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A review and analysis of regression and machine learning models on commercial building electricity load forecasting

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  1. Sha, Huajing & Xu, Peng & Yang, Zhiwei & Chen, Yongbao & Tang, Jixu, 2019. "Overview of computational intelligence for building energy system design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 76-90.
  2. Barman, Mayur & Dev Choudhury, Nalin Behari, 2019. "Season specific approach for short-term load forecasting based on hybrid FA-SVM and similarity concept," Energy, Elsevier, vol. 174(C), pages 886-896.
  3. Li, Xian-Xiang, 2018. "Linking residential electricity consumption and outdoor climate in a tropical city," Energy, Elsevier, vol. 157(C), pages 734-743.
  4. Tomasz Szul & Stanisław Kokoszka, 2020. "Application of Rough Set Theory (RST) to Forecast Energy Consumption in Buildings Undergoing Thermal Modernization," Energies, MDPI, vol. 13(6), pages 1-17, March.
  5. Ghafoori, Mahdi & Abdallah, Moatassem & Kim, Serena, 2023. "Electricity peak shaving for commercial buildings using machine learning and vehicle to building (V2B) system," Applied Energy, Elsevier, vol. 340(C).
  6. Guelpa, Elisa & Bischi, Aldo & Verda, Vittorio & Chertkov, Michael & Lund, Henrik, 2019. "Towards future infrastructures for sustainable multi-energy systems: A review," Energy, Elsevier, vol. 184(C), pages 2-21.
  7. Chinmoy, Lakshmi & Iniyan, S. & Goic, Ranko, 2019. "Modeling wind power investments, policies and social benefits for deregulated electricity market – A review," Applied Energy, Elsevier, vol. 242(C), pages 364-377.
  8. Fu, Guoyin, 2018. "Deep belief network based ensemble approach for cooling load forecasting of air-conditioning system," Energy, Elsevier, vol. 148(C), pages 269-282.
  9. Guanchen Geng & Yu He & Jing Zhang & Tingxiang Qin & Bin Yang, 2023. "Short-Term Power Load Forecasting Based on PSO-Optimized VMD-TCN-Attention Mechanism," Energies, MDPI, vol. 16(12), pages 1-13, June.
  10. Hammad Mahmoud A. & Jereb Borut & Rosi Bojan & Dragan Dejan, 2020. "Methods and Models for Electric Load Forecasting: A Comprehensive Review," Logistics, Supply Chain, Sustainability and Global Challenges, Sciendo, vol. 11(1), pages 51-76, February.
  11. Agbessi Akuété Pierre & Salami Adekunlé Akim & Agbosse Kodjovi Semenyo & Birregah Babiga, 2023. "Peak Electrical Energy Consumption Prediction by ARIMA, LSTM, GRU, ARIMA-LSTM and ARIMA-GRU Approaches," Energies, MDPI, vol. 16(12), pages 1-12, June.
  12. Vogler–Finck, P.J.C. & Bacher, P. & Madsen, H., 2017. "Online short-term forecast of greenhouse heat load using a weather forecast service," Applied Energy, Elsevier, vol. 205(C), pages 1298-1310.
  13. Giuseppe La Tona & Maria Carmela Di Piazza & Massimiliano Luna, 2021. "Effect of Daily Forecasting Frequency on Rolling-Horizon-Based EMS Reducing Electrical Demand Uncertainty in Microgrids," Energies, MDPI, vol. 14(6), pages 1-16, March.
  14. Jihoon Moon & Junhong Kim & Pilsung Kang & Eenjun Hwang, 2020. "Solving the Cold-Start Problem in Short-Term Load Forecasting Using Tree-Based Methods," Energies, MDPI, vol. 13(4), pages 1-37, February.
  15. Zhu, Jizhong & Dong, Hanjiang & Zheng, Weiye & Li, Shenglin & Huang, Yanting & Xi, Lei, 2022. "Review and prospect of data-driven techniques for load forecasting in integrated energy systems," Applied Energy, Elsevier, vol. 321(C).
