Abstract
The active consumers' role in the power and energy market is changing. To deal with the volatile behavior from the Distributed Generation (DG) resources, load flexibility must be provided from the demand side. So, Demand Response (DR) events are triggered. To reduce the response uncertainty, the authors proposed a Trustworthy Rate (TR) to classify the performance of the active consumers in a community. For comparison with previous works' approaches, the innovation from the present study introduces classification methods for deciding the best approach for selecting the DR program participants (load shifting) for specific contexts and reduction goals. From the results, the Decision Tree model created had a lower accuracy performance than the Artificial Neural Network one. To evaluate the effectiveness of the proposed model, the mean absolute error (MAE) was used by the authors.
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