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A hybrid model for building energy consumption forecasting using long short term memory networks

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  1. Karthick Tamilarasu & Charles Raja Sathiasamuel & Jeslin Drusila Nesamalar Joseph & Rajvikram Madurai Elavarasan & Lucian Mihet-Popa, 2021. "Reinforced Demand Side Management for Educational Institution with Incorporation of User’s Comfort," Energies, MDPI, vol. 14(10), pages 1-22, May.
  2. Fang, Lei & He, Bin, 2023. "A deep learning framework using multi-feature fusion recurrent neural networks for energy consumption forecasting," Applied Energy, Elsevier, vol. 348(C).
  3. Neshat, Mehdi & Nezhad, Meysam Majidi & Sergiienko, Nataliia Y. & Mirjalili, Seyedali & Piras, Giuseppe & Garcia, Davide Astiaso, 2022. "Wave power forecasting using an effective decomposition-based convolutional Bi-directional model with equilibrium Nelder-Mead optimiser," Energy, Elsevier, vol. 256(C).
  4. Guo, Jing & Lin, Penghui & Zhang, Limao & Pan, Yue & Xiao, Zhonghua, 2023. "Dynamic adaptive encoder-decoder deep learning networks for multivariate time series forecasting of building energy consumption," Applied Energy, Elsevier, vol. 350(C).
  5. Chakraborty, Debaditya & Alam, Arafat & Chaudhuri, Saptarshi & Başağaoğlu, Hakan & Sulbaran, Tulio & Langar, Sandeep, 2021. "Scenario-based prediction of climate change impacts on building cooling energy consumption with explainable artificial intelligence," Applied Energy, Elsevier, vol. 291(C).
  6. Tang, Lingfeng & Xie, Haipeng & Wang, Xiaoyang & Bie, Zhaohong, 2023. "Privacy-preserving knowledge sharing for few-shot building energy prediction: A federated learning approach," Applied Energy, Elsevier, vol. 337(C).
  7. Liu, Che & Li, Fan & Zhang, Chenghui & Sun, Bo & Zhang, Guanguan, 2023. "A day-ahead prediction method for high-resolution electricity consumption in residential units," Energy, Elsevier, vol. 265(C).
  8. Jeong, Dongyeon & Park, Chiwoo & Ko, Young Myoung, 2021. "Short-term electric load forecasting for buildings using logistic mixture vector autoregressive model with curve registration," Applied Energy, Elsevier, vol. 282(PB).
  9. Tong Lei & Zuoqin Qian & Jie Ren, 2023. "Performance Evaluation of LiBr-H 2 O and LiCl-H 2 O Working Pairs in Compression-Assisted Double-Effect Absorption Refrigeration Systems for Utilization of Low-Temperature Heat Sources," Energies, MDPI, vol. 16(16), pages 1-19, August.
  10. Ding, Zhikun & Chen, Weilin & Hu, Ting & Xu, Xiaoxiao, 2021. "Evolutionary double attention-based long short-term memory model for building energy prediction: Case study of a green building," Applied Energy, Elsevier, vol. 288(C).
  11. Qian Liu & Zhuxin Zhang & Tuo Jia & Lixin Wang & Dingxuan Zhao, 2022. "Energy Consumption Analysis of Helicopter Traction Device: A Modeling Method Considering Both Dynamic and Energy Consumption Characteristics of Mechanical Systems," Energies, MDPI, vol. 15(20), pages 1-20, October.
  12. Moreno Jaramillo, Andres F. & Laverty, David M. & Morrow, D. John & Martinez del Rincon, Jesús & Foley, Aoife M., 2021. "Load modelling and non-intrusive load monitoring to integrate distributed energy resources in low and medium voltage networks," Renewable Energy, Elsevier, vol. 179(C), pages 445-466.
  13. Yun Duan, 2022. "A Novel Interval Energy-Forecasting Method for Sustainable Building Management Based on Deep Learning," Sustainability, MDPI, vol. 14(14), pages 1-18, July.
  14. Sunil Kumar Mohapatra & Sushruta Mishra & Hrudaya Kumar Tripathy & Akash Kumar Bhoi & Paolo Barsocchi, 2021. "A Pragmatic Investigation of Energy Consumption and Utilization Models in the Urban Sector Using Predictive Intelligence Approaches," Energies, MDPI, vol. 14(13), pages 1-28, June.
