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A hybrid short-term load forecasting with a new input selection framework

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  1. Fan, Guo-Feng & Peng, Li-Ling & Hong, Wei-Chiang, 2018. "Short term load forecasting based on phase space reconstruction algorithm and bi-square kernel regression model," Applied Energy, Elsevier, vol. 224(C), pages 13-33.
  2. Gong, Mingju & Zhao, Yin & Sun, Jiawang & Han, Cuitian & Sun, Guannan & Yan, Bo, 2022. "Load forecasting of district heating system based on Informer," Energy, Elsevier, vol. 253(C).
  3. Zhu, Jiawei & Lin, Yishuai & Lei, Weidong & Liu, Youquan & Tao, Mengling, 2019. "Optimal household appliances scheduling of multiple smart homes using an improved cooperative algorithm," Energy, Elsevier, vol. 171(C), pages 944-955.
  4. Bessec, Marie & Fouquau, Julien, 2018. "Short-run electricity load forecasting with combinations of stationary wavelet transforms," European Journal of Operational Research, Elsevier, vol. 264(1), pages 149-164.
  5. Karimi, M. & Karami, H. & Gholami, M. & Khatibzadehazad, H. & Moslemi, N., 2018. "Priority index considering temperature and date proximity for selection of similar days in knowledge-based short term load forecasting method," Energy, Elsevier, vol. 144(C), pages 928-940.
  6. Le Cam, M. & Daoud, A. & Zmeureanu, R., 2016. "Forecasting electric demand of supply fan using data mining techniques," Energy, Elsevier, vol. 101(C), pages 541-557.
  7. Tomasz Szul & Sylwester Tabor & Krzysztof Pancerz, 2021. "Application of the BORUTA Algorithm to Input Data Selection for a Model Based on Rough Set Theory (RST) to Prediction Energy Consumption for Building Heating," Energies, MDPI, vol. 14(10), pages 1-13, May.
  8. Sholeh Hadi Pramono & Mahdin Rohmatillah & Eka Maulana & Rini Nur Hasanah & Fakhriy Hario, 2019. "Deep Learning-Based Short-Term Load Forecasting for Supporting Demand Response Program in Hybrid Energy System," Energies, MDPI, vol. 12(17), pages 1-16, August.
  9. Yan Hong Chen & Wei-Chiang Hong & Wen Shen & Ning Ning Huang, 2016. "Electric Load Forecasting Based on a Least Squares Support Vector Machine with Fuzzy Time Series and Global Harmony Search Algorithm," Energies, MDPI, vol. 9(2), pages 1-13, January.
  10. Sadaei, Hossein Javedani & de Lima e Silva, Petrônio Cândido & Guimarães, Frederico Gadelha & Lee, Muhammad Hisyam, 2019. "Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series," Energy, Elsevier, vol. 175(C), pages 365-377.
  11. Zhang, Liang & Wen, Jin & Li, Yanfei & Chen, Jianli & Ye, Yunyang & Fu, Yangyang & Livingood, William, 2021. "A review of machine learning in building load prediction," Applied Energy, Elsevier, vol. 285(C).
  12. Zizhen Cheng & Li Wang & Yumeng Yang, 2023. "A Hybrid Feature Pyramid CNN-LSTM Model with Seasonal Inflection Month Correction for Medium- and Long-Term Power Load Forecasting," Energies, MDPI, vol. 16(7), pages 1-18, March.
  13. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
  14. Ghimire, Sujan & Nguyen-Huy, Thong & AL-Musaylh, Mohanad S. & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho, 2023. "A novel approach based on integration of convolutional neural networks and echo state network for daily electricity demand prediction," Energy, Elsevier, vol. 275(C).
  15. Shao, Zhen & Chao, Fu & Yang, Shan-Lin & Zhou, Kai-Le, 2017. "A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 123-136.
  16. Le Cam, M. & Zmeureanu, R. & Daoud, A., 2017. "Cascade-based short-term forecasting method of the electric demand of HVAC system," Energy, Elsevier, vol. 119(C), pages 1098-1107.
  17. Wei-Chiang Hong & Guo-Feng Fan, 2019. "Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting," Energies, MDPI, vol. 12(6), pages 1-16, March.
  18. Jungwon Yu & June Ho Park & Sungshin Kim, 2018. "A New Input Selection Algorithm Using the Group Method of Data Handling and Bootstrap Method for Support Vector Regression Based Hourly Load Forecasting," Energies, MDPI, vol. 11(11), pages 1-20, October.
  19. Sepasi, Saeed & Reihani, Ehsan & Howlader, Abdul M. & Roose, Leon R. & Matsuura, Marc M., 2017. "Very short term load forecasting of a distribution system with high PV penetration," Renewable Energy, Elsevier, vol. 106(C), pages 142-148.
