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Short term electricity load forecasting using a hybrid model

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  1. Zhineng Hu & Jing Ma & Liangwei Yang & Xiaoping Li & Meng Pang, 2019. "Decomposition-Based Dynamic Adaptive Combination Forecasting for Monthly Electricity Demand," Sustainability, MDPI, vol. 11(5), pages 1-25, February.
  2. Trull, Oscar & García-Díaz, J. Carlos & Troncoso, Alicia, 2021. "One-day-ahead electricity demand forecasting in holidays using discrete-interval moving seasonalities," Energy, Elsevier, vol. 231(C).
  3. Guo‐Feng Fan & Yan‐Hui Guo & Jia‐Mei Zheng & Wei‐Chiang Hong, 2020. "A generalized regression model based on hybrid empirical mode decomposition and support vector regression with back‐propagation neural network for mid‐short‐term load forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 737-756, August.
  4. Yi Yang & Zhihao Shang & Yao Chen & Yanhua Chen, 2020. "Multi-Objective Particle Swarm Optimization Algorithm for Multi-Step Electric Load Forecasting," Energies, MDPI, vol. 13(3), pages 1-19, January.
  5. Omar Jouma El-Hafez & Tarek Y. ElMekkawy & Mohamed Kharbeche & Ahmed Massoud, 2022. "Impact of COVID-19 Pandemic on Qatar Electricity Demand and Load Forecasting: Preparedness of Distribution Networks for Emerging Situations," Sustainability, MDPI, vol. 14(15), pages 1-13, July.
  6. Pin Li & Jinsuo Zhang, 2019. "Is China’s Energy Supply Sustainable? New Research Model Based on the Exponential Smoothing and GM(1,1) Methods," Energies, MDPI, vol. 12(2), pages 1-30, January.
  7. Niu, Dongxiao & Ji, Zhengsen & Li, Wanying & Xu, Xiaomin & Liu, Da, 2021. "Research and application of a hybrid model for mid-term power demand forecasting based on secondary decomposition and interval optimization," Energy, Elsevier, vol. 234(C).
  8. Pavlos Nikolaidis & Harris Partaourides, 2021. "A Model Predictive Control for the Dynamical Forecast of Operating Reserves in Frequency Regulation Services," Forecasting, MDPI, vol. 3(1), pages 1-14, March.
  9. Talaat, M. & Farahat, M.A. & Mansour, Noura & Hatata, A.Y., 2020. "Load forecasting based on grasshopper optimization and a multilayer feed-forward neural network using regressive approach," Energy, Elsevier, vol. 196(C).
  10. Laiqing Yan & Zutai Yan & Zhenwen Li & Ning Ma & Ran Li & Jian Qin, 2023. "Electricity Market Price Prediction Based on Quadratic Hybrid Decomposition and THPO Algorithm," Energies, MDPI, vol. 16(13), pages 1-18, July.
  11. Ribeiro, Matheus Henrique Dal Molin & da Silva, Ramon Gomes & Ribeiro, Gabriel Trierweiler & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2023. "Cooperative ensemble learning model improves electric short-term load forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
  12. Heydari, Azim & Majidi Nezhad, Meysam & Pirshayan, Elmira & Astiaso Garcia, Davide & Keynia, Farshid & De Santoli, Livio, 2020. "Short-term electricity price and load forecasting in isolated power grids based on composite neural network and gravitational search optimization algorithm," Applied Energy, Elsevier, vol. 277(C).
  13. Yukta Mehta & Rui Xu & Benjamin Lim & Jane Wu & Jerry Gao, 2023. "A Review for Green Energy Machine Learning and AI Services," Energies, MDPI, vol. 16(15), pages 1-30, July.
  14. Liu, Che & Sun, Bo & Zhang, Chenghui & Li, Fan, 2020. "A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine," Applied Energy, Elsevier, vol. 275(C).
