A smart energy distribution and management system for renewable energy distribution and context-aware services based on user patterns and load forecasting

Byun, Jinsung; Hong, Insung; Kang, Byeongkwan; Park, Sehyun

doi:10.1109/tce.2011.5955177

Cited by 93 publications

(47 citation statements)

References 14 publications

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“…So, IBM implemented the IBM TRIRIGA Energy Optimization solution. IBM TRIRIGA Energy Optimization provides [12]: Real-time data gathering and analysis of energy and operational metrics of all infrastructure assets. Some recent studies focused on the energy consumption statistic of both devices of the low voltage and the high voltage.…”

Section: International Journal For Research In Applied Science and Engimentioning

confidence: 99%

Cloud Based Efficient Building Energy Management System

Geetha¹

2017

IJRASET

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In order to achieve energy saving efficiently and provide smart services automatically for users, the architecture of a smart energy monitoring and management system using the Microsoft neural network and computationally non tolling algorithm is proposed in this paper. By monitoring the power consumption information, environment information and users' situation information, the system based on the architecture can calculate the proportion of wasted energy consumption based on the energy consumption statistic, provide smart services based on the person-device interaction, and forecast the energy consumption based on the user energy consumption behaviors. The system is combined with cloud computing for data storage and processing. This paper describes the design and implementation of the system architecture.

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Section: International Journal For Research In Applied Science and Engimentioning

confidence: 99%

Cloud Based Efficient Building Energy Management System

Geetha¹

2017

IJRASET

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“…[3] [4]proposed an intelligent system with some sensors in each domain to aware the user and environment information for building energy saving and context-aware smart services. This system uses the context-aware technology to control electrical devices based on the information of user's situation, environment, and sensor management for building energy saving.…”

Section: Related Workmentioning

confidence: 99%

“…[3] [4]proposed an intelligent system for building energy saving and context-aware smart services. The system achieves energy saving by monitoring the user situation and controlling the electrical devices automatically.…”

Section: Introductionmentioning

confidence: 99%

A System Architecture for Energy Saving in Buildings

Shi¹,

Liu²,

Xing³

et al. 2012

Proceedings of the 2012 2nd International Conference on Computer and Information Applications (ICCIA 2012)

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Abstract-Advances in smart grid technologies have been proven to improve energy efficiency. These technologies will drive significant changes of energy consumption operation, energy management, energy using patterns, and customer services. In order to achieve energy saving efficiently and provide smart services automatically for users, the architecture of a smart energy monitoring and management system is proposed in this paper. By monitoring the power consumption information, environment information and users' situation information, the system based on the architecture can calculate the proportion of wasted energy consumption based on the energy consumption statistic, provide smart services based on the person-device interaction, and forecast the energy consumption based on the user energy consumption behaviors. The system is combined with cloud computing for data storage and processing. This paper describes the design and partial implementation of the system architecture.

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“…The idea behind DLC is to enable access to devices which can have a significant power usage and therefore be controlled to lower power consumption on a the local power grid or when having an overproduction of power, to be able to turn on the devices to take away load on the grid [2]. Devices which can be used to shift load on the grid are called distributed energy resources, (D.E.R), because they are distributed resources and some of them belong to customers [1].…”

Section: Introductionmentioning

confidence: 99%

Bandwidth analysis of smart meter network infrastructure

Balachandran

Olsen

Pedersen

2014

16th International Conference on Advanced Communication Technology

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Abstract-Advanced Metering Infrastructure (AMI) is a network infrastructure in Smart Grid, which links electricity customers to utility company. Compared to a network where different amount of data is send, AMI network sends small amount of data at different time intervals. This network enables smart services by making it possible for the utility company to get an overview of their customers power consumption and also control devices in their costumers household e.g. heat pumps. The problem investigated in this paper is what bandwidth requirements can be expected when implementing such network to utilize smart meters and which existing broadband network technologies can facilitate this smart meter service. Initially, scenarios for smart meter infrastructure are identified. The paper defines abstraction models which cover the AMI scenarios. A general overview of the bandwidth requirements are analysed. For this analysis assumptions and limitations are defined. The results show the bandwidth provided by GPRS technology is sufficient. However, from a delay point of view, GPRS has its limits, therefore, xDSL or 3G can be used for faster transfer but will rarely utilize the available bandwidth from these technologies. The aggregator which is responsible to collect all the data from the different smart meters can become a bottleneck as the aggregated amount of data is high, thus the bandwidth for the data concentrator has to be high. These results are interesting to look at, as they show there are different bandwidth requirements from a smart meter and aggregator point of view and that, the aggregator is a vital element in the network and it has to be studied further.

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A smart energy distribution and management system for renewable energy distribution and context-aware services based on user patterns and load forecasting

Cited by 93 publications

References 14 publications

Cloud Based Efficient Building Energy Management System

Cloud Based Efficient Building Energy Management System

A System Architecture for Energy Saving in Buildings

Bandwidth analysis of smart meter network infrastructure

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