The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Deep neural networks for energy load forecasting
Abstract
Smartgrids of the future promise unprecedented flexibility in energy management. Therefore, accurate predictions/forecasts of energy demands (loads) at individual site and aggregate level of the grid is crucial. Despite extensive research, load forecasting remains to be a difficult problem. This paper presents a load forecasting methodology based on deep learning. Specifically, the work presented in this paper investigates the effectiveness of using Convolutional Neural Networks (CNN) for performing energy load forecasting at individual building level. The presented methodology uses convolutions on historical loads. The output from the convolutional operation is fed to fully connected layers together with other pertinent information. The presented methodology was implemented on a benchmark data set of electricity consumption for a single residential customer. Results obtained from the CNN were compared against results obtained by Long Short Term Memories LSTM sequence-to-sequence (LSTM S2S), Factored Restricted Boltzmann Machines (FCRBM), “shallow” Artificial Neural Networks (ANN) and Support Vector Machines (SVM) for the same dataset. Experimental results showed that the CNN outperformed SVR while producing comparable results to the ANN and deep learning methodologies. Further testing is required to compare the performances of different deep learning architectures in load forecasting.
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Cites
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UCI Machine Learning Repository
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“Further, apart from being a major energy consumer by accounting for 20%-40% of the total worldwide energy production [6]-[8], buildings are shown to account for a significant portion of the total energy [9].”
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Deep learning for estimating building energy consumption
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Building energy load forecasting using Deep Neural Networks
Marino, D. L., Amarasinghe, K., & Manic, M. (2016) · IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society · doi:10.1109/iecon.2016.7793413 · Cited by 522
Self-citationHernandez, L., Baladron, C., Aguiar, J. M., Carro, B., Sanchez-Esguevillas, A. J., Lloret, J., & Massana, J. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2014.032014.00094 · Cited by 506
Cited by
Review on the research and practice of deep learning and reinforcement learning in smart grids
China Electric Power Research Institute, Zhang, D., Han, X., Taiyuan University of Technology, Deng, C., & China Electric Power Research Institute (2018) · CSEE Journal of Power and Energy Systems · doi:10.17775/cseejpes.2018.00520 · Cited by 561
Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review
Runge, J., & Zmeureanu, R. (2019) · Energies · doi:10.3390/en12173254 · Cited by 268
Nutkiewicz, A., Yang, Z., & Jain, R. K. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.05.023 · Cited by 223
Short-Term Load Forecasting based on ResNet and LSTM
Choi, H., Ryu, S., & Kim, H. (2018) · 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) · doi:10.1109/smartgridcomm.2018.8587554 · Cited by 103
Metadata
- Authors
- Amarasinghe, K., Marino, D. L., & Manic, M.
- Venue
- 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE)
- Publisher
- IEEE
- Published
- 2017-06
- Pages
- 1483-1488
- OpenAlex
- W2742473260
- Cited by
- 336
- References
- 24
- FWCI
- 42.03
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Amarasinghe, K., Marino, D. L., & Manic, M. (2017). Deep neural networks for energy load forecasting. 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE). https://doi.org/10.1109/isie.2017.8001465
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