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Predicting residential energy consumption using CNN-LSTM neural networks

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  1. Zhang, Yi-Ming & Wang, Hao, 2023. "Multi-head attention-based probabilistic CNN-BiLSTM for day-ahead wind speed forecasting," Energy, Elsevier, vol. 278(PA).
  2. Guo, Yanhua & Wang, Ningbo & Shao, Shuangquan & Huang, Congqi & Zhang, Zhentao & Li, Xiaoqiong & Wang, Youdong, 2024. "A review on hybrid physics and data-driven modeling methods applied in air source heat pump systems for energy efficiency improvement," Renewable and Sustainable Energy Reviews, Elsevier, vol. 204(C).
  3. Maleki, Neda & Lundström, Oxana & Musaddiq, Arslan & Jeansson, John & Olsson, Tobias & Ahlgren, Fredrik, 2024. "Future energy insights: Time-series and deep learning models for city load forecasting," Applied Energy, Elsevier, vol. 374(C).
  4. 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).
  5. Chongchong Xu & Zhicheng Liao & Chaojie Li & Xiaojun Zhou & Renyou Xie, 2022. "Review on Interpretable Machine Learning in Smart Grid," Energies, MDPI, vol. 15(12), pages 1-31, June.
  6. Li, Jingmiao & Wang, Jun, 2020. "Forcasting of energy futures market and synchronization based on stochastic gated recurrent unit model," Energy, Elsevier, vol. 213(C).
  7. 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).
  8. Ihab Taleb & Guillaume Guerard & Frédéric Fauberteau & Nga Nguyen, 2022. "A Flexible Deep Learning Method for Energy Forecasting," Energies, MDPI, vol. 15(11), pages 1-16, May.
  9. Zhang, Yunfei & Zhou, Zhihua & Liu, Junwei & Yuan, Jianjuan, 2022. "Data augmentation for improving heating load prediction of heating substation based on TimeGAN," Energy, Elsevier, vol. 260(C).
  10. Bilgili, Mehmet & Pinar, Engin, 2023. "Gross electricity consumption forecasting using LSTM and SARIMA approaches: A case study of Türkiye," Energy, Elsevier, vol. 284(C).
  11. Brucke, Karoline & Arens, Stefan & Telle, Jan-Simon & Steens, Thomas & Hanke, Benedikt & von Maydell, Karsten & Agert, Carsten, 2021. "A non-intrusive load monitoring approach for very short-term power predictions in commercial buildings," Applied Energy, Elsevier, vol. 292(C).
  12. Chen, Sai & Song, Yan & Ding, Yueting & Zhang, Ming & Nie, Rui, 2021. "Using long short-term memory model to study risk assessment and prediction of China’s oil import from the perspective of resilience theory," Energy, Elsevier, vol. 215(PB).
  13. Yan Liu & Ting Zhang & Aiqing Kang & Jianzhu Li & Xiaohui Lei, 2021. "Research on Runoff Simulations Using Deep-Learning Methods," Sustainability, MDPI, vol. 13(3), pages 1-20, January.
  14. Salam, Abdulwahed & El Hibaoui, Abdelaaziz, 2021. "Energy consumption prediction model with deep inception residual network inspiration and LSTM," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 190(C), pages 97-109.
  15. 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.
  16. Gonçalves, Rui & Ribeiro, Vitor Miguel & Pereira, Fernando Lobo, 2023. "Variable Split Convolutional Attention: A novel Deep Learning model applied to the household electric power consumption," Energy, Elsevier, vol. 274(C).
  17. 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).
  18. Sun, Yeran & Wang, Shaohua & Zhang, Xucai & Chan, Ting On & Wu, Wenjie, 2021. "Estimating local-scale domestic electricity energy consumption using demographic, nighttime light imagery and Twitter data," Energy, Elsevier, vol. 226(C).
  19. Yang, Xilian & Zhao, Qunfei & Wang, Yuzhang & Cheng, Kanru, 2023. "Fault signal reconstruction for multi-sensors in gas turbine control systems based on prior knowledge from time series representation," Energy, Elsevier, vol. 262(PA).
