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Open AccessArticle

Data-Driven Prediction of Load Curtailment in Incentive-Based Demand Response System

Korea Electrotechnology Research Institute, Ansan 15588, Korea
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Energies 2018, 11(11), 2905; https://doi.org/10.3390/en11112905
Received: 1 October 2018 / Revised: 22 October 2018 / Accepted: 24 October 2018 / Published: 25 October 2018
(This article belongs to the Collection Smart Grid)
Demand response, in which energy customers reduce their energy consumption at the request of service providers, is spreading as a new technology. However, the amount of load curtailment from each customer is uncertain. This is because an energy customer can freely decide to reduce his energy consumption or not in the current liberalized energy market. Because this uncertainty can cause serious problems in a demand response system, it is clear that the amount of energy reduction should be predicted and managed. In this paper, a data-driven prediction method of load curtailment is proposed, considering two difficulties in the prediction. The first problem is that the data is very sparse. Each customer receives a request for load curtailment only a few times a year. Therefore, the k-nearest neighbor method, which requires a relatively small amount of data, is mainly used in our proposed method. The second difficulty is that the characteristic of each customer is so different that a single prediction method cannot cover all the customers. A prediction method that provides remarkable prediction performance for one customer may provide a poor performance for other customers. As a result, the proposed prediction method adopts a weighted ensemble model to apply different models for different customers. The confidence of each sub-model is defined and used as a weight in the ensemble. The prediction is fully based on the electricity consumption data and the history of demand response events without demanding any other additional internal information from each customer. In the experiment, real data obtained from demand response service providers verifies that the proposed framework is suitable for the prediction of each customer’s load curtailment. View Full-Text
Keywords: demand response; prediction of load curtailment; prediction of demand response demand response; prediction of load curtailment; prediction of demand response
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MDPI and ACS Style

Kang, J.; Lee, S. Data-Driven Prediction of Load Curtailment in Incentive-Based Demand Response System. Energies 2018, 11, 2905. https://doi.org/10.3390/en11112905

AMA Style

Kang J, Lee S. Data-Driven Prediction of Load Curtailment in Incentive-Based Demand Response System. Energies. 2018; 11(11):2905. https://doi.org/10.3390/en11112905

Chicago/Turabian Style

Kang, Jimyung; Lee, Soonwoo. 2018. "Data-Driven Prediction of Load Curtailment in Incentive-Based Demand Response System" Energies 11, no. 11: 2905. https://doi.org/10.3390/en11112905

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