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Neural network based real-time pricing in demand side management for future smart grid

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Neural network based real-time pricing in demand side management for future smart grid. / Gelazanskas, Linas; Gamage, Kelum.
Power Electronics, Machines and Drives (PEMD 2014), 7th IET International Conference on. IEEE, 2015. p. 1-5.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Gelazanskas, L & Gamage, K 2015, Neural network based real-time pricing in demand side management for future smart grid. in Power Electronics, Machines and Drives (PEMD 2014), 7th IET International Conference on. IEEE, pp. 1-5, The 7th IET international conference on power electronics, machines and drives (PEMD 2014), Manchester, United Kingdom, 8/04/14. https://doi.org/10.1049/cp.2014.0346

APA

Gelazanskas, L., & Gamage, K. (2015). Neural network based real-time pricing in demand side management for future smart grid. In Power Electronics, Machines and Drives (PEMD 2014), 7th IET International Conference on (pp. 1-5). IEEE. https://doi.org/10.1049/cp.2014.0346

Vancouver

Gelazanskas L, Gamage K. Neural network based real-time pricing in demand side management for future smart grid. In Power Electronics, Machines and Drives (PEMD 2014), 7th IET International Conference on. IEEE. 2015. p. 1-5 doi: 10.1049/cp.2014.0346

Author

Gelazanskas, Linas ; Gamage, Kelum. / Neural network based real-time pricing in demand side management for future smart grid. Power Electronics, Machines and Drives (PEMD 2014), 7th IET International Conference on. IEEE, 2015. pp. 1-5

Bibtex

@inproceedings{bce6f42eb02247c4871c689a357a6b41,
title = "Neural network based real-time pricing in demand side management for future smart grid",
abstract = "Electricity grid is currently being transformed into smart grid. Increased number of renewables require more and more ancillary services to backup intermittent power generation. A very important topic in tomorrow's electricity grid is demand side management. This tool should be used as an alternative for traditional backup power reserves. It requires a deep understanding on how consumption depends on dynamic pricing. This paper proposes a method for modelling the electricity demand response to a real-time pricing. A virtual smart house is modelled using Gridlab-D smart grid simulator. The HVAC system is setup to respond to real-time price sent by the utility. The paper is analysing the ability of neural network to predict the exact price, which is sent to the end user in order to maintain the supply balance in the system. It should also reduce the peaks in demand and increase system resilience.",
keywords = "Smart Grid, Demand Side Management, Neural Networks",
author = "Linas Gelazanskas and Kelum Gamage",
year = "2015",
month = apr,
day = "10",
doi = "10.1049/cp.2014.0346",
language = "English",
pages = "1--5",
booktitle = "Power Electronics, Machines and Drives (PEMD 2014), 7th IET International Conference on",
publisher = "IEEE",
note = "The 7th IET international conference on power electronics, machines and drives (PEMD 2014) ; Conference date: 08-04-2014 Through 10-04-2014",

}

RIS

TY - GEN

T1 - Neural network based real-time pricing in demand side management for future smart grid

AU - Gelazanskas, Linas

AU - Gamage, Kelum

PY - 2015/4/10

Y1 - 2015/4/10

N2 - Electricity grid is currently being transformed into smart grid. Increased number of renewables require more and more ancillary services to backup intermittent power generation. A very important topic in tomorrow's electricity grid is demand side management. This tool should be used as an alternative for traditional backup power reserves. It requires a deep understanding on how consumption depends on dynamic pricing. This paper proposes a method for modelling the electricity demand response to a real-time pricing. A virtual smart house is modelled using Gridlab-D smart grid simulator. The HVAC system is setup to respond to real-time price sent by the utility. The paper is analysing the ability of neural network to predict the exact price, which is sent to the end user in order to maintain the supply balance in the system. It should also reduce the peaks in demand and increase system resilience.

AB - Electricity grid is currently being transformed into smart grid. Increased number of renewables require more and more ancillary services to backup intermittent power generation. A very important topic in tomorrow's electricity grid is demand side management. This tool should be used as an alternative for traditional backup power reserves. It requires a deep understanding on how consumption depends on dynamic pricing. This paper proposes a method for modelling the electricity demand response to a real-time pricing. A virtual smart house is modelled using Gridlab-D smart grid simulator. The HVAC system is setup to respond to real-time price sent by the utility. The paper is analysing the ability of neural network to predict the exact price, which is sent to the end user in order to maintain the supply balance in the system. It should also reduce the peaks in demand and increase system resilience.

KW - Smart Grid

KW - Demand Side Management

KW - Neural Networks

U2 - 10.1049/cp.2014.0346

DO - 10.1049/cp.2014.0346

M3 - Conference contribution/Paper

SP - 1

EP - 5

BT - Power Electronics, Machines and Drives (PEMD 2014), 7th IET International Conference on

PB - IEEE

T2 - The 7th IET international conference on power electronics, machines and drives (PEMD 2014)

Y2 - 8 April 2014 through 10 April 2014

ER -