  20. Wang, Guimei & Mukhtar, Azfarizal & Moayedi, Hossein & Khalilpoor, Nima & Tt, Quynh, 2024. "Application and evaluation of the evolutionary algorithms combined with conventional neural network to determine the building energy consumption of the residential sector," Energy, Elsevier, vol. 298(C).
  21. Sekhar, Charan & Dahiya, Ratna, 2023. "Robust framework based on hybrid deep learning approach for short term load forecasting of building electricity demand," Energy, Elsevier, vol. 268(C).
  22. 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.
  23. Zang, Haixiang & Xu, Ruiqi & Cheng, Lilin & Ding, Tao & Liu, Ling & Wei, Zhinong & Sun, Guoqiang, 2021. "Residential load forecasting based on LSTM fusing self-attention mechanism with pooling," Energy, Elsevier, vol. 229(C).
  24. Varlamis, Iraklis & Sardianos, Christos & Chronis, Christos & Dimitrakopoulos, George & Himeur, Yassine & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2022. "Smart fusion of sensor data and human feedback for personalized energy-saving recommendations," Applied Energy, Elsevier, vol. 305(C).
  25. Gao, Bixuan & Huang, Xiaoqiao & Shi, Junsheng & Tai, Yonghang & Zhang, Jun, 2020. "Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks," Renewable Energy, Elsevier, vol. 162(C), pages 1665-1683.
  26. Jiang, Ben & Li, Yu & Rezgui, Yacine & Zhang, Chengyu & Wang, Peng & Zhao, Tianyi, 2024. "Multi-source domain generalization deep neural network model for predicting energy consumption in multiple office buildings," Energy, Elsevier, vol. 299(C).
  27. Muhammad Ali & Krishneel Prakash & Carlos Macana & Ali Kashif Bashir & Alireza Jolfaei & Awais Bokhari & Jiří Jaromír Klemeš & Hemanshu Pota, 2022. "Modeling Residential Electricity Consumption from Public Demographic Data for Sustainable Cities," Energies, MDPI, vol. 15(6), pages 1-16, March.
  28. 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.
  29. Chaturvedi, Shobhit & Rajasekar, Elangovan & Natarajan, Sukumar & McCullen, Nick, 2022. "A comparative assessment of SARIMA, LSTM RNN and Fb Prophet models to forecast total and peak monthly energy demand for India," Energy Policy, Elsevier, vol. 168(C).
  30. Shiyu Liu & Ou Liu & Junyang Chen, 2023. "A Review on Business Analytics: Definitions, Techniques, Applications and Challenges," Mathematics, MDPI, vol. 11(4), pages 1-20, February.
  31. Wang, Xin & Liu, Xiang & Bai, Yun, 2024. "Prediction of the temperature of diesel engine oil in railroad locomotives using compressed information-based data fusion method with attention-enhanced CNN-LSTM," Applied Energy, Elsevier, vol. 367(C).
  32. Qu, Jiaqi & Qian, Zheng & Pei, Yan, 2021. "Day-ahead hourly photovoltaic power forecasting using attention-based CNN-LSTM neural network embedded with multiple relevant and target variables prediction pattern," Energy, Elsevier, vol. 232(C).
  33. Ma, Tai-Yu & Faye, Sébastien, 2022. "Multistep electric vehicle charging station occupancy prediction using hybrid LSTM neural networks," Energy, Elsevier, vol. 244(PB).
  34. Abubakar Ahmad Musa & Adamu Hussaini & Weixian Liao & Fan Liang & Wei Yu, 2023. "Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey," Future Internet, MDPI, vol. 15(6), pages 1-24, May.
  35. Lan, Puzhe & Han, Dong & Xu, Xiaoyuan & Yan, Zheng & Ren, Xijun & Xia, Shiwei, 2022. "Data-driven state estimation of integrated electric-gas energy system," Energy, Elsevier, vol. 252(C).
  36. Liangfeng Zou & Yuanyuan Zha & Yuqing Diao & Chi Tang & Wenquan Gu & Dongguo Shao, 2023. "Coupling the Causal Inference and Informer Networks for Short-term Forecasting in Irrigation Water Usage," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 427-449, January.