  16. Hafeez, Ghulam & Khan, Imran & Jan, Sadaqat & Shah, Ibrar Ali & Khan, Farrukh Aslam & Derhab, Abdelouahid, 2021. "A novel hybrid load forecasting framework with intelligent feature engineering and optimization algorithm in smart grid," Applied Energy, Elsevier, vol. 299(C).
  17. Severinsen, A. & Myrland, Ø., 2022. "Statistical learning to estimate energy savings from retrofitting in the Norwegian food retail market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
  18. Lee, Juyong & Cho, Youngsang, 2022. "National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?," Energy, Elsevier, vol. 239(PD).
  19. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2020. "Multi-Sequence LSTM-RNN Deep Learning and Metaheuristics for Electric Load Forecasting," Energies, MDPI, vol. 13(2), pages 1-21, January.
  20. Imani, Maryam, 2021. "Electrical load-temperature CNN for residential load forecasting," Energy, Elsevier, vol. 227(C).
  21. Lu, Yakai & Tian, Zhe & Zhou, Ruoyu & Liu, Wenjing, 2021. "A general transfer learning-based framework for thermal load prediction in regional energy system," Energy, Elsevier, vol. 217(C).
  22. Jihoon Moon & Sungwoo Park & Seungmin Rho & Eenjun Hwang, 2019. "A comparative analysis of artificial neural network architectures for building energy consumption forecasting," International Journal of Distributed Sensor Networks, , vol. 15(9), pages 15501477198, September.
  23. Sooyoun Cho & Jeehang Lee & Jumi Baek & Gi-Seok Kim & Seung-Bok Leigh, 2019. "Investigating Primary Factors Affecting Electricity Consumption in Non-Residential Buildings Using a Data-Driven Approach," Energies, MDPI, vol. 12(21), pages 1-23, October.
  24. Aslam, Sheraz & Herodotou, Herodotos & Mohsin, Syed Muhammad & Javaid, Nadeem & Ashraf, Nouman & Aslam, Shahzad, 2021. "A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
  25. Jee-Heon Kim & Nam-Chul Seong & Wonchang Choi, 2019. "Cooling Load Forecasting via Predictive Optimization of a Nonlinear Autoregressive Exogenous (NARX) Neural Network Model," Sustainability, MDPI, vol. 11(23), pages 1-13, November.
  26. 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.
  27. Guo, Zhifeng & Zhou, Kaile & Zhang, Xiaoling & Yang, Shanlin, 2018. "A deep learning model for short-term power load and probability density forecasting," Energy, Elsevier, vol. 160(C), pages 1186-1200.
  28. V.K. Arun Shankar & Umashankar Subramaniam & Sanjeevikumar Padmanaban & Jens Bo Holm-Nielsen & Frede Blaabjerg & S. Paramasivam, 2019. "Experimental Investigation of Power Signatures for Cavitation and Water Hammer in an Industrial Parallel Pumping System," Energies, MDPI, vol. 12(7), pages 1-14, April.
  29. Luta, Doudou N. & Raji, Atanda K., 2019. "Optimal sizing of hybrid fuel cell-supercapacitor storage system for off-grid renewable applications," Energy, Elsevier, vol. 166(C), pages 530-540.
  30. Xie, Yiwei & Hu, Pingfang & Peng, Donggen & Zhu, Na & Lei, Fei, 2023. "Development of a group control strategy based on multi-step load forecasting and its application in hybrid ground source heat pump," Energy, Elsevier, vol. 273(C).
  31. Jiakang Wang & Hui Liu & Guangji Zheng & Ye Li & Shi Yin, 2023. "Short-Term Load Forecasting Based on Outlier Correction, Decomposition, and Ensemble Reinforcement Learning," Energies, MDPI, vol. 16(11), pages 1-16, May.
