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Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001
Abstract
Load forecasting is usually made by constructing models on relative information, such as climate and previous load demand data. In 2001, EUNITE network organized a competition aiming at mid-term load forecasting (predicting daily maximum load of the next 31 days). During the competition we proposed a support vector machine (SVM) model, which was the winning entry, to solve the problem. In this paper, we discuss in detail how SVM, a new learning technique, is successfully applied to load forecasting. In addition, motivated by the competition results and the approaches by other participants, more experiments and deeper analyses are conducted and presented here. Some important conclusions from the results are that temperature (or other types of climate information) might not be useful in such a mid-term load forecasting problem and that the introduction of time-series concept may improve the forecasting.
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Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
Self-citation
Cited by
Electric load forecasting methods: Tools for decision making
Hahn, H., Meyer-Nieberg, S., & Pickl, S. (2009) · European Journal of Operational Research · doi:10.1016/j.ejor.2009.01.062 · Cited by 549
Short-term load forecasting using lifting scheme and ARIMA models
Lee, C.-M., & Ko, C.-N. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.11.033 · Cited by 438
A Novel Hybrid Short-Term Load Forecasting Method of Smart Grid Using MLR and LSTM Neural Network
Li, J., Deng, D., Zhao, J., Cai, D., Hu, W., Zhang, M., & Huang, Q. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3000184 · Cited by 269
Multivariate time series prediction by RNN architectures for energy consumption forecasting
Amalou, I., Mouhni, N., & Abdali, A. (2022) · Energy Reports · doi:10.1016/j.egyr.2022.07.139 · Cited by 220
Cox, S. J., Kim, D., Cho, H., & Mago, P. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.01.093 · Cited by 157
Metadata
- Authors
- Chen, B.-J., Chang, M.-W., & Lin, C.-J.
- Venue
- IEEE Transactions on Power Systems
- Published
- 2004-11
- Volume
- 19
- Issue
- 4
- Pages
- 1821-1830
- ISSN
- 0885-8950 (ISSN-L)
- OpenAlex
- W2168138569
- Cited by
- 841
- References
- 2
- FWCI
- 21.25
- Type
- journal article
- Language
- en
- Open access
- closed
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- Chen, B.-J., Chang, M.-W., & Lin, C.-J. (2004). Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001. IEEE Transactions on Power Systems. https://doi.org/10.1109/tpwrs.2004.835679
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