Short-Term Electric Load Forecasting Using Standardized Load Profile (SLP) And Support Vector Regression (SVR)
Abstract
Short-term load forecasting (STLF) plays an important role in business strategy building, ensuring reliability and safe operation for any electrical system. There are many different methods used for short-term forecasts including regression models, time series, neural networks, expert systems, fuzzy logic, machine learning, and statistical algorithms. The practical requirement is to minimize forecast errors, avoid wastages, prevent shortages, and limit risks in the electricity market. This paper proposes a method of STLF by constructing a standardized load profile (SLP) based on the past electrical load data, utilizing Support Regression Vector (SVR) machine learning algorithm to improve the accuracy of short-term forecasting algorithms.
Keywords:
short-term load forecast, regression model, standardized load profile, support vector regressionDownloads
References
M. H. M. R. Shyamali Dilhani, C. Jeenanunt, “Daily electric load forecasting: Case of Thailand”. 7th International Conference on Information Communication Technology for Embedded Systems, Bangkok, Thailand, March 20-22, 2016
J. Huo, T. Shi, J. Chang, “Comparison of Random Forest and SVM for Electrical Short-term Load Forecast with Different Data Sources”, 7th IEEE International Conference on Software Engineering and Service Science, Beijing, China, March 23, 2017
L. C. P. Velasco, C. R. Villezas, P. N. C. Phalang, J. A. A. Dagaang, “Next Day Electric Load Forecasting Using Artificial Neural Networks”, Cebu City, Philippines, December 9-12, 2015 DOI: https://doi.org/10.1109/HNICEM.2015.7393166
D. Willingham, “Electricity Load Forecasting for the Australian Market Case Study”, available at https://ww2.mathworks.cn/matlabcentral/fileexchange/31877-electricity-load-forecasting-for-the-australian-market-case-study?s_tid=FX_rc1_behav, 2016
N. T. Dung, T. T. Ha, N. T. Phuong, “Comparative Study of Short-term Electric Load Forecasting: Case Study EVNHCMC”, 4th International Conference on Green Technology and Sustainable Development, Ho Chi Minh City, Vietnam, November 23-24, 2018 DOI: https://doi.org/10.1109/GTSD.2018.8595514
E. Ceperic, V. Ceperic, A. Baric, “A strategy for short-term load forecasting by support vector regression machines”, IEEE Transactions on Power Systems, Vol. 28, No. 4, pp. 4356-4364, 2013 DOI: https://doi.org/10.1109/TPWRS.2013.2269803
V. Vapnik, The Nature of Statistical Learning Theory, Springer, 1995 DOI: https://doi.org/10.1007/978-1-4757-2440-0
S. Gunn, Support Vector Machines for Classification and Regression, Technical Report, University of Southampton, 1995
V. Cherkassky, Y. Ma, “Selection of Meta-parameters for Support Vector Regression”, International Conference on Artificial Neural Networks, Madrid, Spain, August 28-30, 2002 DOI: https://doi.org/10.1007/3-540-46084-5_112
D. Basak, S. Pal, D. C. Patranabis, “Support Vector Regression”, Neural Information Processing – Letters and Reviews, Vol. 11, No. 10, pp. 203–224, 2007
A. J. Smola, B. Scholkopf, “A Tutorial on Support Vector Regression, Statistics and Computing”, Vol. 14, No. 3, pp. 199–222, 2004 DOI: https://doi.org/10.1023/B:STCO.0000035301.49549.88
Understanding Support Vector Machine Regression and Support Vector Machine Regression, available at: https://www.mathworks.com/help/stats/understanding-support-vector-machine-regression.html
L. Breiman, “Random Forests”, Machine Learning, Vol. 45, No. 1, pp. 5-32, 2001 DOI: https://doi.org/10.1023/A:1010933404324
L. Breiman, J. H. Friedman, R. A. Olshen, C. J. Stone, Classification and Regression Trees. Chapman & Hall 1984
Downloads
How to Cite
License
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