  32. Wate, P. & Iglesias, M. & Coors, V. & Robinson, D., 2020. "Framework for emulation and uncertainty quantification of a stochastic building performance simulator," Applied Energy, Elsevier, vol. 258(C).
  33. Prado, Francisco & Minutolo, Marcel C. & Kristjanpoller, Werner, 2020. "Forecasting based on an ensemble Autoregressive Moving Average - Adaptive neuro - Fuzzy inference system – Neural network - Genetic Algorithm Framework," Energy, Elsevier, vol. 197(C).
  34. Tomasz Szul & Krzysztof Nęcka & Stanisław Lis, 2021. "Application of the Takagi-Sugeno Fuzzy Modeling to Forecast Energy Efficiency in Real Buildings Undergoing Thermal Improvement," Energies, MDPI, vol. 14(7), pages 1-16, March.
  35. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2018. "Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †," Energies, MDPI, vol. 11(7), pages 1-20, June.
  36. Anand Krishnan Prakash & Susu Xu & Ram Rajagopal & Hae Young Noh, 2018. "Robust Building Energy Load Forecasting Using Physically-Based Kernel Models," Energies, MDPI, vol. 11(4), pages 1-21, April.
  37. Liang, Yi & Niu, Dongxiao & Hong, Wei-Chiang, 2019. "Short term load forecasting based on feature extraction and improved general regression neural network model," Energy, Elsevier, vol. 166(C), pages 653-663.
  38. Kei Hirose & Keigo Wada & Maiya Hori & Rin-ichiro Taniguchi, 2020. "Event Effects Estimation on Electricity Demand Forecasting," Energies, MDPI, vol. 13(21), pages 1-20, November.
  39. Amir Mosavi & Mohsen Salimi & Sina Faizollahzadeh Ardabili & Timon Rabczuk & Shahaboddin Shamshirband & Annamaria R. Varkonyi-Koczy, 2019. "State of the Art of Machine Learning Models in Energy Systems, a Systematic Review," Energies, MDPI, vol. 12(7), pages 1-42, April.
  40. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2019. "Single and Multi-Sequence Deep Learning Models for Short and Medium Term Electric Load Forecasting," Energies, MDPI, vol. 12(1), pages 1-21, January.
  41. Meng, Ming & Wang, Lixue & Shang, Wei, 2018. "Decomposition and forecasting analysis of China's household electricity consumption using three-dimensional decomposition and hybrid trend extrapolation models," Energy, Elsevier, vol. 165(PA), pages 143-152.
  42. Barone, G. & Buonomano, A. & Forzano, C. & Palombo, A. & Russo, G., 2023. "The role of energy communities in electricity grid balancing: A flexible tool for smart grid power distribution optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 187(C).
  43. Sotiropoulou, Kalliopi F. & Vavatsikos, Athanasios P. & Botsaris, Pantelis N., 2024. "A hybrid AHP-PROMETHEE II onshore wind farms multicriteria suitability analysis using kNN and SVM regression models in northeastern Greece," Renewable Energy, Elsevier, vol. 221(C).
  44. Athanasios Anagnostis & Serafeim Moustakidis & Elpiniki Papageorgiou & Dionysis Bochtis, 2022. "A Hybrid Bimodal LSTM Architecture for Cascading Thermal Energy Storage Modelling," Energies, MDPI, vol. 15(6), pages 1-24, March.
  45. Da Liu & Kun Sun & Han Huang & Pingzhou Tang, 2018. "Monthly Load Forecasting Based on Economic Data by Decomposition Integration Theory," Sustainability, MDPI, vol. 10(9), pages 1-22, September.
  46. Ang, B.W. & Wang, H. & Ma, Xiaojing, 2017. "Climatic influence on electricity consumption: The case of Singapore and Hong Kong," Energy, Elsevier, vol. 127(C), pages 534-543.
  47. Suzana Domjan & Sašo Medved & Boštjan Černe & Ciril Arkar, 2019. "Fast Modelling of nZEB Metrics of Office Buildings Built with Advanced Glass and BIPV Facade Structures," Energies, MDPI, vol. 12(16), pages 1-18, August.
