Research graph
References from Short-term power load forecasting method based on Bagging-stochastic configuration networks. Local targets link to admitted publications; unresolved targets remain external evidence.
Analysis of the value for unit commitment of improved load forecasts
10.1109/59.801894 · 1999 · External reference
Short-term residential load forecasting based on resident behaviour learning
10.1109/tpwrs.2017.2688178 · 2017 · External reference
Short-term load forecasting with deep residual networks
10.1109/tsg.2018.2844307 · 2018 · External reference
Hybrid multitask multi-information fusion deep learning for household short-term load forecasting
10.1109/tsg.2021.3091469 · 2021 · External reference
Secondary forecasting based on deviation analysis for short-term load forecasting
10.1109/tpwrs.2010.2052638 · 2010 · External reference
A very short-term load forecasting method based on deep LSTM RNN at zone level
2019 · External reference
Ultra short-term power load forecasting based on combined LSTM-XGBoost model
2020 · External reference
Load forecasting method based on CNN-GRU hybrid neural network
2020 · External reference
Parsimonious short-term load forecasting for optimal operation planning of electrical distribution systems
10.1109/tpwrs.2018.2872388 · 2018 · External reference
Short-term load forecasting, profile identification, and customer segmentation: a methodology based on periodic time series
10.1109/tpwrs.2005.852123 · 2005 · External reference
A new hybrid model for short-term electricity load forecasting
10.1109/access.2019.2937222 · 2019 · External reference
A particle swarm optimization to identifying the ARMAX model for short-term load forecasting
10.1109/tpwrs.2005.846106 · 2005 · External reference
Ultra-short-term industrial power demand forecasting using LSTM based hybrid ensemble learning
10.1109/tpwrs.2019.2963109 · 2019 · External reference
Regional load clustering integration forecasting based on convolutional neural network support vector regression machine [J]
2020 · External reference
A novel hybrid short-term load forecasting method of smart grid using MLR and LSTM neural network
10.1109/tii.2020.3000184 · 2020 · External reference
Short-term load forecasting for the holidays using fuzzy linear regression method
10.1109/tpwrs.2004.835632 · 2005 · External reference
Hybrid Kalman filters for very short-term load forecasting and prediction interval estimation
10.1109/tpwrs.2013.2264488 · 2013 · External reference
Forecasting Brazil’s electricity consumption with pegels exponential smoothing techniques.
10.1109/tla.2016.7459606 · 2016 · External reference
A weight initialization method based on neural network with asymmetric activation function.
10.1016/j.neucom.2022.01.088 · 2022 · External reference
A short-term and high-resolution distribution system load forecasting approach using support vector regression with hybrid parameters optimization
10.1109/tsg.2016.2628061 · 2016 · External reference
Short-term wind-power prediction based on wavelet transform–support vector machine and statistic-characteristics analysis
10.1109/tia.2012.2199449 · 2012 · External reference
RBF neural network and ANFIS-based short-term load forecasting approach in real-time price environment
10.1109/tpwrs.2008.922249 · 2008 · External reference
Short-term wind power forecasting based on VMD decomposition, ConvLSTM networks and error analysis
10.1109/access.2020.3011060 · 2020 · External reference
Application of bidirectional recurrent neural network combined with deep belief network in short-term load forecasting
10.1109/access.2019.2950957 · 2019 · External reference
Deep-based conditional probability density function forecasting of residential loads
10.1109/tsg.2020.2972513 · 2020 · External reference
Mutual information-based analysis of JPEG2000 contexts
10.1109/tip.2004.841199 · 2005 · External reference
Detecting associations based on the multi-variable maximum information coefficient
10.1109/access.2021.3070925 · 2021 · External reference
A short-term and high-resolution distribution system load forecasting approach using support vector regression with hybrid parameters optimization
10.1109/tsg.2016.2628061 · 2016 · External reference
Multi-task learning based on deep architecture for various types of load forecasting in regional energy system integration
2018 · External reference
Statistical load forecasting using optimal quantile regression random forest and risk assessment index
10.1109/tsg.2020.3034194 · 2020 · External reference
10.1007/978-1-4612-4380-9_41
10.1007/978-1-4612-4380-9_41 · 1992 · External reference
A hybrid short-term load forecasting model based on improved fuzzy c-means clustering, random forest and deep neural networks
10.1109/access.2021.3063123 · 2021 · External reference
Stochastic configuration networks: Fundamentals and algorithms
10.1109/tcyb.2017.2734043 · 2017 · External reference
