Short-term load forecasting based on an adaptive hybrid method

By Shu Fan, Luonan Chen. 2006

410 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Management Science and Operations Research

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Citations in the corpus

Deep neural network based demand side short term load forecasting (2016, methodology): “Other types of neural networks, such as a self-organizing map (SOM), are also used for prediction and classiﬁcation [20].”
Density Forecasting for Long-Term Peak Electricity Demand (2010, methodology): “This procedure of using individual models for each time of the day has also been applied by other researchers (Ramanathan et al., 1997; Fan and Chen, 2006).”
RBF Neural Network and ANFIS-Based Short-Term Load Forecasting Approach in Real-Time Price Environment (2008, background): “And thus, for a long time, STLF has been under great focus and various algorithms have been put forward [1]–[5].”
A gradient boosting approach to the Kaggle load forecasting competition (2014, methodology): “This idea has subsequently been used by Fay et al. (2003); McSharry et al. (2005); Fan and Chen (2006) and Fan and Hyndman (2012).”
Short-Term Load Forecasting Based on a Semi-Parametric Additive Model (2012, methodology): “In addition to the ANN-based model, the hybrid model in [16] has also been used to compare the forecasting performance.”
Optimal Charging Strategies for Unidirectional Vehicle-to-Grid (2011, methodology): “Day ahead load and wind forecasts are generated in similar manner to match the load forecast errors observed in [20] and wind forecast errors reported in [21].”
A Strategy for Short-Term Load Forecasting by Support Vector Regression Machines (2013, methodology): “• Divide data by a suitable clustering technique such as the self-organizing neural networks (SOM) [9].”
Short-term load forecasting using lifting scheme and ARIMA models (2011, methodology): “However, no single model has performed well in short-term load forecasting; thus, hybrid methods have been successfully applied for short-term load forecasting (Amjady, 2007; Fan & Chen, 2006; Huang, Huang, & Wang, 2005; Kim, Park, Hwang, & Kim, 1995; Zhang, 2003).”
Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches † (2018, methodology): “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.”