Forecasting short-term electricity load using hybrid support vector regression with grey catastrophe and random forest modeling
暂无分享,去创建一个
Wei-Chiang Hong | Guo-Feng Fan | Yi-Hsuan Yeh | Song-Qiao Dong | Meng Yu | Wei‐Chiang Hong | Guo-Feng Fan | Meng Yu | Song Dong | Yi-Hsuan Yeh
[1] Qi-yi Tang,et al. Data Processing System (DPS) software with experimental design, statistical analysis and data mining developed for use in entomological research , 2013, Insect science.
[2] Arun Kumar Sangaiah,et al. Smart grid load forecasting using online support vector regression , 2017, Comput. Electr. Eng..
[3] Zichen Zhang,et al. Electric load forecasting by complete ensemble empirical mode decomposition adaptive noise and support vector regression with quantum-based dragonfly algorithm , 2019, Nonlinear Dynamics.
[4] Farshid Keynia,et al. Short-term electricity load and price forecasting by a new optimal LSTM-NN based prediction algorithm , 2021, Electric Power Systems Research.
[5] Jian Zhang,et al. Short-term electrical load forecasting based on error correction using dynamic mode decomposition , 2020, Applied Energy.
[6] Weilin Li,et al. Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings , 2017 .
[7] Rahmat-Allah Hooshmand,et al. Short term electric load forecasting by wavelet transform and grey model improved by PSO (particle swarm optimization) algorithm , 2014 .
[8] Feng Yu,et al. A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network , 2014 .
[9] Chengzhi Deng,et al. Sequential grid approach based support vector regression for short-term electric load forecasting , 2019, Applied Energy.
[10] Jie Zhang,et al. Assessment of aggregation strategies for machine-learning based short-term load forecasting , 2020, Electric Power Systems Research.
[11] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[12] J. Sáenz,et al. Combining random forests and physics-based models to forecast the electricity generated by ocean waves: A case study of the Mutriku wave farm , 2019, Ocean Engineering.
[13] Pan Dongmei,et al. Comparison of four algorithms based on machine learning for cooling load forecasting of large-scale shopping mall , 2017 .
[14] Mayur Barman,et al. A regional hybrid GOA-SVM model based on similar day approach for short-term load forecasting in Assam, India , 2018 .
[15] Azim Heydari,et al. Renewable Energies Generation and Carbon Dioxide Emission Forecasting in Microgrids and National Grids using GRNN-GWO Methodology , 2019, Energy Procedia.
[16] Zhong-kai Feng,et al. A hybrid short-term load forecasting model based on variational mode decomposition and long short-term memory networks considering relevant factors with Bayesian optimization algorithm , 2019, Applied Energy.
[17] Wei-Chiang Hong,et al. Short term load forecasting based on phase space reconstruction algorithm and bi-square kernel regression model , 2018, Applied Energy.
[18] Tchitile Emmanuel Wilfried Azong,et al. Forecast of electricity consumption in the Cameroonian residential sector by Grey and vector autoregressive models , 2021 .
[19] Chia-Nan Ko,et al. Short-term load forecasting using SVR (support vector regression)-based radial basis function neural network with dual extended Kalman filter , 2013 .
[20] Haidar Samet,et al. A new hybrid Modified Firefly Algorithm and Support Vector Regression model for accurate Short Term Load Forecasting , 2014, Expert Syst. Appl..
[21] Yaoguo Dang,et al. Using a self-adaptive grey fractional weighted model to forecast Jiangsu’s electricity consumption in China , 2020 .
[22] YanYing Li,et al. Subsampled support vector regression ensemble for short term electric load forecasting , 2018, Energy.
[23] Yan Li,et al. Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia , 2018, Adv. Eng. Informatics.
[24] Chuan Li,et al. Improving forecasting accuracy of daily enterprise electricity consumption using a random forest based on ensemble empirical mode decomposition , 2018, Energy.
[25] Shanlin Yang,et al. A deep learning model for short-term power load and probability density forecasting , 2018, Energy.
[26] Qian Li,et al. Short Term Load Forecasting of Offshore Oil Field Microgrids Based on DA-SVM , 2019, Energy Procedia.
[27] K. Polat,et al. A new multistage short-term wind power forecast model using decomposition and artificial intelligence methods , 2019, Physica A: Statistical Mechanics and its Applications.
[28] Yi-Ming Wei,et al. Short term electricity load forecasting using a hybrid model , 2018, Energy.
[29] Mayur Barman,et al. Hybrid GOA-SVR technique for short term load forecasting during periods with substantial weather changes in North-East India , 2018 .
[30] Dan Wang,et al. Combined probability density model for medium term load forecasting based on quantile regression and kernel density estimation , 2019, Energy Procedia.
[31] Yuntao Han,et al. A hybrid EMD-SVR model for the short-term prediction of significant wave height , 2016 .
[32] Wei-Chiang Hong,et al. Applications of Hybrid EMD with PSO and GA for an SVR-Based Load Forecasting Model , 2017 .
[33] Jason Runge,et al. Hybrid short-term forecasting of the electric demand of supply fans using machine learning , 2020 .
[34] Tanveer Ahmad,et al. Nonlinear autoregressive and random forest approaches to forecasting electricity load for utility energy management systems , 2019, Sustainable Cities and Society.
[35] Eenjun Hwang,et al. Recurrent inception convolution neural network for multi short-term load forecasting , 2019, Energy and Buildings.
[36] Wei-Chiang Hong,et al. Hybrid evolutionary algorithms in a SVR-based electric load forecasting model , 2009 .
[37] Qiang Wang,et al. Integrating linear and nonlinear forecasting techniques based on grey theory and artificial intelligence to forecast shale gas monthly production in Pennsylvania and Texas of the United States , 2019, Energy.
[38] Wei-Chiang Hong,et al. Electric load forecasting by the SVR model with differential empirical mode decomposition and auto regression , 2016, Neurocomputing.