  37. 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.
  38. Khan, Noman & Khan, Samee Ullah & Baik, Sung Wook, 2023. "Deep dive into hybrid networks: A comparative study and novel architecture for efficient power prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
  39. Li, Wei & Becker, Denis Mike, 2021. "Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling," Energy, Elsevier, vol. 237(C).
  40. Ma, Zhengjing & Mei, Gang, 2022. "A hybrid attention-based deep learning approach for wind power prediction," Applied Energy, Elsevier, vol. 323(C).
  41. Khan, Zulfiqar Ahmad & Khan, Shabbir Ahmad & Hussain, Tanveer & Baik, Sung Wook, 2024. "DSPM: Dual sequence prediction model for efficient energy management in micro-grid," Applied Energy, Elsevier, vol. 356(C).
  42. 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.
  43. Tereza Matasová & Marek Vochozka & Zuzana Rowland, 2022. "Alternative costs of equity of coal mining companies taking into account a context of the Russian Invasion into Ukraine," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 10(2), pages 394-407, December.
  44. Huang, Qian & Li, Jinghua & Zhu, Mengshu, 2020. "An improved convolutional neural network with load range discretization for probabilistic load forecasting," Energy, Elsevier, vol. 203(C).
  45. Tovar Rosas, Mario A. & Pérez, Miguel Robles & Martínez Pérez, E. Rafael, 2022. "Itineraries for charging and discharging a BESS using energy predictions based on a CNN-LSTM neural network model in BCS, Mexico," Renewable Energy, Elsevier, vol. 188(C), pages 1141-1165.
  46. Guillaume Guerard & Hugo Pousseur & Ihab Taleb, 2021. "Isolated Areas Consumption Short-Term Forecasting Method," Energies, MDPI, vol. 14(23), pages 1-23, November.
  47. 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.
  48. Lu, Renzhi & Bai, Ruichang & Ding, Yuemin & Wei, Min & Jiang, Junhui & Sun, Mingyang & Xiao, Feng & Zhang, Hai-Tao, 2021. "A hybrid deep learning-based online energy management scheme for industrial microgrid," Applied Energy, Elsevier, vol. 304(C).
  49. 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).
  50. Cui, Ying & Ye, Lin & Yao, Zhongran & Gu, Xiaoyong & Wang, Xinwang, 2024. "Performance optimization of cement calciner based on CFD simulation and machine learning algorithm," Energy, Elsevier, vol. 302(C).
  51. Zaki Masood & Rahma Gantassi & Ardiansyah & Yonghoon Choi, 2022. "A Multi-Step Time-Series Clustering-Based Seq2Seq LSTM Learning for a Single Household Electricity Load Forecasting," Energies, MDPI, vol. 15(7), pages 1-11, April.
  52. Subin Im & Hojun Lee & Don Hur & Minhan Yoon, 2023. "Comparison and Enhancement of Machine Learning Algorithms for Wind Turbine Output Prediction with Insufficient Data," Energies, MDPI, vol. 16(15), pages 1-16, August.
  53. Sun, Hongchang & Niu, Yanlei & Li, Chengdong & Zhou, Changgeng & Zhai, Wenwen & Chen, Zhe & Wu, Hao & Niu, Lanqiang, 2022. "Energy consumption optimization of building air conditioning system via combining the parallel temporal convolutional neural network and adaptive opposition-learning chimp algorithm," Energy, Elsevier, vol. 259(C).
  54. Huiqi Deng & Wenjie Chen & Guoru Huang, 2022. "Deep insight into daily runoff forecasting based on a CNN-LSTM model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 113(3), pages 1675-1696, September.
  55. 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).
  56. Ruijin Zhu & Weilin Guo & Xuejiao Gong, 2019. "Short-Term Load Forecasting for CCHP Systems Considering the Correlation between Heating, Gas and Electrical Loads Based on Deep Learning," Energies, MDPI, vol. 12(17), pages 1-18, August.
