The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting
Open accessAbstract
The ability to predict short-term electric energy demand would provide several benefits, both at the economic and environmental level. For example, it would allow for an efficient use of resources in order to face the actual demand, reducing the costs associated to the production as well as the emission of CO 2 . To this aim, in this paper we propose a strategy based on ensemble learning in order to tackle the short-term load forecasting problem. In particular, our approach is based on a stacking ensemble learning scheme, where the predictions produced by three base learning methods are used by a top level method in order to produce final predictions. We tested the proposed scheme on a dataset reporting the energy consumption in Spain over more than nine years. The obtained experimental results show that an approach for short-term electricity consumption forecasting based on ensemble learning can help in combining predictions produced by weaker learning methods in order to obtain superior results. In particular, the system produces a lower error with respect to the existing state-of-the art techniques used on the same dataset. More importantly, this case study has shown that using an ensemble scheme can achieve very accurate predictions, and thus that it is a suitable approach for addressing the short-term load forecasting problem.
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A deep learning framework for building energy consumption forecast
Somu, N., Raman M R, G., & Ramamritham, K. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110591 · Cited by 500
Khalil, M., McGough, A. S., Pourmirza, Z., Pazhoohesh, M., & Walker, S. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105287 · Cited by 299
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Ariza-Colpas, P. P., Vicario, E., Oviedo-Carrascal, A. I., Butt Aziz, S., Piñeres-Melo, M. A., Quintero-Linero, A., & Patara, F. (2022) · Sensors · doi:10.3390/s22093401 · Cited by 52
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Metadata
- Authors
- Divina, F., Gilson, A., Goméz-Vela, F., García Torres, M., & Torres, J.
- Venue
- Energies
- Publisher
- MDPI AG
- Published
- 2018-04-16
- Volume
- 11
- Issue
- 4
- Pages
- 949
- ISSN
- 1996-1073 (ISSN-L)
- OpenAlex
- W2797373921
- Cited by
- 299
- References
- 55
- FWCI
- 7.90
- Type
- journal article
- Language
- en
- Open access
- gold
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