  15. Yongrui Qin & Meng Zhao & Qingcheng Lin & Xuefeng Li & Jing Ji, 2022. "Data-Driven Building Energy Consumption Prediction Model Based on VMD-SA-DBN," Mathematics, MDPI, vol. 10(17), pages 1-10, August.
  16. Jason Runge & Radu Zmeureanu, 2021. "A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings," Energies, MDPI, vol. 14(3), pages 1-26, January.
  17. Kamel, Ehsan & Sheikh, Shaya & Huang, Xueqing, 2020. "Data-driven predictive models for residential building energy use based on the segregation of heating and cooling days," Energy, Elsevier, vol. 206(C).
  18. Mobarak Abumohsen & Amani Yousef Owda & Majdi Owda, 2023. "Electrical Load Forecasting Using LSTM, GRU, and RNN Algorithms," Energies, MDPI, vol. 16(5), pages 1-31, February.
  19. Md Tariqul Islam & M. J. Hossain, 2023. "Artificial Intelligence for Hosting Capacity Analysis: A Systematic Literature Review," Energies, MDPI, vol. 16(4), pages 1-33, February.
  20. Daniela Durand & Jose Aguilar & Maria D. R-Moreno, 2022. "An Analysis of the Energy Consumption Forecasting Problem in Smart Buildings Using LSTM," Sustainability, MDPI, vol. 14(20), pages 1-22, October.
  21. Meihang Zhang & Hua Zhang & Wei Yan & Zhigang Jiang & Shuo Zhu, 2023. "An Integrated Deep-Learning-Based Approach for Energy Consumption Prediction of Machining Systems," Sustainability, MDPI, vol. 15(7), pages 1-17, March.
  22. Neshat, Mehdi & Mirjalili, Seyedali & Sergiienko, Nataliia Y. & Esmaeilzadeh, Soheil & Amini, Erfan & Heydari, Azim & Garcia, Davide Astiaso, 2022. "Layout optimisation of offshore wave energy converters using a novel multi-swarm cooperative algorithm with backtracking strategy: A case study from coasts of Australia," Energy, Elsevier, vol. 239(PE).
  23. Elsa Chaerun Nisa & Yean-Der Kuan, 2021. "Comparative Assessment to Predict and Forecast Water-Cooled Chiller Power Consumption Using Machine Learning and Deep Learning Algorithms," Sustainability, MDPI, vol. 13(2), pages 1-18, January.
  24. Manuel Jaramillo & Wilson Pavón & Lisbeth Jaramillo, 2024. "Adaptive Forecasting in Energy Consumption: A Bibliometric Analysis and Review," Data, MDPI, vol. 9(1), pages 1-23, January.
  25. Hu, Yusha & Man, Yi, 2023. "Energy consumption and carbon emissions forecasting for industrial processes: Status, challenges and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
  26. Yang, Wangwang & Shi, Jing & Li, Shujian & Song, Zhaofang & Zhang, Zitong & Chen, Zexu, 2022. "A combined deep learning load forecasting model of single household resident user considering multi-time scale electricity consumption behavior," Applied Energy, Elsevier, vol. 307(C).
  27. Marek Vochozka & Jaromir Vrbka & Petr Suler, 2020. "Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM," Sustainability, MDPI, vol. 12(18), pages 1-17, September.
  28. Aridi, Rima & Faraj, Jalal & Ali, Samer & Lemenand, Thierry & khaled, Mahmoud, 2022. "A comprehensive review on hybrid heat recovery systems: Classifications, applications, pros and cons, and new systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
  29. Behm, Christian & Nolting, Lars & Praktiknjo, Aaron, 2020. "How to model European electricity load profiles using artificial neural networks," Applied Energy, Elsevier, vol. 277(C).
  30. dos Santos Ferreira, Greicili & Martins dos Santos, Deilson & Luciano Avila, Sérgio & Viana Luiz Albani, Vinicius & Cardoso Orsi, Gustavo & Cesar Cordeiro Vieira, Pedro & Nilson Rodrigues, Rafael, 2023. "Short- and long-term forecasting for building energy consumption considering IPMVP recommendations, WEO and COP27 scenarios," Applied Energy, Elsevier, vol. 339(C).