  57. Peng, Lu & Wang, Lin & Xia, De & Gao, Qinglu, 2022. "Effective energy consumption forecasting using empirical wavelet transform and long short-term memory," Energy, Elsevier, vol. 238(PB).
  58. Hu, Zehuan & Gao, Yuan & Sun, Luning & Mae, Masayuki & Imaizumi, Taiji, 2024. "Self-learning dynamic graph neural network with self-attention based on historical data and future data for multi-task multivariate residential air conditioning forecasting," Applied Energy, Elsevier, vol. 364(C).
  59. 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.
  60. Jiang, Feifeng & Ma, Jun & Li, Zheng & Ding, Yuexiong, 2022. "Prediction of energy use intensity of urban buildings using the semi-supervised deep learning model," Energy, Elsevier, vol. 249(C).
  61. Han, Yongming & Hao, Yuhang & Feng, Mingfei & Chen, Kai & Xing, Rumeng & Liu, Yuandong & Lin, Xiaoyong & Ma, Bo & Fan, Jinzhen & Geng, Zhiqiang, 2024. "Novel STAttention GraphWaveNet model for residential household appliance prediction and energy structure optimization," Energy, Elsevier, vol. 307(C).
  62. Mi, Hanning & Chen, Sijie & Li, Qingxin & Shi, Ming & Hou, Shuoming & Zheng, Linfeng & Xu, Chengke & Yan, Zheng & Li, Canbing, 2024. "Strategic bidding by predicting locational marginal price with aggregated supply curve," Energy, Elsevier, vol. 304(C).
  63. Yang, Qingqing & Li, Jianwei & Cao, Wanke & Li, Shuangqi & Lin, Jie & Huo, Da & He, Hongwen, 2020. "An improved vehicle to the grid method with battery longevity management in a microgrid application," Energy, Elsevier, vol. 198(C).
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  65. Lange, Jelto & Kaltschmitt, Martin, 2022. "Probabilistic day-ahead forecast of available thermal storage capacities in residential households," Applied Energy, Elsevier, vol. 306(PA).
  66. Ding, Jia & Zhao, Yuxuan & Jin, Junyang, 2023. "Forecasting natural gas consumption with multiple seasonal patterns," Applied Energy, Elsevier, vol. 337(C).
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  68. Namrye Son, 2021. "Comparison of the Deep Learning Performance for Short-Term Power Load Forecasting," Sustainability, MDPI, vol. 13(22), pages 1-25, November.
  69. Kshitij Sharma & Yogesh K. Dwivedi & Bhimaraya Metri, 2024. "Incorporating causality in energy consumption forecasting using deep neural networks," Annals of Operations Research, Springer, vol. 339(1), pages 537-572, August.
  70. Minkyu Kim & Chankook Park, 2021. "Academic Topics Related to Household Energy Consumption Using the Future Sign Detection Technique," Energies, MDPI, vol. 14(24), pages 1-24, December.
  71. 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).
  72. Fernández, Joaquín Delgado & Menci, Sergio Potenciano & Lee, Chul Min & Rieger, Alexander & Fridgen, Gilbert, 2022. "Privacy-preserving federated learning for residential short-term load forecasting," Applied Energy, Elsevier, vol. 326(C).
  73. Stephan Schlüter & Sejung Jung & Andreas von Döllen & Wonhee Lee, 2022. "An Alternative to Index-Based Gas Sourcing Using Neural Networks," Energies, MDPI, vol. 15(13), pages 1-11, June.
  74. Adila El Maghraoui & Younes Ledmaoui & Oussama Laayati & Hicham El Hadraoui & Ahmed Chebak, 2022. "Smart Energy Management: A Comparative Study of Energy Consumption Forecasting Algorithms for an Experimental Open-Pit Mine," Energies, MDPI, vol. 15(13), pages 1-22, June.
  75. AL-Alimi, Dalal & AlRassas, Ayman Mutahar & Al-qaness, Mohammed A.A. & Cai, Zhihua & Aseeri, Ahmad O. & Abd Elaziz, Mohamed & Ewees, Ahmed A., 2023. "TLIA: Time-series forecasting model using long short-term memory integrated with artificial neural networks for volatile energy markets," Applied Energy, Elsevier, vol. 343(C).
