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Forecasting hot water consumption in dwellings using artificial neural networks

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

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Forecasting hot water consumption in dwellings using artificial neural networks. / Gelazanskas, Linas; Gamage, Kelum.
2015 IEEE 5th International Conference on Power Engineering, Energy and Electrical Drives (POWERENG2015). IEEE, 2015. p. 410-415.

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

Harvard

Gelazanskas, L & Gamage, K 2015, Forecasting hot water consumption in dwellings using artificial neural networks. in 2015 IEEE 5th International Conference on Power Engineering, Energy and Electrical Drives (POWERENG2015). IEEE, pp. 410-415, The 5th International Conference on Power Engineering, Energy and Electrical Drives (POWERENG2015), Riga, Latvia, 11/05/15. https://doi.org/10.1109/PowerEng.2015.7266352

APA

Gelazanskas, L., & Gamage, K. (2015). Forecasting hot water consumption in dwellings using artificial neural networks. In 2015 IEEE 5th International Conference on Power Engineering, Energy and Electrical Drives (POWERENG2015) (pp. 410-415). IEEE. https://doi.org/10.1109/PowerEng.2015.7266352

Vancouver

Gelazanskas L, Gamage K. Forecasting hot water consumption in dwellings using artificial neural networks. In 2015 IEEE 5th International Conference on Power Engineering, Energy and Electrical Drives (POWERENG2015). IEEE. 2015. p. 410-415 doi: 10.1109/PowerEng.2015.7266352

Author

Gelazanskas, Linas ; Gamage, Kelum. / Forecasting hot water consumption in dwellings using artificial neural networks. 2015 IEEE 5th International Conference on Power Engineering, Energy and Electrical Drives (POWERENG2015). IEEE, 2015. pp. 410-415

Bibtex

@inproceedings{d1fa69836bbe45c9a4ddeb8ee0395667,
title = "Forecasting hot water consumption in dwellings using artificial neural networks",
abstract = "The electricity grid is currently transforming and becoming more and more decentralised. Green energy generation has many incentives throughout the world thus small renewable generation units become popular. Intermittent generation units pose threat to system stability so new balancing techniques like Demand Side Management must be researched. Residential hot water heaters are perfect candidates to be used for shifting electricity consumption in time. This paper investigates the ability on Artificial Neural Networks to predict individual hot water heater energy demand profile. Data from about a hundred dwellings are analysed using autocorrelation technique. The most appropriate lags were chosen and different Neural Network model topologies were tested and compared. The results are positive and show that water heaters have could potentially shift electric energy.",
author = "Linas Gelazanskas and Kelum Gamage",
year = "2015",
month = may,
day = "13",
doi = "10.1109/PowerEng.2015.7266352",
language = "English",
isbn = "9781467372039",
pages = "410--415",
booktitle = "2015 IEEE 5th International Conference on Power Engineering, Energy and Electrical Drives (POWERENG2015)",
publisher = "IEEE",
note = "The 5th International Conference on Power Engineering, Energy and Electrical Drives (POWERENG2015) ; Conference date: 11-05-2015 Through 13-05-2015",

}

RIS

TY - GEN

T1 - Forecasting hot water consumption in dwellings using artificial neural networks

AU - Gelazanskas, Linas

AU - Gamage, Kelum

PY - 2015/5/13

Y1 - 2015/5/13

N2 - The electricity grid is currently transforming and becoming more and more decentralised. Green energy generation has many incentives throughout the world thus small renewable generation units become popular. Intermittent generation units pose threat to system stability so new balancing techniques like Demand Side Management must be researched. Residential hot water heaters are perfect candidates to be used for shifting electricity consumption in time. This paper investigates the ability on Artificial Neural Networks to predict individual hot water heater energy demand profile. Data from about a hundred dwellings are analysed using autocorrelation technique. The most appropriate lags were chosen and different Neural Network model topologies were tested and compared. The results are positive and show that water heaters have could potentially shift electric energy.

AB - The electricity grid is currently transforming and becoming more and more decentralised. Green energy generation has many incentives throughout the world thus small renewable generation units become popular. Intermittent generation units pose threat to system stability so new balancing techniques like Demand Side Management must be researched. Residential hot water heaters are perfect candidates to be used for shifting electricity consumption in time. This paper investigates the ability on Artificial Neural Networks to predict individual hot water heater energy demand profile. Data from about a hundred dwellings are analysed using autocorrelation technique. The most appropriate lags were chosen and different Neural Network model topologies were tested and compared. The results are positive and show that water heaters have could potentially shift electric energy.

U2 - 10.1109/PowerEng.2015.7266352

DO - 10.1109/PowerEng.2015.7266352

M3 - Conference contribution/Paper

SN - 9781467372039

SP - 410

EP - 415

BT - 2015 IEEE 5th International Conference on Power Engineering, Energy and Electrical Drives (POWERENG2015)

PB - IEEE

T2 - The 5th International Conference on Power Engineering, Energy and Electrical Drives (POWERENG2015)

Y2 - 11 May 2015 through 13 May 2015

ER -