Day-ahead residential load forecasting with artificial neural network using smart meter data

B. Asare-Bediako, W.L. Kling, P.F. Ribeiro

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

9 Citations (Scopus)

Abstract

Load forecasting is an important operational procedure for the electric industry particularly in a liberalized, deregulated environment. It enables the prediction of utilization of assets, provides input for load/supply balancing and supports optimal energy utilization. Current residential load forecasting is mainly based on the use of synthetic load profiles due to lack of or insufficient historical data. However, the advent of smart meters presents an opportunity for making accurate residential load forecasting possible. In this paper artificial neural networks are used with weather data and historical smart meter data for day-ahead load prediction. Extensive error analyses are performed on the model to investigate the suitability of the model for day-ahead prediction. The forecast model can be implemented by energy suppliers and distributed system operators for submission of day-ahead bids and for management of network assets respectively.
Original languageEnglish
Title of host publicationProceedings of the 2013 IEEE Grenoble PowerTech (POWERTECH), 16-20 June 2013, Grenoble, France
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages1-6
DOIs
Publication statusPublished - 2013
Event2013 IEEE Power Tech - Grenoble, Switzerland
Duration: 16 Jun 201320 Jun 2013

Conference

Conference2013 IEEE Power Tech
CountrySwitzerland
CityGrenoble
Period16/06/1320/06/13

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Smart meters
Neural networks
Electric industry
Energy utilization

Cite this

Asare-Bediako, B., Kling, W. L., & Ribeiro, P. F. (2013). Day-ahead residential load forecasting with artificial neural network using smart meter data. In Proceedings of the 2013 IEEE Grenoble PowerTech (POWERTECH), 16-20 June 2013, Grenoble, France (pp. 1-6). Piscataway: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/PTC.2013.6652093
Asare-Bediako, B. ; Kling, W.L. ; Ribeiro, P.F. / Day-ahead residential load forecasting with artificial neural network using smart meter data. Proceedings of the 2013 IEEE Grenoble PowerTech (POWERTECH), 16-20 June 2013, Grenoble, France. Piscataway : Institute of Electrical and Electronics Engineers, 2013. pp. 1-6
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Asare-Bediako, B, Kling, WL & Ribeiro, PF 2013, Day-ahead residential load forecasting with artificial neural network using smart meter data. in Proceedings of the 2013 IEEE Grenoble PowerTech (POWERTECH), 16-20 June 2013, Grenoble, France. Institute of Electrical and Electronics Engineers, Piscataway, pp. 1-6, 2013 IEEE Power Tech, Grenoble, Switzerland, 16/06/13. https://doi.org/10.1109/PTC.2013.6652093

Day-ahead residential load forecasting with artificial neural network using smart meter data. / Asare-Bediako, B.; Kling, W.L.; Ribeiro, P.F.

Proceedings of the 2013 IEEE Grenoble PowerTech (POWERTECH), 16-20 June 2013, Grenoble, France. Piscataway : Institute of Electrical and Electronics Engineers, 2013. p. 1-6.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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AB - Load forecasting is an important operational procedure for the electric industry particularly in a liberalized, deregulated environment. It enables the prediction of utilization of assets, provides input for load/supply balancing and supports optimal energy utilization. Current residential load forecasting is mainly based on the use of synthetic load profiles due to lack of or insufficient historical data. However, the advent of smart meters presents an opportunity for making accurate residential load forecasting possible. In this paper artificial neural networks are used with weather data and historical smart meter data for day-ahead load prediction. Extensive error analyses are performed on the model to investigate the suitability of the model for day-ahead prediction. The forecast model can be implemented by energy suppliers and distributed system operators for submission of day-ahead bids and for management of network assets respectively.

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Asare-Bediako B, Kling WL, Ribeiro PF. Day-ahead residential load forecasting with artificial neural network using smart meter data. In Proceedings of the 2013 IEEE Grenoble PowerTech (POWERTECH), 16-20 June 2013, Grenoble, France. Piscataway: Institute of Electrical and Electronics Engineers. 2013. p. 1-6 https://doi.org/10.1109/PTC.2013.6652093