Feature extraction via multiresolution analysis for short-term load forecasting

By Agnaldo J. R. Reis, A.P.A. da Silva. IEEE Transactions on Power Systems, 2005

299 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/17136d1fd59b8ee38fe0a963b814fa7694884c4b

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In Computer Vision and Pattern Recognition

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Citations in the corpus

A Strategy for Short-Term Load Forecasting by Support Vector Regression Machines (2013, methodology): “The proposed model is compared in Section III-A with the state-of-the-art model described in [1] on the daily elec-This trical loads in New England and in Section III-B with the state-of-the-art models described in [15]–[17] on the daily and hourly loads in North America.”
Short-term wind power forecasting in Portugal by neural networks and wavelet transform (2011, methodology): “This scheme is more efficient and just as accurate as the CWT [22].”
Short-Term Load Forecasting With Deep Residual Networks (2018, methodology): “We follow the way of modifying temperature values introduced in [43], and consider three”
An Adaptive Wavelet Neural Network-Based Energy Price Forecasting in Electricity Markets (2008, methodology): “Daubechies wavelets which have been used in several works [10]–[12] offer very competitive feature extraction capabilities but may lose some information due to high frequency components of signals [13].”
Comparative study of wavelet-ARIMA and wavelet-ANN models for temperature time series data in northeastern Bangladesh (2017, background): “Daubechies wavelets show a good trade-off between parsimony and information richness and identical events across the observed time series are produced by it in so many fashions that most prediction models cannot recognize them well (Reis and Silva, 2005; Benaouda et al., 2006).”
Modeling and forecasting short-term electricity load: A comparison of methods with an application to Brazilian data (2008, background): “Most papers in the load forecasting literature take first-order differences of the load series without previously testing for unit roots; see Reis and Silva (2005) for an example.”
Applying Wavelets to Short-Term Load Forecasting Using PSO-Based Neural Networks (2009, methodology): “Combining the wavelet transform with other techniques such as autoregressive modeling, and neural networks for forecasting electrical load demand has been studied [6]–[9].”