IDEAS home Printed from https://ideas.repec.org/r/gam/jeners/v10y2017i10p1525-d114249.html
   My bibliography  Save this item

Building Energy Consumption Prediction: An Extreme Deep Learning Approach

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Guixiang Xue & Yu Pan & Tao Lin & Jiancai Song & Chengying Qi & Zhipan Wang, 2019. "District Heating Load Prediction Algorithm Based on Feature Fusion LSTM Model," Energies, MDPI, vol. 12(11), pages 1-21, June.
  2. Noye, Sarah & Mulero Martinez, Rubén & Carnieletto, Laura & De Carli, Michele & Castelruiz Aguirre, Amaia, 2022. "A review of advanced ground source heat pump control: Artificial intelligence for autonomous and adaptive control," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
  3. Vincent, Immanuel & Lee, Eun-Chong & Cha, Kyung-Ho & Kim, Hyung-Man, 2021. "The WASP model on the symbiotic strategy of renewable and nuclear power for the future of ‘Renewable Energy 3020’ policy in South Korea," Renewable Energy, Elsevier, vol. 172(C), pages 929-940.
  4. Chen, Zhiwen & Deng, Qiao & Ren, Hao & Zhao, Zhengrun & Peng, Tao & Yang, Chunhua & Gui, Weihua, 2022. "A new energy consumption prediction method for chillers based on GraphSAGE by combining empirical knowledge and operating data," Applied Energy, Elsevier, vol. 310(C).
  5. Wei Jiang & Yanhe Xu & Yahui Shan & Han Liu, 2018. "Degradation Tendency Measurement of Aircraft Engines Based on FEEMD Permutation Entropy and Regularized Extreme Learning Machine Using Multi-Sensor Data," Energies, MDPI, vol. 11(12), pages 1-18, November.
  6. Zhang, Yuhang & Zhang, Yi & Yi Zhang, & Zhang, Chengxu, 2022. "Effect of physical, environmental, and social factors on prediction of building energy consumption for public buildings based on real-world big data," Energy, Elsevier, vol. 261(PB).
  7. Fan, Cheng & Sun, Yongjun & Zhao, Yang & Song, Mengjie & Wang, Jiayuan, 2019. "Deep learning-based feature engineering methods for improved building energy prediction," Applied Energy, Elsevier, vol. 240(C), pages 35-45.
  8. Abdurahman Alrobaie & Moncef Krarti, 2022. "A Review of Data-Driven Approaches for Measurement and Verification Analysis of Building Energy Retrofits," Energies, MDPI, vol. 15(21), pages 1-30, October.
  9. Rubén Pérez-Chacón & José M. Luna-Romera & Alicia Troncoso & Francisco Martínez-Álvarez & José C. Riquelme, 2018. "Big Data Analytics for Discovering Electricity Consumption Patterns in Smart Cities," Energies, MDPI, vol. 11(3), pages 1-19, March.
  10. Wei, Yixuan & Xia, Liang & Pan, Song & Wu, Jinshun & Zhang, Xingxing & Han, Mengjie & Zhang, Weiya & Xie, Jingchao & Li, Qingping, 2019. "Prediction of occupancy level and energy consumption in office building using blind system identification and neural networks," Applied Energy, Elsevier, vol. 240(C), pages 276-294.
  11. 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).
  12. Zhong, Hai & Wang, Jiajun & Jia, Hongjie & Mu, Yunfei & Lv, Shilei, 2019. "Vector field-based support vector regression for building energy consumption prediction," Applied Energy, Elsevier, vol. 242(C), pages 403-414.
  13. 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.
  14. Jin-Young Kim & Sung-Bae Cho, 2019. "Electric Energy Consumption Prediction by Deep Learning with State Explainable Autoencoder," Energies, MDPI, vol. 12(4), pages 1-14, February.
  15. Isaac Kofi Nti & Adebayo Felix Adekoya & Benjamin Asubam Weyori & Owusu Nyarko-Boateng, 2022. "Applications of artificial intelligence in engineering and manufacturing: a systematic review," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1581-1601, August.
  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. 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).
  18. Piotr Michalak & Krzysztof Szczotka & Jakub Szymiczek, 2023. "Audit-Based Energy Performance Analysis of Multifamily Buildings in South-East Poland," Energies, MDPI, vol. 16(12), pages 1-21, June.
  19. Seok-Jun Bu & Sung-Bae Cho, 2020. "Time Series Forecasting with Multi-Headed Attention-Based Deep Learning for Residential Energy Consumption," Energies, MDPI, vol. 13(18), pages 1-16, September.
  20. Gde Dharma Nugraha & Ardiansyah Musa & Jaiyoung Cho & Kishik Park & Deokjai Choi, 2018. "Lambda-Based Data Processing Architecture for Two-Level Load Forecasting in Residential Buildings," Energies, MDPI, vol. 11(4), pages 1-20, March.
  21. 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.
  22. Pan, Yue & Zhang, Limao, 2020. "Data-driven estimation of building energy consumption with multi-source heterogeneous data," Applied Energy, Elsevier, vol. 268(C).
  23. Prince Waqas Khan & Yongjun Kim & Yung-Cheol Byun & Sang-Joon Lee, 2021. "Influencing Factors Evaluation of Machine Learning-Based Energy Consumption Prediction," Energies, MDPI, vol. 14(21), pages 1-22, November.
  24. Muhammad Ali & Dae-Hee Son & Sang-Hee Kang & Soon-Ryul Nam, 2017. "An Accurate CT Saturation Classification Using a Deep Learning Approach Based on Unsupervised Feature Extraction and Supervised Fine-Tuning Strategy," Energies, MDPI, vol. 10(11), pages 1-24, November.