  31. Fath U Min Ullah & Noman Khan & Tanveer Hussain & Mi Young Lee & Sung Wook Baik, 2021. "Diving Deep into Short-Term Electricity Load Forecasting: Comparative Analysis and a Novel Framework," Mathematics, MDPI, vol. 9(6), pages 1-22, March.
  32. Shamim Akhtar & Muhamad Zahim Bin Sujod & Syed Sajjad Hussain Rizvi, 2022. "An Intelligent Data-Driven Approach for Electrical Energy Load Management Using Machine Learning Algorithms," Energies, MDPI, vol. 15(15), pages 1-19, August.
  33. Xiaohui Gao, 2022. "Monthly Wind Power Forecasting: Integrated Model Based on Grey Model and Machine Learning," Sustainability, MDPI, vol. 14(22), pages 1-14, November.
  34. Lee, Zachary E. & Zhang, K. Max, 2021. "Scalable identification and control of residential heat pumps: A minimal hardware approach," Applied Energy, Elsevier, vol. 286(C).
  35. Razak Olu-Ajayi & Hafiz Alaka & Hakeem Owolabi & Lukman Akanbi & Sikiru Ganiyu, 2023. "Data-Driven Tools for Building Energy Consumption Prediction: A Review," Energies, MDPI, vol. 16(6), pages 1-20, March.
  36. Zheng, Peijun & Zhou, Heng & Liu, Jiang & Nakanishi, Yosuke, 2023. "Interpretable building energy consumption forecasting using spectral clustering algorithm and temporal fusion transformers architecture," Applied Energy, Elsevier, vol. 349(C).
  37. Lee, Juyong & Cho, Youngsang, 2022. "National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?," Energy, Elsevier, vol. 239(PD).
  38. Yijue Sun & Fenglin Zhang, 2022. "Grey Multivariable Prediction Model of Energy Consumption with Different Fractional Orders," Sustainability, MDPI, vol. 14(24), pages 1-17, December.
  39. Ijaz Ul Haq & Amin Ullah & Samee Ullah Khan & Noman Khan & Mi Young Lee & Seungmin Rho & Sung Wook Baik, 2021. "Sequential Learning-Based Energy Consumption Prediction Model for Residential and Commercial Sectors," Mathematics, MDPI, vol. 9(6), pages 1-17, March.
  40. Raman MR, Gauthama & Somu, Nivethitha & Mathur, A.P., 2020. "A multilayer perceptron model for anomaly detection in water treatment plants," International Journal of Critical Infrastructure Protection, Elsevier, vol. 31(C).
  41. Xin Ma & Yubin Cai & Hong Yuan & Yanqiao Deng, 2023. "Partially Linear Component Support Vector Machine for Primary Energy Consumption Forecasting of the Electric Power Sector in the United States," Sustainability, MDPI, vol. 15(9), pages 1-26, April.
  42. Haizhou Fang & Hongwei Tan & Ningfang Dai & Zhaohui Liu & Risto Kosonen, 2023. "Hourly Building Energy Consumption Prediction Using a Training Sample Selection Method Based on Key Feature Search," Sustainability, MDPI, vol. 15(9), pages 1-23, May.
  43. Olamide Jogunola & Bamidele Adebisi & Khoa Van Hoang & Yakubu Tsado & Segun I. Popoola & Mohammad Hammoudeh & Raheel Nawaz, 2022. "CBLSTM-AE: A Hybrid Deep Learning Framework for Predicting Energy Consumption," Energies, MDPI, vol. 15(3), pages 1-16, January.
  44. Ru-Guan Wang & Wen-Jen Ho & Kuei-Chun Chiang & Yung-Chieh Hung & Jen-Kuo Tai & Jia-Cheng Tan & Mei-Ling Chuang & Chi-Yun Ke & Yi-Fan Chien & An-Ping Jeng & Chien-Cheng Chou, 2023. "Analyzing Long-Term and High Instantaneous Power Consumption of Buildings from Smart Meter Big Data with Deep Learning and Knowledge Graph Techniques," Energies, MDPI, vol. 16(19), pages 1-24, September.
  45. Fang, Xi & Gong, Guangcai & Li, Guannan & Chun, Liang & Li, Wenqiang & Peng, Pei, 2021. "A hybrid deep transfer learning strategy for short term cross-building energy prediction," Energy, Elsevier, vol. 215(PB).
  46. Fekri, Mohammad Navid & Patel, Harsh & Grolinger, Katarina & Sharma, Vinay, 2021. "Deep learning for load forecasting with smart meter data: Online Adaptive Recurrent Neural Network," Applied Energy, Elsevier, vol. 282(PA).
