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Predicting consumer load profiles using commercial and open data

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Abstract
Automated Metering Infrastructure (AMI) has gradually become commonplace within the utilities industry and has brought with it numerous improvements in all related fields. Specifically in tariff setting and demand response models, classification of smart meter readings into load profiles helps in finding the right segments to target. This paper addresses the issue of assigning new customers, for whom no AMI readings are available, to one of these load profiles. This post-clustering phase has received little attention in the past. Our framework combines commercial, government and open data with the internal company data to accurately predict the load profile of a new customer using high performing classification models. The daily load profiles are generated using Spectral Clustering and are used as the dependent variable in our model. The framework was tested on over 6000 customers from GDF SUEZ in Belgium and six relevant load profiles were identified. The results show that the combination of internal data with commercial and cartographic data achieves the highest accuracy. Using external data alone, the model was still able to adequately place customers into their relevant load profile.

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Please use this url to cite or link to this publication:

MLA
Vercamer, Dauwe et al. “Predicting Consumer Load Profiles Using Commercial and Open Data.” IEEE TRANSACTIONS ON POWER SYSTEMS 31.5 (2016): 3693–3701. Print.
APA
Vercamer, D., Steurtewagen, B., Van den Poel, D., & Vermeulen, F. (2016). Predicting consumer load profiles using commercial and open data. IEEE TRANSACTIONS ON POWER SYSTEMS, 31(5), 3693–3701.
Chicago author-date
Vercamer, Dauwe, Bram Steurtewagen, Dirk Van den Poel, and Frank Vermeulen. 2016. “Predicting Consumer Load Profiles Using Commercial and Open Data.” Ieee Transactions on Power Systems 31 (5): 3693–3701.
Chicago author-date (all authors)
Vercamer, Dauwe, Bram Steurtewagen, Dirk Van den Poel, and Frank Vermeulen. 2016. “Predicting Consumer Load Profiles Using Commercial and Open Data.” Ieee Transactions on Power Systems 31 (5): 3693–3701.
Vancouver
1.
Vercamer D, Steurtewagen B, Van den Poel D, Vermeulen F. Predicting consumer load profiles using commercial and open data. IEEE TRANSACTIONS ON POWER SYSTEMS. 2016;31(5):3693–701.
IEEE
[1]
D. Vercamer, B. Steurtewagen, D. Van den Poel, and F. Vermeulen, “Predicting consumer load profiles using commercial and open data,” IEEE TRANSACTIONS ON POWER SYSTEMS, vol. 31, no. 5, pp. 3693–3701, 2016.
@article{8523655,
  abstract     = {Automated Metering Infrastructure (AMI) has gradually become commonplace within the utilities industry and has brought with it numerous improvements in all related fields. Specifically in tariff setting and demand response models, classification of smart meter readings into load profiles helps in finding the right segments to target. This paper addresses the issue of assigning new customers, for whom no AMI readings are available, to one of these load profiles. This post-clustering phase has received little attention in the past. Our framework combines commercial, government and open data with the internal company data to accurately predict the load profile of a new customer using high performing classification models. The daily load profiles are generated using Spectral Clustering and are used as the dependent variable in our model. The framework was tested on over 6000 customers from GDF SUEZ in Belgium and six relevant load profiles were identified. The results show that the combination of internal data with commercial and cartographic data achieves the highest accuracy. Using external data alone, the model was still able to adequately place customers into their relevant load profile.},
  author       = {Vercamer, Dauwe and Steurtewagen, Bram and Van den Poel, Dirk and Vermeulen, Frank},
  issn         = {0885-8950},
  journal      = {IEEE TRANSACTIONS ON POWER SYSTEMS},
  language     = {eng},
  number       = {5},
  pages        = {3693--3701},
  title        = {Predicting consumer load profiles using commercial and open data},
  url          = {http://dx.doi.org/10.1109/TPWRS.2015.2493083},
  volume       = {31},
  year         = {2016},
}

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