  48. Srinivasan, Arvind & Wu, Raphael & Heer, Philipp & Sansavini, Giovanni, 2023. "Impact of forecast uncertainty and electricity markets on the flexibility provision and economic performance of highly-decarbonized multi-energy systems," Applied Energy, Elsevier, vol. 338(C).
  49. Shiyi Song & Hong Leng & Ran Guo, 2022. "Multi-Agent-Based Model for the Urban Macro-Level Impact Factors of Building Energy Consumption on Different Types of Land," Land, MDPI, vol. 11(11), pages 1-24, November.
  50. Venkatraj, V. & Dixit, M.K., 2022. "Challenges in implementing data-driven approaches for building life cycle energy assessment: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
  51. 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.
  52. Grillone, Benedetto & Danov, Stoyan & Sumper, Andreas & Cipriano, Jordi & Mor, Gerard, 2020. "A review of deterministic and data-driven methods to quantify energy efficiency savings and to predict retrofitting scenarios in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
  53. Gautham Krishnadas & Aristides Kiprakis, 2020. "A Machine Learning Pipeline for Demand Response Capacity Scheduling," Energies, MDPI, vol. 13(7), pages 1-25, April.
  54. Zhao, Li & Ke, Hanchen & Huo, Weiwei, 2023. "A frequency item mining based energy consumption prediction method for electric bus," Energy, Elsevier, vol. 263(PD).
  55. Ibrahim, Muhammad Sohail & Dong, Wei & Yang, Qiang, 2020. "Machine learning driven smart electric power systems: Current trends and new perspectives," Applied Energy, Elsevier, vol. 272(C).
  56. Zheng, Ling & Zhou, Bin & Cao, Yijia & Wing Or, Siu & Li, Yong & Wing Chan, Ka, 2022. "Hierarchical distributed multi-energy demand response for coordinated operation of building clusters," Applied Energy, Elsevier, vol. 308(C).
  57. Di Piazza, A. & Di Piazza, M.C. & La Tona, G. & Luna, M., 2021. "An artificial neural network-based forecasting model of energy-related time series for electrical grid management," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 184(C), pages 294-305.
  58. Zhang, Wenyu & Chen, Qian & Yan, Jianyong & Zhang, Shuai & Xu, Jiyuan, 2021. "A novel asynchronous deep reinforcement learning model with adaptive early forecasting method and reward incentive mechanism for short-term load forecasting," Energy, Elsevier, vol. 236(C).
  59. Xing Zhang, 2018. "Short-Term Load Forecasting for Electric Bus Charging Stations Based on Fuzzy Clustering and Least Squares Support Vector Machine Optimized by Wolf Pack Algorithm," Energies, MDPI, vol. 11(6), pages 1-18, June.
  60. 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).
  61. Zhang, Xiaofeng & Kong, Xiaoying & Yan, Renshi & Liu, Yuting & Xia, Peng & Sun, Xiaoqin & Zeng, Rong & Li, Hongqiang, 2023. "Data-driven cooling, heating and electrical load prediction for building integrated with electric vehicles considering occupant travel behavior," Energy, Elsevier, vol. 264(C).
  62. Peplinski, McKenna & Dilkina, Bistra & Chen, Mo & Silva, Sam J. & Ban-Weiss, George A. & Sanders, Kelly T., 2024. "A machine learning framework to estimate residential electricity demand based on smart meter electricity, climate, building characteristics, and socioeconomic datasets," Applied Energy, Elsevier, vol. 357(C).
  63. Sarah O’Connell & Marcus Martin Keane, 2021. "Development of a Framework for Activation of Aggregator Led Flexibility," Energies, MDPI, vol. 14(16), pages 1-15, August.