  15. Hu, Yusha & Li, Jigeng & Hong, Mengna & Ren, Jingzheng & Lin, Ruojue & Liu, Yue & Liu, Mengru & Man, Yi, 2019. "Short term electric load forecasting model and its verification for process industrial enterprises based on hybrid GA-PSO-BPNN algorithm—A case study of papermaking process," Energy, Elsevier, vol. 170(C), pages 1215-1227.
  16. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
    • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
  17. Zhao, Zhenyu & Zhang, Yao & Yang, Yujia & Yuan, Shuguang, 2022. "Load forecasting via Grey Model-Least Squares Support Vector Machine model and spatial-temporal distribution of electric consumption intensity," Energy, Elsevier, vol. 255(C).
  18. Massimiliano Giacalone, 2022. "Optimal forecasting accuracy using Lp-norm combination," METRON, Springer;Sapienza Università di Roma, vol. 80(2), pages 187-230, August.
  19. Sulandari, Winita & Subanar, & Lee, Muhammad Hisyam & Rodrigues, Paulo Canas, 2020. "Indonesian electricity load forecasting using singular spectrum analysis, fuzzy systems and neural networks," Energy, Elsevier, vol. 190(C).
  20. Somu, Nivethitha & M R, Gauthama Raman & Ramamritham, Krithi, 2020. "A hybrid model for building energy consumption forecasting using long short term memory networks," Applied Energy, Elsevier, vol. 261(C).
  21. Richard Bean, 2023. "Forecasting the Monash Microgrid for the IEEE-CIS Technical Challenge," Energies, MDPI, vol. 16(3), pages 1-23, January.
  22. Jiarong Shi & Zhiteng Wang, 2022. "A Hybrid Forecast Model for Household Electric Power by Fusing Landmark-Based Spectral Clustering and Deep Learning," Sustainability, MDPI, vol. 14(15), pages 1-21, July.
  23. Barja-Martinez, Sara & Aragüés-Peñalba, Mònica & Munné-Collado, Íngrid & Lloret-Gallego, Pau & Bullich-Massagué, Eduard & Villafafila-Robles, Roberto, 2021. "Artificial intelligence techniques for enabling Big Data services in distribution networks: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
  24. Jiaan Zhang & Chenyu Liu & Leijiao Ge, 2022. "Short-Term Load Forecasting Model of Electric Vehicle Charging Load Based on MCCNN-TCN," Energies, MDPI, vol. 15(7), pages 1-25, April.
  25. Kong, Xiangyu & Li, Chuang & Wang, Chengshan & Zhang, Yusen & Zhang, Jian, 2020. "Short-term electrical load forecasting based on error correction using dynamic mode decomposition," Applied Energy, Elsevier, vol. 261(C).
  26. Hafeez, Ghulam & Alimgeer, Khurram Saleem & Khan, Imran, 2020. "Electric load forecasting based on deep learning and optimized by heuristic algorithm in smart grid," Applied Energy, Elsevier, vol. 269(C).
  27. Lai, Changzhi & Wang, Yu & Fan, Kai & Cai, Qilin & Ye, Qing & Pang, Haoqiang & Wu, Xi, 2022. "An improved forecasting model of short-term electric load of papermaking enterprises for production line optimization," Energy, Elsevier, vol. 245(C).
  28. Wang, Jianzhou & Xing, Qianyi & Zeng, Bo & Zhao, Weigang, 2022. "An ensemble forecasting system for short-term power load based on multi-objective optimizer and fuzzy granulation," Applied Energy, Elsevier, vol. 327(C).
  29. Li, Yiyan & Zhang, Si & Hu, Rongxing & Lu, Ning, 2021. "A meta-learning based distribution system load forecasting model selection framework," Applied Energy, Elsevier, vol. 294(C).
  30. Chuanhui Zuo & Jialong Wang & Mingping Liu & Suhui Deng & Qingnian Wang, 2023. "An Ensemble Framework for Short-Term Load Forecasting Based on TimesNet and TCN," Energies, MDPI, vol. 16(14), pages 1-17, July.
