Using Supervised Learning to Assign New Consumers to Demand Response Programs According to the Context
| Title | Using Supervised Learning to Assign New Consumers to Demand Response Programs According to the Context |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Silva, Cátia, Faria, Pedro, Vale, Zita |
| Conference Name | 2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) |
| Keywords | Active Consumers, Collaboration, Decision Tree, demand response, Policy Based Governance, privacy, pubcrawl, Random Forest, Schedules, security, Sensitivity, smart grid consumer privacy, Smart grids, Spatial Flexibility, supervised learning, Uncertainty |
| Abstract | Active consumers have now been empowered thanks to the smart grid concept. To avoid fossil fuels, the demand side must provide flexibility through Demand Response events. However, selecting the proper participants for an event can be complex due to response uncertainty. The authors design a Contextual Consumer Rate to identify the trustworthy participants according to previous performances. In the present case study, the authors address the problem of new players with no information. In this way, two different methods were compared to predict their rate. Besides, the authors also refer to the consumer privacy testing of the dataset with and without information that could lead to the participant identification. The results found to prove that, for the proposed methodology, private information does not have a high impact to attribute a rate. |
| DOI | 10.1109/EEEIC/ICPSEurope54979.2022.9854646 |
| Citation Key | silva_using_2022 |