  76. Zhang, Yunfei & Zhou, Zhihua & Du, Yahui & Shen, Jun & Li, Zhenxing & Yuan, Jianjuan, 2023. "A data transfer method based on one dimensional convolutional neural network for cross-building load prediction," Energy, Elsevier, vol. 277(C).
  77. Jonas Sievers & Thomas Blank, 2023. "A Systematic Literature Review on Data-Driven Residential and Industrial Energy Management Systems," Energies, MDPI, vol. 16(4), pages 1-21, February.
  78. Jun Hao & Xiaolei Sun & Qianqian Feng, 2020. "A Novel Ensemble Approach for the Forecasting of Energy Demand Based on the Artificial Bee Colony Algorithm," Energies, MDPI, vol. 13(3), pages 1-25, January.
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  84. Wenhao Chen & Guangjie Han & Hongbo Zhu & Lyuchao Liao, 2022. "Short-Term Load Forecasting with an Ensemble Model Using Densely Residual Block and Bi-LSTM Based on the Attention Mechanism," Sustainability, MDPI, vol. 14(24), pages 1-16, December.
  85. Zhang, Yuejuan & Li, Xian-Xiang & Xin, Rui & Chew, Lup Wai & Liu, Chun-Ho, 2024. "Applicability of data-driven methods in modeling electricity demand-climate nexus: A tale of Singapore and Hong Kong," Energy, Elsevier, vol. 300(C).
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  88. Ruslan Abdulkadirov & Pavel Lyakhov & Nikolay Nagornov, 2023. "Survey of Optimization Algorithms in Modern Neural Networks," Mathematics, MDPI, vol. 11(11), pages 1-37, May.
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  92. Jinyuan Liu & Shouxi Wang & Nan Wei & Yi Yang & Yihao Lv & Xu Wang & Fanhua Zeng, 2023. "An Enhancement Method Based on Long Short-Term Memory Neural Network for Short-Term Natural Gas Consumption Forecasting," Energies, MDPI, vol. 16(3), pages 1-14, January.
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  94. Fatin Nadiah Yussof & Normah Maan & Mohd Nadzri Md Reba, 2021. "LSTM Networks to Improve the Prediction of Harmful Algal Blooms in the West Coast of Sabah," IJERPH, MDPI, vol. 18(14), pages 1-14, July.
  95. Khan, Zulfiqar Ahmad & Hussain, Tanveer & Baik, Sung Wook, 2023. "Dual stream network with attention mechanism for photovoltaic power forecasting," Applied Energy, Elsevier, vol. 338(C).
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  101. Wenchao Ban & Liangduo Shen, 2022. "PM2.5 Prediction Based on the CEEMDAN Algorithm and a Machine Learning Hybrid Model," Sustainability, MDPI, vol. 14(23), pages 1-15, December.
  102. Shen, Hui & Lin, Jane, 2020. "Investigation of crowdshipping delivery trip production with real-world data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 143(C).
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  107. Xiaoyu Lin & Hang Yu & Meng Wang & Chaoen Li & Zi Wang & Yin Tang, 2021. "Electricity Consumption Forecast of High-Rise Office Buildings Based on the Long Short-Term Memory Method," Energies, MDPI, vol. 14(16), pages 1-21, August.
  108. van Zyl, Corne & Ye, Xianming & Naidoo, Raj, 2024. "Harnessing eXplainable artificial intelligence for feature selection in time series energy forecasting: A comparative analysis of Grad-CAM and SHAP," Applied Energy, Elsevier, vol. 353(PA).
  109. Lei, Lei & Shao, Suola & Liang, Lixia, 2024. "An evolutionary deep learning model based on EWKM, random forest algorithm, SSA and BiLSTM for building energy consumption prediction," Energy, Elsevier, vol. 288(C).
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  111. Su, Miao & Nie, Yufei & Li, Jiankun & Yang, Lin & Kim, Woohyoung, 2024. "Futures markets and the baltic dry index: A prediction study based on deep learning," Research in International Business and Finance, Elsevier, vol. 71(C).
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