Applying Wavelets to Short-Term Load Forecasting Using PSO-Based Neural Networks

By Zidan A. Bashir, M. E. El-Hawary. IEEE Transactions on Power Systems, 2009

392 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/fda9b211074450738640fe037ebe6bf8a95c5871

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Citations in the corpus

Short-term load forecasting using lifting scheme and ARIMA models (2011, methodology): “Recently, hybrid methods that integrate wavelet theory with other techniques (Bashir & El-Hawary, 2009; Kim, Yu, & Song, 2002; Reis & da Silva, 2005; Yao, Song, Zhang, & Cheng, 2000; Zhang & Dong, 2005) have been extensively utilized for short-term load forecasting.”
Deep neural network based demand side short term load forecasting (2016, methodology): “Furthermore, neural network models could be combined with other methods: fuzzy logic [16], wavelet transform [17] or both [18].”
Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches † (2018, methodology): “…ANN, multilayer perceptron (MLP), was used to forecast a load proﬁle using previous load data [27], the fuzzy neural [28], wavelet neural networks [29], fuzzy wavelet neural network [30] and self-originating map (SOM), neural network [31], were used mainly for STLF. Unlike the conventional ANN…”
An improved wavelet–ARIMA approach for forecasting metal prices (2014, methodology): “Much research has also concentrated on the combination of artificial neural networks with wavelets (e.g. Amina et al., 2012; Amjady and Keynia, 2008; Aussem and Murtagh, 1997; Bashir and El-Hawary, 2009; Catalão et al., 2011; Chen et al., 2010; Jammazi and Aloui, 2012).”
A Microgrid Energy Management System Based on the Rolling Horizon Strategy (2013, methodology): “Recently, wavelets have been used for characterizing the load, and then, they are used as inputs for the neural network models [22].”
Clustering-based short-term load forecasting for residential electricity under the increasing-block pricing tariffs in China (2018, methodology): “From the data aggregation point of view, in general, extant approaches of load forecasting (see Table 2) can be divided into two types: the overall aggregation approach [3, 10, 11, 23, 28, 34, 39, 41, 54] and the individual-level approach [44].”