  47. Chan-Wook Lee & Do-Guen Yoo, 2021. "Development of Leakage Detection Model and Its Application for Water Distribution Networks Using RNN-LSTM," Sustainability, MDPI, vol. 13(16), pages 1-15, August.
  48. Luo, X.J. & Oyedele, Lukumon O. & Ajayi, Anuoluwapo O. & Akinade, Olugbenga O. & Owolabi, Hakeem A. & Ahmed, Ashraf, 2020. "Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
  49. Bampoulas, Adamantios & Pallonetto, Fabiano & Mangina, Eleni & Finn, Donal P., 2022. "An ensemble learning-based framework for assessing the energy flexibility of residential buildings with multicomponent energy systems," Applied Energy, Elsevier, vol. 315(C).
  50. Byung-Ki Jeon & Eui-Jong Kim, 2021. "LSTM-Based Model Predictive Control for Optimal Temperature Set-Point Planning," Sustainability, MDPI, vol. 13(2), pages 1-14, January.
  51. Jahangir Hossain & Aida. F. A. Kadir & Ainain. N. Hanafi & Hussain Shareef & Tamer Khatib & Kyairul. A. Baharin & Mohamad. F. Sulaima, 2023. "A Review on Optimal Energy Management in Commercial Buildings," Energies, MDPI, vol. 16(4), pages 1-40, February.
  52. Sachin Kahawala & Daswin De Silva & Seppo Sierla & Damminda Alahakoon & Rashmika Nawaratne & Evgeny Osipov & Andrew Jennings & Valeriy Vyatkin, 2021. "Robust Multi-Step Predictor for Electricity Markets with Real-Time Pricing," Energies, MDPI, vol. 14(14), pages 1-20, July.
  53. Wenninger, Simon & Kaymakci, Can & Wiethe, Christian, 2022. "Explainable long-term building energy consumption prediction using QLattice," Applied Energy, Elsevier, vol. 308(C).
  54. Thibaut Th'eate & Antonio Sutera & Damien Ernst, 2023. "Matching of Everyday Power Supply and Demand with Dynamic Pricing: Problem Formalisation and Conceptual Analysis," Papers 2301.11587, arXiv.org.
  55. Xiao Li & Liping Zhang & Sidong Zeng & Zhenyu Tang & Lina Liu & Qin Zhang & Zhengyang Tang & Xiaojun Hua, 2022. "Predicting Monthly Runoff of the Upper Yangtze River Based on Multiple Machine Learning Models," Sustainability, MDPI, vol. 14(18), pages 1-23, September.
  56. Somu, Nivethitha & Raman M R, Gauthama & Ramamritham, Krithi, 2021. "A deep learning framework for building energy consumption forecast," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
  57. Fan, Junliang & Zheng, Jing & Wu, Lifeng & Zhang, Fucang, 2021. "Estimation of daily maize transpiration using support vector machines, extreme gradient boosting, artificial and deep neural networks models," Agricultural Water Management, Elsevier, vol. 245(C).
  58. Li, Guannan & Li, Fan & Ahmad, Tanveer & Liu, Jiangyan & Li, Tao & Fang, Xi & Wu, Yubei, 2022. "Performance evaluation of sequence-to-sequence-Attention model for short-term multi-step ahead building energy predictions," Energy, Elsevier, vol. 259(C).
  59. Fatemi Bushehri, Seyyed Mohammad Mehdi & Dehghan Khavari, Saeed & Mirjalili, Seyed Hossein & Babaei Meybodi, Hamid & Sardari Zarchi, Mohsen, 2022. "Energy Consumption Prediction in Iran: A Hybrid Machine Learning and Genetic Algorithm Method with Sustainable Development Considerations," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 6(2).
  60. Lu, Hongfang & Cheng, Feifei & Ma, Xin & Hu, Gang, 2020. "Short-term prediction of building energy consumption employing an improved extreme gradient boosting model: A case study of an intake tower," Energy, Elsevier, vol. 203(C).
  61. Munkhammar, Joakim & van der Meer, Dennis & Widén, Joakim, 2021. "Very short term load forecasting of residential electricity consumption using the Markov-chain mixture distribution (MCM) model," Applied Energy, Elsevier, vol. 282(PA).
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