Abstract
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.
Highlights
The smart grid, which is the combination of an electricity network and information network, provides many new applications in electricity systems
To solve the abovementioned difficulties, in this paper we propose a k-nearest neighbor (k-NN)-based ensemble method
We compared our method to various well‐known machine learning methods such as linear regression, neural network, machine, and even convolutional neural network to assess the performance of the proposed method
Summary
The smart grid, which is the combination of an electricity network and information network, provides many new applications in electricity systems. It is not easy to implement those methods because their descriptions are not adequate and the target customers are different from those of our system Their targets are university campus buildings, 2018, 11, x while ours are various demand response customers including factories, 9shopping of 14. We compared our well-known methods as linear neural network, support vector factories, shoppinglearning centers, and even a such fish farm, located across a nation. We compared our method to various well‐known machine learning methods such as linear regression, neural network, machine, and even convolutional neural network to assess the performance of the proposed method. Method to various well‐known machine learning methods such as linear regression, neural network, support vector machine, and even convolutional neural network to assess the performance of the.
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