The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Short-Term Load Forecasting Based on an Adaptive Hybrid Method
Abstract
This paper aims to develop a load forecasting method for short-term load forecasting, based on an adaptive two-stage hybrid network with self-organized map (SOM) and support vector machine (SVM). In the first stage, a SOM network is applied to cluster the input data set into several subsets in an unsupervised manner. Then, groups of 24 SVMs for the next day's load profile are used to fit the training data of each subset in the second stage in a supervised way. The proposed structure is robust with different data types and can deal well with the nonstationarity of load series. In particular, our method has the ability to adapt to different models automatically for the regular days and anomalous days at the same time. With the trained network, we can straightforwardly predict the next-day hourly electricity load. To confirm the effectiveness, the proposed model has been trained and tested on the data of the historical energy load from New York Independent System Operator.
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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
Time Series Analysis: Forecasting and Control
Geurts, M., Box, G. E. P., & Jenkins, G. M. (1977) · Journal of Marketing Research · doi:10.2307/3150485 · Cited by 19,186
Kohonen, T. (2001) · Springer Series in Information Sciences · doi:10.1007/978-3-642-56927-2 · Cited by 16,019
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Cristianini, N., & Shawe-Taylor, J. (2000) · doi:10.1017/cbo9780511801389 · Cited by 13,855
Libsvm : A library for support vector machines
Ferrari, V. (2008) · Medical Entomology and Zoology · Cited by 10,122
Time Series Analysis: Forecasting and Control
Kleiner, B. (1977) · Technometrics · doi:10.1080/00401706.1977.10489562 · Cited by 6,446
Neural networks for short-term load forecasting: a review and evaluation
Hippert, H. S., Pedreira, C. E., & Souza, R. C. (2001) · IEEE Transactions on Power Systems · doi:10.1109/59.910780 · Cited by 2,221
Predicting time series with support vector machines
Müller, K. R., Smola, A. J., Rätsch, G., Schölkopf, B., Kohlmorgen, J., & Vapnik, V. (1997) · Lecture Notes in Computer Science · doi:10.1007/bfb0020283 · Cited by 981
Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001
Chen, B.-J., Chang, M.-W., & Lin, C.-J. (2004) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2004.835679 · Cited by 841
Cited by
A review on applications of ANN and SVM for building electrical energy consumption forecasting
Ahmad, A. S., Hassan, M. Y., Abdullah, M. P., Rahman, H. A., Hussin, F., Abdullah, H., & Saidur, R. (2014) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2014.01.069 · Cited by 954
Bouktif, S., Fiaz, A., Ouni, A., & Serhani, M. (2018) · Energies · doi:10.3390/en11071636 · Cited by 915
Big data driven smart energy management: from big data to big insights
Zhou, K., Fu, C., & Yang, S. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.11.050 · Cited by 841
Optimal Charging Strategies for Unidirectional Vehicle-to-Grid
Sortomme, E., & El-Sharkawi, M. A. (2011) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2010.2090910 · Cited by 782
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
Metadata
- Authors
- Fan, S., & Chen, L.
- Venue
- IEEE Transactions on Power Systems
- Published
- 2006-02
- Volume
- 21
- Issue
- 1
- Pages
- 392-401
- ISSN
- 0885-8950 (ISSN-L)
- OpenAlex
- W2139073438
- Cited by
- 405
- References
- 21
- FWCI
- 17.21
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
