As energy demand continues to increase, smart grid systems that perform efficient energy management become increasingly important due to environmental and cost reasons. It requires faster prediction of electric energy consumption and valid explanation of the predicted results. Recently, several demand predictors based on deep learning that can deal with complex features of data are actively investigated, but most of them suffer from lack of explanation due to the black-box characteristics. In this paper, we propose a hybrid autoencoder-based deep learning model that predicts power demand in minutes and also provides the explanation for the predicted results. It consists of an information projector that uses auxiliary information to extract features for the current situation and a model that predicts future power demand. This model exploits the latent space composed of the two different modalities to account for the prediction. Experiments with household electric power demand data collected over five years show that the proposed model is the best with a mean squared error of 0.3764. In addition, by analyzing the latent variables extracted by the information projector, the correlation with various conditions including the power demand is confirmed to provide the reason of the coming power demand predicted.
|Title of host publication||15th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2020|
|Editors||Álvaro Herrero, Carlos Cambra, Daniel Urda, Javier Sedano, Héctor Quintián, Emilio Corchado|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||11|
|Publication status||Published - 2021|
|Event||15th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2020 - Burgos, Spain|
Duration: 2020 Sep 16 → 2020 Sep 18
|Name||Advances in Intelligent Systems and Computing|
|Conference||15th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2020|
|Period||20/9/16 → 20/9/18|
Bibliographical noteFunding Information:
This research was supported by Korea Electric Power Corporation (Grant number: R18XA05). J. Y. Kim has been supported by NRF (National Research Foundation of Korea) grant funded by the Korean government (NRF-2019-Fostering Core Leaders of the Future Basic Science Program/Global Ph.D. Fellowship Program).
© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Computer Science(all)