  31. Zhang, Jinliang & Siya, Wang & Zhongfu, Tan & Anli, Sun, 2023. "An improved hybrid model for short term power load prediction," Energy, Elsevier, vol. 268(C).
  32. Laib, Oussama & Khadir, Mohamed Tarek & Mihaylova, Lyudmila, 2019. "Toward efficient energy systems based on natural gas consumption prediction with LSTM Recurrent Neural Networks," Energy, Elsevier, vol. 177(C), pages 530-542.
  33. Fan, Guo-Feng & Yu, Meng & Dong, Song-Qiao & Yeh, Yi-Hsuan & Hong, Wei-Chiang, 2021. "Forecasting short-term electricity load using hybrid support vector regression with grey catastrophe and random forest modeling," Utilities Policy, Elsevier, vol. 73(C).
  34. Fanidhar Dewangan & Almoataz Y. Abdelaziz & Monalisa Biswal, 2023. "Load Forecasting Models in Smart Grid Using Smart Meter Information: A Review," Energies, MDPI, vol. 16(3), pages 1-55, January.
  35. Qiangqiang Cheng & Yiqi Yan & Shichao Liu & Chunsheng Yang & Hicham Chaoui & Mohamad Alzayed, 2020. "Particle Filter-Based Electricity Load Prediction for Grid-Connected Microgrid Day-Ahead Scheduling," Energies, MDPI, vol. 13(24), pages 1-15, December.
  36. Md Jamal Ahmed Shohan & Md Omar Faruque & Simon Y. Foo, 2022. "Forecasting of Electric Load Using a Hybrid LSTM-Neural Prophet Model," Energies, MDPI, vol. 15(6), pages 1-18, March.
  37. Akbal, Yıldırım & Ünlü, Kamil Demirberk, 2022. "A univariate time series methodology based on sequence-to-sequence learning for short to midterm wind power production," Renewable Energy, Elsevier, vol. 200(C), pages 832-844.
  38. Heni Boubaker & Nawres Bannour, 2023. "Coupling the Empirical Wavelet and the Neural Network Methods in Order to Forecast Electricity Price," JRFM, MDPI, vol. 16(4), pages 1-22, April.
  39. Huang, Yanmei & Hasan, Najmul & Deng, Changrui & Bao, Yukun, 2022. "Multivariate empirical mode decomposition based hybrid model for day-ahead peak load forecasting," Energy, Elsevier, vol. 239(PC).
  40. Fu, Xin & Zeng, Xiao-Jun & Feng, Pengpeng & Cai, Xiuwen, 2018. "Clustering-based short-term load forecasting for residential electricity under the increasing-block pricing tariffs in China," Energy, Elsevier, vol. 165(PB), pages 76-89.
  41. Xie, Yiwei & Hu, Pingfang & Zhu, Na & Lei, Fei & Xing, Lu & Xu, Linghong & Sun, Qiming, 2020. "A hybrid short-term load forecasting model and its application in ground source heat pump with cooling storage system," Renewable Energy, Elsevier, vol. 161(C), pages 1244-1259.
  42. Wang, Chong & Ju, Ping & Wu, Feng & Lei, Shunbo & Hou, Yunhe, 2021. "Coordinated scheduling of integrated power and gas grids in consideration of gas flow dynamics," Energy, Elsevier, vol. 220(C).
  43. Faisal Saeed & Anand Paul & Hyuncheol Seo, 2022. "A Hybrid Channel-Communication-Enabled CNN-LSTM Model for Electricity Load Forecasting," Energies, MDPI, vol. 15(6), pages 1-17, March.
  44. Kailai Ni & Jianzhou Wang & Guangyu Tang & Danxiang Wei, 2019. "Research and Application of a Novel Hybrid Model Based on a Deep Neural Network for Electricity Load Forecasting: A Case Study in Australia," Energies, MDPI, vol. 12(13), pages 1-30, June.