  64. Umar Javed & Khalid Ijaz & Muhammad Jawad & Ejaz A. Ansari & Noman Shabbir & Lauri Kütt & Oleksandr Husev, 2021. "Exploratory Data Analysis Based Short-Term Electrical Load Forecasting: A Comprehensive Analysis," Energies, MDPI, vol. 14(17), pages 1-22, September.
  65. Østergård, Torben & Jensen, Rasmus Lund & Maagaard, Steffen Enersen, 2018. "A comparison of six metamodeling techniques applied to building performance simulations," Applied Energy, Elsevier, vol. 211(C), pages 89-103.
  66. Li, Wenqiang & Gong, Guangcai & Ren, Zhongjun & Ouyang, Qianwu & Peng, Pei & Chun, Liang & Fang, Xi, 2022. "A method for energy consumption optimization of air conditioning systems based on load prediction and energy flexibility," Energy, Elsevier, vol. 243(C).
  67. Zulfiqar, M. & Kamran, M. & Rasheed, M.B. & Alquthami, T. & Milyani, A.H., 2023. "A hybrid framework for short term load forecasting with a navel feature engineering and adaptive grasshopper optimization in smart grid," Applied Energy, Elsevier, vol. 338(C).
  68. Shin, Bigyeong & Chang, Seong Jin & Wi, Seunghwan & Kim, Sumin, 2023. "Estimation of energy demand and greenhouse gas emission reduction effect of cross-laminated timber (CLT) hybrid wall using life cycle assessment for urban residential planning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).
  69. Michael Wood & Emanuele Ogliari & Alfredo Nespoli & Travis Simpkins & Sonia Leva, 2023. "Day Ahead Electric Load Forecast: A Comprehensive LSTM-EMD Methodology and Several Diverse Case Studies," Forecasting, MDPI, vol. 5(1), pages 1-18, March.
  70. Lu, Yakai & Tian, Zhe & Zhang, Qiang & Zhou, Ruoyu & Chu, Chengshan, 2021. "Data augmentation strategy for short-term heating load prediction model of residential building," Energy, Elsevier, vol. 235(C).
  71. S. M. Mahfuz Alam & Mohd. Hasan Ali, 2020. "Equation Based New Methods for Residential Load Forecasting," Energies, MDPI, vol. 13(23), pages 1-22, December.
  72. Elalem, Yara Kayyali & Maier, Sebastian & Seifert, Ralf W., 2023. "A machine learning-based framework for forecasting sales of new products with short life cycles using deep neural networks," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1874-1894.
  73. Massaoudi, Mohamed & Refaat, Shady S. & Chihi, Ines & Trabelsi, Mohamed & Oueslati, Fakhreddine S. & Abu-Rub, Haitham, 2021. "A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short-Term Load Forecasting," Energy, Elsevier, vol. 214(C).
  74. Joanna Piotrowska-Woroniak & Tomasz Szul, 2022. "Application of a Model Based on Rough Set Theory (RST) to Estimate the Energy Efficiency of Public Buildings," Energies, MDPI, vol. 15(23), pages 1-13, November.
  75. Chitalia, Gopal & Pipattanasomporn, Manisa & Garg, Vishal & Rahman, Saifur, 2020. "Robust short-term electrical load forecasting framework for commercial buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 278(C).
  76. Arpita Samanta Santra & Jun-Lin Lin, 2019. "Integrating Long Short-Term Memory and Genetic Algorithm for Short-Term Load Forecasting," Energies, MDPI, vol. 12(11), pages 1-11, May.
  77. Aneeque A. Mir & Mohammed Alghassab & Kafait Ullah & Zafar A. Khan & Yuehong Lu & Muhammad Imran, 2020. "A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons," Sustainability, MDPI, vol. 12(15), pages 1-35, July.
