Electric load forecasting: Literature survey and classification of methods
International Journal of Systems SciencePublished 1 January 2002
Hesham K. Alfares, Mohammad Khaja Nazeeruddin
Citations721
SJR quartileQ1
SJR score1.34
SNIP1.14
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TL;DR
A review and categorization of electric load forecasting techniques is presented, dividing them into nine categories: multiple regression, exponential smoothing, iterative reweighted least-squares, adaptive load forecasting, stochastic time series, ARMAX models based on genetic algorithms, fuzzy logic, neural networks, and expert systems.
Abstract
A review and categorization of electric load forecasting techniques is presented. A wide range of methodologies and models for forecasting are given in the literature. These techniques are classified here into nine categories: (1) multiple regression, (2) exponential smoothing, (3) iterative reweighted least-squares, (4) adaptive load forecasting, (5) stochastic time series, (6) ARMAX models based on genetic algorithms, (7) fuzzy logic, (8) neural networks and (9) expert systems. The methodology for each category is briefly described, the advantages and disadvantages discussed, and the pertinent literature reviewed. Conclusions and comments are made on future research directions.
Keywords
Decision SciencesEngineering
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