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Input variable selection for ANN-based short-term load forecasting
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DOI：10.1109/59.736244.png)
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Abstract
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This paper describes a novel method for input variable selection for artificial neural network (ANN) based short-term load forecasting (STLF). The method is based on the phase-space embedding of a load time-series. The accuracy of the method is enhanced by the addition of temperature and cycle variables. To test the viability of the method, real load data for two US-based electric utilities were used. Only 15 input variables were identified in both cases and used for 24-hour ahead load forecasting. Results compare favorably to the ones reported in the literature, indicating that more parsimonious set of input variables can be used in STLF without sacrificing the accuracy of the forecast. This allows more compact ANNs, smaller training sets and easier training. Consequently, the method represents a step forward in determining a general procedure for input variable selection for ANN-based STLF.
Keywords:
electric power systems
short-term load forecasting
phase-space embedding
artificial neural networks
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