  25. Chou, Jui-Sheng & Truong, Dinh-Nhat & Kuo, Ching-Chiun, 2021. "Imaging time-series with features to enable visual recognition of regional energy consumption by bio-inspired optimization of deep learning," Energy, Elsevier, vol. 224(C).
  26. 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.
  27. Wenhui Zhao & Tong Li & Danyang Xu & Zhaohua Wang, 2024. "A global forecasting method of heterogeneous household short-term load based on pre-trained autoencoder and deep-LSTM model," Annals of Operations Research, Springer, vol. 339(1), pages 227-259, August.
  28. Tran, Duc-Hoc & Luong, Duc-Long & Chou, Jui-Sheng, 2020. "Nature-inspired metaheuristic ensemble model for forecasting energy consumption in residential buildings," Energy, Elsevier, vol. 191(C).
  29. Pedone, Livio & Molaioni, Filippo & Vallati, Andrea & Pampanin, Stefano, 2023. "Energy refurbishment planning of Italian school buildings using data-driven predictive models," Applied Energy, Elsevier, vol. 350(C).
  30. Muhammad Waseem Ahmad & Anthony Mouraud & Yacine Rezgui & Monjur Mourshed, 2018. "Deep Highway Networks and Tree-Based Ensemble for Predicting Short-Term Building Energy Consumption," Energies, MDPI, vol. 11(12), pages 1-21, December.
  31. Francisco Martínez-Álvarez & Alicia Troncoso & José C. Riquelme, 2018. "Data Science and Big Data in Energy Forecasting," Energies, MDPI, vol. 11(11), pages 1-2, November.
  32. Chih-Chiang Wei, 2019. "Evaluation of Photovoltaic Power Generation by Using Deep Learning in Solar Panels Installed in Buildings," Energies, MDPI, vol. 12(18), pages 1-18, September.
  33. Marcin Relich & Arkadiusz Gola & Małgorzata Jasiulewicz-Kaczmarek, 2022. "Identifying Improvement Opportunities in Product Design for Reducing Energy Consumption," Energies, MDPI, vol. 15(24), pages 1-19, December.
  34. Yu, Hang & Niu, Songyan & Zhang, Yumeng & Jian, Linni, 2020. "An integrated and reconfigurable hybrid AC/DC microgrid architecture with autonomous power flow control for nearly/net zero energy buildings," Applied Energy, Elsevier, vol. 263(C).
  35. Xing, Yazhou & Zhang, Su & Wen, Peng & Shao, Limin & Rouyendegh, Babak Daneshvar, 2020. "Load prediction in short-term implementing the multivariate quantile regression," Energy, Elsevier, vol. 196(C).
  36. Habeebur Rahman & Iniyan Selvarasan & Jahitha Begum A, 2018. "Short-Term Forecasting of Total Energy Consumption for India-A Black Box Based Approach," Energies, MDPI, vol. 11(12), pages 1-21, December.
  37. Papadopoulos, Sokratis & Kontokosta, Constantine E., 2019. "Grading buildings on energy performance using city benchmarking data," Applied Energy, Elsevier, vol. 233, pages 244-253.
  38. Sana Iqbal & Mohammad Sarfraz & Mohammad Ayyub & Mohd Tariq & Ripon K. Chakrabortty & Michael J. Ryan & Basem Alamri, 2021. "A Comprehensive Review on Residential Demand Side Management Strategies in Smart Grid Environment," Sustainability, MDPI, vol. 13(13), pages 1-23, June.
  39. Linlin Zhao & Zhansheng Liu & Jasper Mbachu, 2019. "Energy Management through Cost Forecasting for Residential Buildings in New Zealand," Energies, MDPI, vol. 12(15), pages 1-24, July.
  40. Streltsov, Artem & Malof, Jordan M. & Huang, Bohao & Bradbury, Kyle, 2020. "Estimating residential building energy consumption using overhead imagery," Applied Energy, Elsevier, vol. 280(C).
  41. Zheng, Zhi-xin & Li, Jun-qing & Duan, Pei-yong, 2019. "Optimal chiller loading by improved artificial fish swarm algorithm for energy saving," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 155(C), pages 227-243.
  42. Chengdong Li & Zixiang Ding & Jianqiang Yi & Yisheng Lv & Guiqing Zhang, 2018. "Deep Belief Network Based Hybrid Model for Building Energy Consumption Prediction," Energies, MDPI, vol. 11(1), pages 1-26, January.
  43. Shailendra Singh & Abdulsalam Yassine, 2018. "Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting," Energies, MDPI, vol. 11(2), pages 1-26, February.
  44. Fateme Dinmohammadi & Yuxuan Han & Mahmood Shafiee, 2023. "Predicting Energy Consumption in Residential Buildings Using Advanced Machine Learning Algorithms," Energies, MDPI, vol. 16(9), pages 1-23, April.
  45. Amber, K.P. & Ahmad, R. & Aslam, M.W. & Kousar, A. & Usman, M. & Khan, M.S., 2018. "Intelligent techniques for forecasting electricity consumption of buildings," Energy, Elsevier, vol. 157(C), pages 886-893.
  46. 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).
  47. 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.
  48. Zheng, Zhuang & Chen, Hainan & Luo, Xiaowei, 2019. "A Kalman filter-based bottom-up approach for household short-term load forecast," Applied Energy, Elsevier, vol. 250(C), pages 882-894.
  49. Hwang, Jun Kwon & Yun, Geun Young & Lee, Sukho & Seo, Hyeongjoon & Santamouris, Mat, 2020. "Using deep learning approaches with variable selection process to predict the energy performance of a heating and cooling system," Renewable Energy, Elsevier, vol. 149(C), pages 1227-1245.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.