for smart meter data analytics using cloud technologies, aiming to maximize the information assets in demand-side energy management and relieving peak load. The proposed platform will oer information integration pipeline to ingest smart meter time-series data; a secure repository for researchers sharing their knowledge; scalable data analytics platform for data mining over big data sets for energy demand forecasting and consumption discovering; data as the service for other applications using smart meter data; and a portal for visualizing data analytics results. The design will incorporate hybrid clouds, including Infrastructure as a Service (IaaS) and Platform as a Service (PaaS), which are suitable for on-demand provisioning, massive scaling, and manageability. Besides, the design will impose extensibility, eciency, and high availability on the system. The paper will evaluate the system comprehensively, and compare with other similar works. This paper will provide a proof of concept for building the data management system expanding from the data management of energy sector to the entire sectors of smart cities.

KW - Cloud-based

KW - Platform

KW - Smart meter

KW - Data managment

M3 - Conference abstract for conference

T2 - 10th Conference on Sustainable Development of Energy, Water and Environment Systems

Y2 - 27 September 2015 through 2 October 2015

ER -

Cloud-Based Software Platform for Smart Meter Data Management

Abstract

Conference

Keywords

UN SDGs

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Activities

10th Conference on Sustainable Development of Energy, Water and Environment Systems

Cite this