  78. Shimoda, Yoshiyuki & Yamaguchi, Yohei & Iwafune, Yumiko & Hidaka, Kazuyoshi & Meier, Alan & Yagita, Yoshie & Kawamoto, Hisaki & Nishikiori, Soichi, 2020. "Energy demand science for a decarbonized society in the context of the residential sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
  79. Roberto Zanetti Freire & Gerson Henrique dos Santos & Leandro dos Santos Coelho, 2017. "Hygrothermal Dynamic and Mould Growth Risk Predictions for Concrete Tiles by Using Least Squares Support Vector Machines," Energies, MDPI, vol. 10(8), pages 1-16, July.
  80. Dietrich, Bastian & Walther, Jessica & Weigold, Matthias & Abele, Eberhard, 2020. "Machine learning based very short term load forecasting of machine tools," Applied Energy, Elsevier, vol. 276(C).
  81. Yildiz, B. & Bilbao, J.I. & Dore, J. & Sproul, A.B., 2017. "Recent advances in the analysis of residential electricity consumption and applications of smart meter data," Applied Energy, Elsevier, vol. 208(C), pages 402-427.
  82. Cai, Mengmeng & Pipattanasomporn, Manisa & Rahman, Saifur, 2019. "Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques," Applied Energy, Elsevier, vol. 236(C), pages 1078-1088.
  83. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.
  84. Hernandez-Matheus, Alejandro & Löschenbrand, Markus & Berg, Kjersti & Fuchs, Ida & Aragüés-Peñalba, Mònica & Bullich-Massagué, Eduard & Sumper, Andreas, 2022. "A systematic review of machine learning techniques related to local energy communities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
  85. Kaytez, Fazil, 2020. "A hybrid approach based on autoregressive integrated moving average and least-square support vector machine for long-term forecasting of net electricity consumption," Energy, Elsevier, vol. 197(C).
  86. R. Rueda & M. P. Cuéllar & M. Molina-Solana & Y. Guo & M. C. Pegalajar, 2019. "Generalised Regression Hypothesis Induction for Energy Consumption Forecasting," Energies, MDPI, vol. 12(6), pages 1-22, March.
  87. Miriam Benedetti & Francesca Bonfà & Vito Introna & Annalisa Santolamazza & Stefano Ubertini, 2019. "Real Time Energy Performance Control for Industrial Compressed Air Systems: Methodology and Applications," Energies, MDPI, vol. 12(20), pages 1-28, October.
  88. Ding, Jia & Wang, Maolin & Ping, Zuowei & Fu, Dongfei & Vassiliadis, Vassilios S., 2020. "An integrated method based on relevance vector machine for short-term load forecasting," European Journal of Operational Research, Elsevier, vol. 287(2), pages 497-510.
  89. Lawal, Abiola S. & Servadio, Joseph L. & Davis, Tate & Ramaswami, Anu & Botchwey, Nisha & Russell, Armistead G., 2021. "Orthogonalization and machine learning methods for residential energy estimation with social and economic indicators," Applied Energy, Elsevier, vol. 283(C).
  90. Balderrama, Sergio & Lombardi, Francesco & Stevanato, Nicolo & Peña, Gabriela & Colombo, Emanuela & Quoilin, Sylvain, 2021. "Surrogate models for rural energy planning: Application to Bolivian lowlands isolated communities," Energy, Elsevier, vol. 232(C).
  91. Eren, Yavuz & Küçükdemiral, İbrahim, 2024. "A comprehensive review on deep learning approaches for short-term load forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
  92. Houben, Nikolaus & Cosic, Armin & Stadler, Michael & Mansoor, Muhammad & Zellinger, Michael & Auer, Hans & Ajanovic, Amela & Haas, Reinhard, 2023. "Optimal dispatch of a multi-energy system microgrid under uncertainty: A renewable energy community in Austria," Applied Energy, Elsevier, vol. 337(C).
  93. Jason Runge & Radu Zmeureanu, 2019. "Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review," Energies, MDPI, vol. 12(17), pages 1-27, August.
  94. Wang, Qiang & Jiang, Feng, 2019. "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," Energy, Elsevier, vol. 178(C), pages 781-803.
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