  45. Li, Fengyun & Zheng, Haofeng & Li, Xingmei & Yang, Fei, 2021. "Day-ahead city natural gas load forecasting based on decomposition-fusion technique and diversified ensemble learning model," Applied Energy, Elsevier, vol. 303(C).
  46. Yang, Dongchuan & Guo, Ju-e & Sun, Shaolong & Han, Jing & Wang, Shouyang, 2022. "An interval decomposition-ensemble approach with data-characteristic-driven reconstruction for short-term load forecasting," Applied Energy, Elsevier, vol. 306(PA).
  47. Yin, Chen & Mao, Shuhua, 2023. "Fractional multivariate grey Bernoulli model combined with improved grey wolf algorithm: Application in short-term power load forecasting," Energy, Elsevier, vol. 269(C).
  48. Zhineng Hu & Jing Ma & Liangwei Yang & Liming Yao & Meng Pang, 2019. "Monthly electricity demand forecasting using empirical mode decomposition-based state space model," Energy & Environment, , vol. 30(7), pages 1236-1254, November.
  49. Namrye Son, 2021. "Comparison of the Deep Learning Performance for Short-Term Power Load Forecasting," Sustainability, MDPI, vol. 13(22), pages 1-25, November.
  50. Wu, Han & Liang, Yan & Heng, Jiani, 2023. "Pulse-diagnosis-inspired multi-feature extraction deep network for short-term electricity load forecasting," Applied Energy, Elsevier, vol. 339(C).
  51. Hany Habbak & Mohamed Mahmoud & Khaled Metwally & Mostafa M. Fouda & Mohamed I. Ibrahem, 2023. "Load Forecasting Techniques and Their Applications in Smart Grids," Energies, MDPI, vol. 16(3), pages 1-33, February.
  52. María Carmen Ruiz-Abellón & Luis Alfredo Fernández-Jiménez & Antonio Guillamón & Alberto Falces & Ana García-Garre & Antonio Gabaldón, 2019. "Integration of Demand Response and Short-Term Forecasting for the Management of Prosumers’ Demand and Generation," Energies, MDPI, vol. 13(1), pages 1-31, December.
  53. Rafati, Amir & Joorabian, Mahmood & Mashhour, Elaheh, 2020. "An efficient hour-ahead electrical load forecasting method based on innovative features," Energy, Elsevier, vol. 201(C).
  54. Tavassoli-Hojati, Z. & Ghaderi, S.F. & Iranmanesh, H. & Hilber, P. & Shayesteh, E., 2020. "A self-partitioning local neuro fuzzy model for short-term load forecasting in smart grids," Energy, Elsevier, vol. 199(C).
  55. Liu, Jiefeng & Zhang, Zhenhao & Fan, Xianhao & Zhang, Yiyi & Wang, Jiaqi & Zhou, Ke & Liang, Shuo & Yu, Xiaoyong & Zhang, Wei, 2022. "Power system load forecasting using mobility optimization and multi-task learning in COVID-19," Applied Energy, Elsevier, vol. 310(C).
  56. Li, Lechen & Meinrenken, Christoph J. & Modi, Vijay & Culligan, Patricia J., 2021. "Short-term apartment-level load forecasting using a modified neural network with selected auto-regressive features," Applied Energy, Elsevier, vol. 287(C).
  57. Ma, Yixiang & Yu, Lean & Zhang, Guoxing, 2022. "Short-term wind power forecasting with an intermittency-trait-driven methodology," Renewable Energy, Elsevier, vol. 198(C), pages 872-883.
  58. Wen Zhang & Zhibin Wu, 2022. "Optimal hybrid framework for carbon price forecasting using time series analysis and least squares support vector machine," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 615-632, April.
  59. 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.
  60. Ibrahim Salem Jahan & Vaclav Snasel & Stanislav Misak, 2020. "Intelligent Systems for Power Load Forecasting: A Study Review," Energies, MDPI, vol. 13(22), pages 1-12, November.
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