The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Deep Neural Network Based Demand Side Short Term Load Forecasting
Open accessAbstract
In the smart grid, one of the most important research areas is load forecasting; it spans from traditional time series analyses to recent machine learning approaches and mostly focuses on forecasting aggregated electricity consumption. However, the importance of demand side energy management, including individual load forecasting, is becoming critical. In this paper, we propose deep neural network (DNN)-based load forecasting models and apply them to a demand side empirical load database. DNNs are trained in two different ways: a pre-training restricted Boltzmann machine and using the rectified linear unit without pre-training. DNN forecasting models are trained by individual customer’s electricity consumption data and regional meteorological elements. To verify the performance of DNNs, forecasting results are compared with a shallow neural network (SNN), a double seasonal Holt–Winters (DSHW) model and the autoregressive integrated moving average (ARIMA). The mean absolute percentage error (MAPE) and relative root mean square error (RRMSE) are used for verification. Our results show that DNNs exhibit accurate and robust predictions compared to other forecasting models, e.g., MAPE and RRMSE are reduced by up to 17% and 22% compared to SNN and 9% and 29% compared to DSHW.
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Cited by
Bouktif, S., Fiaz, A., Ouni, A., & Serhani, M. (2018) · Energies · doi:10.3390/en11071636 · Cited by 915
On-Line Building Energy Optimization Using Deep Reinforcement Learning
Mocanu, E., Mocanu, D. C., Nguyen, P. H., Liotta, A., Webber, M. E., Gibescu, M., & Slootweg, J. G. (2019) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2018.2834219 · Cited by 649
Hossain, E., Khan, I., Un-Noor, F., Sikander, S. S., & Sunny, M. S. H. (2019) · IEEE Access · doi:10.1109/access.2019.2894819 · Cited by 513
O’Dwyer, E., Pan, I., Acha, S., & Shah, N. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.01.024 · Cited by 410
Incentive-based demand response for smart grid with reinforcement learning and deep neural network
Lu, R., & Hong, S. H. (2019) · Applied Energy · doi:10.1016/j.apenergy.2018.12.061 · Cited by 394
Metadata
- Authors
- Ryu, S., Noh, J., & Kim, H.
- Venue
- Energies
- Publisher
- MDPI AG
- Published
- 2016-12-22
- Volume
- 10
- Issue
- 1
- Pages
- 3
- ISSN
- 1996-1073 (ISSN-L)
- OpenAlex
- W2562403923
- Cited by
- 409
- References
- 26
- FWCI
- 15.70
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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