A hybrid wavelet-Kalman filter method for load forecasting

By T. Zheng, A. Girgis, E. Makram. 2000

108 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/eb2a8638d3fd0cf48598a3c3abe5905847a21c10

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In Computer Vision and Pattern Recognition

In Media Technology

In Signal Processing

Citations in the corpus

Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. (2010, unknown): “…have become a tool for analyzing local variation in time series (Torrence and Compo, 1998), and hybrid models have been proposed for forecasting a time-series based on a wavelet transform pre-processing (Aussem and Murtagh, 1997; Aussem et al., 1998; Zheng et al., 2000; Zhang and Dong, 2001).”
Short‐Term Traffic Volume Forecasting Using Kalman Filter with Discrete Wavelet Decomposition (2007, methodology): “Instead of using wavelet coefficients as the state variable of Kalman filter models (Chui and Chen, 1999; Zheng et al., 2000), DWT is used in the new hybrid model to decompose and denoise raw traffic volume data series.”
Electric load forecasting: Literature survey and classification of methods (2002, methodology): “Zheng et al. (2000) applied a wavelet transform-Kalman ®lter method for load forecasting.”
NONLINEAR MODEL FOR DROUGHT FORECASTING BASED ON A CONJUNCTION OF WAVELET TRANSFORMS AND NEURAL NETWORKS (2003, methodology): “However, the training process of ANNs requires significant amounts of data so that the patterns embedded in the system are discovered (Zheng et al. 2000).”
Transmission Expansion Planning From Past to Future (2006, methodology): “adaptive load forecasting [108], stochastic time series [109], autoregressive moving average with exogenous variable (ARMAX) models based on GAs [110], fuzzy logic [111],”