Abstract
In order to improve short term load forecasting accuracy, a novel particle swarm optimization (PSO) based support vector machine (SVM) model that combined input variable and similar day selection technique is proposed. Among so many load influential factors, rough set theory is used to select most relevant ones in order to make the input neurons representative. Next, Euclidean norm based similar day selection is used both for the forecasting day and for the days in the training set. After the preprocessing finished, PSO based support vector machine is used to establish the forecasting model. PSO is applied to train support vector machine to solve quadratic programming problem which is an effective method with better convergence and stability. The presented model is applied in certain area; and the experiment showed satisfactory results.
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Sun, W., He, Y. (2007). Optimal Support Vector Machine Based Short–Term Load Forecasting Model with Input Variables and Samples Selection. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques. ICIC 2007. Communications in Computer and Information Science, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74282-1_5
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DOI: https://doi.org/10.1007/978-3-540-74282-1_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74281-4
Online ISBN: 978-3-540-74282-1
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