  20. Yizhen Wang & Ningqing Zhang & Xiong Chen, 2021. "A Short-Term Residential Load Forecasting Model Based on LSTM Recurrent Neural Network Considering Weather Features," Energies, MDPI, vol. 14(10), pages 1-13, May.
  21. Zhu, Xi & Zeng, Bo & Dong, Houqi & Liu, Jiaomin, 2020. "An interval-prediction based robust optimization approach for energy-hub operation scheduling considering flexible ramping products," Energy, Elsevier, vol. 194(C).
  22. Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
  23. Benedetti, Miriam & Cesarotti, Vittorio & Introna, Vito & Serranti, Jacopo, 2016. "Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study," Applied Energy, Elsevier, vol. 165(C), pages 60-71.
  24. Takeda, Hisashi & Tamura, Yoshiyasu & Sato, Seisho, 2016. "Using the ensemble Kalman filter for electricity load forecasting and analysis," Energy, Elsevier, vol. 104(C), pages 184-198.
  25. Huiting Zheng & Jiabin Yuan & Long Chen, 2017. "Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation," Energies, MDPI, vol. 10(8), pages 1-20, August.
  26. Luca Massidda & Marino Marrocu, 2017. "Decoupling Weather Influence from User Habits for an Optimal Electric Load Forecast System," Energies, MDPI, vol. 10(12), pages 1-16, December.
  27. Seyedeh Narjes Fallah & Mehdi Ganjkhani & Shahaboddin Shamshirband & Kwok-wing Chau, 2019. "Computational Intelligence on Short-Term Load Forecasting: A Methodological Overview," Energies, MDPI, vol. 12(3), pages 1-21, January.
  28. Nantian Huang & Guobo Lu & Dianguo Xu, 2016. "A Permutation Importance-Based Feature Selection Method for Short-Term Electricity Load Forecasting Using Random Forest," Energies, MDPI, vol. 9(10), pages 1-24, September.
  29. Zhao, Yin & Gong, Mingju & Sun, Jiawang & Han, Cuitian & Jing, Lei & Li, Bo & Zhao, Zhixuan, 2023. "A new hybrid optimization prediction strategy based on SH-Informer for district heating system," Energy, Elsevier, vol. 282(C).
  30. He, Yaoyao & Xu, Qifa & Wan, Jinhong & Yang, Shanlin, 2016. "Short-term power load probability density forecasting based on quantile regression neural network and triangle kernel function," Energy, Elsevier, vol. 114(C), pages 498-512.
  31. Heydari, Azim & Astiaso Garcia, Davide & Keynia, Farshid & Bisegna, Fabio & De Santoli, Livio, 2019. "A novel composite neural network based method for wind and solar power forecasting in microgrids," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
  32. Yuanyuan Zhou & Min Zhou & Qing Xia & Wei-Chiang Hong, 2019. "Construction of EMD-SVR-QGA Model for Electricity Consumption: Case of University Dormitory," Mathematics, MDPI, vol. 7(12), pages 1-23, December.
  33. Tingting Hou & Rengcun Fang & Jinrui Tang & Ganheng Ge & Dongjun Yang & Jianchao Liu & Wei Zhang, 2021. "A Novel Short-Term Residential Electric Load Forecasting Method Based on Adaptive Load Aggregation and Deep Learning Algorithms," Energies, MDPI, vol. 14(22), pages 1-21, November.
  34. He, Yaoyao & Liu, Rui & Li, Haiyan & Wang, Shuo & Lu, Xiaofen, 2017. "Short-term power load probability density forecasting method using kernel-based support vector quantile regression and Copula theory," Applied Energy, Elsevier, vol. 185(P1), pages 254-266.
  35. Lin Lin & Lin Xue & Zhiqiang Hu & Nantian Huang, 2018. "Modular Predictor for Day-Ahead Load Forecasting and Feature Selection for Different Hours," Energies, MDPI, vol. 11(7), pages 1-30, July.
  36. Wang, Deyun & Yue, Chenqiang & ElAmraoui, Adnen, 2021. "Multi-step-ahead electricity load forecasting using a novel hybrid architecture with decomposition-based error correction strategy," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
  37. Qi Jiang & Yuxin Cheng & Haozhe Le & Chunquan Li & Peter X. Liu, 2022. "A Stacking Learning Model Based on Multiple Similar Days for Short-Term Load Forecasting," Mathematics, MDPI, vol. 10(14), pages 1-20, July.
  38. Wu, Jinran & Cui, Zhesen & Chen, Yanyan & Kong, Demeng & Wang, You-Gan, 2019. "A new hybrid model to predict the electrical load in five states of Australia," Energy, Elsevier, vol. 166(C), pages 598-609.
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