The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
A Survey on Electric Power Demand Forecasting: Future Trends in Smart Grids, Microgrids and Smart Buildings
Open accessAbstract
Recently there has been a significant proliferation in the use of forecasting techniques, mainly due to the increased availability and power of computation systems and, in particular, to the usage of personal computers. This is also true for power network systems, where energy demand forecasting has been an important field in order to allow generation planning and adaptation. Apart from the quantitative progression, there has also been a change in the type of models proposed and used. In the `70s, the usage of non-linear techniques was generally not popular among scientists and engineers. However, in the last two decades they have become very important techniques in solving complex problems which would be very difficult to tackle otherwise. With the recent emergence of smart grids, new environments have appeared capable of integrating demand, generation, and storage. These employ intelligent and adaptive elements that require more advanced techniques for accurate and precise demand and generation forecasting in order to work optimally. This review discusses the most relevant studies on electric demand prediction over the last 40 years, and presents the different models used as well as the future trends. Additionally, it analyzes the latest studies on demand forecasting in the future environments that emerge from the usage of smart grids.
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Cites
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Cited by
Bouktif, S., Fiaz, A., Ouni, A., & Serhani, M. (2018) · Energies · doi:10.3390/en11071636 · Cited by 915
Hoang, A. T., Pham, V. V., & Nguyen, X. P. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.127161 · Cited by 663
Deep neural networks for energy load forecasting
Amarasinghe, K., Marino, D. L., & Manic, M. (2017) · 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE) · doi:10.1109/isie.2017.8001465 · Cited by 336
Smart (Electricity) Grids for Smart Cities: Assessing Roles and Societal Impacts
Masera, M., Bompard, E. F., Profumo, F., & Hadjsaid, N. (2018) · Proceedings of the IEEE · doi:10.1109/jproc.2018.2812212 · Cited by 185
Pinheiro, M. G., Madeira, S. C., & Francisco, A. P. (2023) · Applied Energy · doi:10.1016/j.apenergy.2022.120493 · Cited by 119
Metadata
- Authors
- Hernandez, L., Baladron, C., Aguiar, J. M., Carro, B., Sanchez-Esguevillas, A. J., Lloret, J., & Massana, J.
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2014
- Volume
- 16
- Issue
- 3
- Pages
- 1460-1495
- ISSN
- 1553-877X (ISSN-L)
- OpenAlex
- W2144335970
- Cited by
- 506
- References
- 4
- FWCI
- 16.27
- Type
- journal article
- Language
- en
- Open access
- green
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- Hernandez, L., Baladron, C., Aguiar, J. M., Carro, B., Sanchez-Esguevillas, A. J., Lloret, J., & Massana, J. (2014). A Survey on Electric Power Demand Forecasting: Future Trends in Smart Grids, Microgrids and Smart Buildings. IEEE Communications Surveys & Tutorials. https://doi.org/10.1109/surv.2014.032014.00094
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