| Title | DRUMS: Demand Response Management in a Smart City Using Deep Learning and SVR |
|---|---|
| ID_Doc | 36672 |
| Authors | Jindal, A; Aujla, GS; Kumar, N; Prodan, R; Obaidat, MS |
| Title | DRUMS: Demand Response Management in a Smart City Using Deep Learning and SVR |
| Year | 2018 |
| Published | |
| Abstract | Demand response management in smart cities is one of the most challenging tasks to be performed due to the continuous changes in the load profile of the home users. The existing proposals in the literature fail to observe the hidden patterns in the load profile of these users. So, to fill these gaps, the concept of deep learning has been used in this paper for smart energy management in a smart city. The consumption data from smart homes (SHs) is gathered and taken as an input to the deep learning model, convolution neural network (CNN). The CNN model learns the hidden patterns in the data and outputs different load curves. These load curves are then used to train a support vector regression (SVR) model, which predicts the overall load consumption of all SHs in the smart city. This prediction is then compared with the power generation from the grid and consequently the demand response (DR) of the connected SHs is managed so as to minimize the gap between predicted demand and supply. The proposed scheme has been evaluated on the dataset collected from PJM and open energy information with respect to load demand prediction and DR management. The results obtained prove the efficacy of the proposed scheme. The prediction errors, i.e., root mean squared error and mean absolute percentage error are observed less in comparison to the cases when CNN and SVR are used individually. |
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