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Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study

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Cited by:

  1. Jesús Ferrero Bermejo & Juan Francisco Gómez Fernández & Rafael Pino & Adolfo Crespo Márquez & Antonio Jesús Guillén López, 2019. "Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants," Energies, MDPI, vol. 12(21), pages 1-18, October.
  2. Abhinandana Boodi & Karim Beddiar & Malek Benamour & Yassine Amirat & Mohamed Benbouzid, 2018. "Intelligent Systems for Building Energy and Occupant Comfort Optimization: A State of the Art Review and Recommendations," Energies, MDPI, vol. 11(10), pages 1-26, September.
  3. Hongwen Dou & Radu Zmeureanu, 2023. "Transfer Learning Prediction Performance of Chillers for Neural Network Models," Energies, MDPI, vol. 16(20), pages 1-16, October.
  4. Li, Shuangqi & He, Hongwen & Li, Jianwei, 2019. "Big data driven lithium-ion battery modeling method based on SDAE-ELM algorithm and data pre-processing technology," Applied Energy, Elsevier, vol. 242(C), pages 1259-1273.
  5. Wang, Endong & Alp, Neslihan & Shi, Jonathan & Wang, Chao & Zhang, Xiaodong & Chen, Hong, 2017. "Multi-criteria building energy performance benchmarking through variable clustering based compromise TOPSIS with objective entropy weighting," Energy, Elsevier, vol. 125(C), pages 197-210.
  6. Wang, Kung-Jeng & Lin, Chiuhsiang Joe & Dagne, Teshome Bekele & Woldegiorgis, Bereket Haile, 2022. "Bilayer stochastic optimization model for smart energy conservation systems," Energy, Elsevier, vol. 247(C).
  7. Kapp, Sean & Choi, Jun-Ki & Hong, Taehoon, 2023. "Predicting industrial building energy consumption with statistical and machine-learning models informed by physical system parameters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 172(C).
  8. Xu, Bin & Cheng, Yuan-xia & Chen, Xing-ni & Xie, Xing & Ji, Jie & Jiao, Dong-sheng, 2023. "Error correction method for heat flux and a new algorithm employed in inverting wall thermal resistance using an artificial neural network: Based on IN-SITU heat flux measurements," Energy, Elsevier, vol. 282(C).
  9. Xu, Lei & Wang, Shengwei & Tang, Rui, 2019. "Probabilistic load forecasting for buildings considering weather forecasting uncertainty and uncertain peak load," Applied Energy, Elsevier, vol. 237(C), pages 180-195.
  10. Huang, Y.W. & Chen, M.Q. & Li, Y. & Guo, J., 2016. "Modeling of chemical exergy of agricultural biomass using improved general regression neural network," Energy, Elsevier, vol. 114(C), pages 1164-1175.
  11. Raza, Muhammad Qamar & Nadarajah, Mithulananthan & Ekanayake, Chandima, 2017. "Demand forecast of PV integrated bioclimatic buildings using ensemble framework," Applied Energy, Elsevier, vol. 208(C), pages 1626-1638.
  12. Reynolds, Jonathan & Rezgui, Yacine & Kwan, Alan & Piriou, Solène, 2018. "A zone-level, building energy optimisation combining an artificial neural network, a genetic algorithm, and model predictive control," Energy, Elsevier, vol. 151(C), pages 729-739.
  13. 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.
  14. 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.
  15. Muhammad Salman Sami & Muhammad Abrar & Rizwan Akram & Muhammad Majid Hussain & Mian Hammad Nazir & Muhammad Saad Khan & Safdar Raza, 2021. "Energy Management of Microgrids for Smart Cities: A Review," Energies, MDPI, vol. 14(18), pages 1-18, September.
  16. Beisheim, Benedikt & Krämer, Stefan & Engell, Sebastian, 2020. "Hierarchical aggregation of energy performance indicators in continuous production processes," Applied Energy, Elsevier, vol. 264(C).
  17. Fang, Guochang & Tian, Lixin & Fu, Min & Sun, Mei & Du, Ruijin & Liu, Menghe, 2017. "Investigating carbon tax pilot in YRD urban agglomerations—Analysis of a novel ESER system with carbon tax constraints and its application," Applied Energy, Elsevier, vol. 194(C), pages 635-647.
  18. 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.
  19. Annalisa Santolamazza & Daniele Dadi & Vito Introna, 2021. "A Data-Mining Approach for Wind Turbine Fault Detection Based on SCADA Data Analysis Using Artificial Neural Networks," Energies, MDPI, vol. 14(7), pages 1-25, March.
  20. 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.
  21. Ascione, Fabrizio & Bianco, Nicola & De Stasio, Claudio & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2017. "Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach," Energy, Elsevier, vol. 118(C), pages 999-1017.
  22. Mantas Plienis & Tomas Deveikis & Audrius Jonaitis & Saulius Gudžius & Inga Konstantinavičiūtė & Donata Putnaitė, 2023. "Improved Methodology for Power Transformer Loss Evaluation: Algorithm Refinement and Resonance Risk Analysis," Energies, MDPI, vol. 16(23), pages 1-16, November.
  23. Mahmoud Abdelkader Bashery Abbass & Mohamed Hamdy, 2021. "A Generic Pipeline for Machine Learning Users in Energy and Buildings Domain," Energies, MDPI, vol. 14(17), pages 1-30, August.
  24. Wang, Zeyu & Liu, Jian & Zhang, Yuanxin & Yuan, Hongping & Zhang, Ruixue & Srinivasan, Ravi S., 2021. "Practical issues in implementing machine-learning models for building energy efficiency: Moving beyond obstacles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
  25. Fan, Cheng & Xiao, Fu & Zhao, Yang, 2017. "A short-term building cooling load prediction method using deep learning algorithms," Applied Energy, Elsevier, vol. 195(C), pages 222-233.
  26. Alexandru G. Berciu & Eva H. Dulf & Dan D. Micu, 2022. "Improving the Efficiency of Electricity Consumption by Applying Real-Time Fuzzy and Fractional Control," Mathematics, MDPI, vol. 10(20), pages 1-16, October.
  27. Zeng, Sheng & Su, Bin & Zhang, Minglong & Gao, Yuan & Liu, Jun & Luo, Song & Tao, Qingmei, 2021. "Analysis and forecast of China's energy consumption structure," Energy Policy, Elsevier, vol. 159(C).
  28. Sergey Zhironkin & Fares Abu-Abed, 2024. "Review of the Transition to Energy 5.0 in the Context of Non-Renewable Energy Sustainable Development," Energies, MDPI, vol. 17(18), pages 1-31, September.
  29. Zheng Wen Lie & Qing Liang Zheng & Shiyuan Zhou & Hozan Latif Rauf, 2022. "Virtual energy-saving environmental protection building design and implementation," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 263-272, March.
  30. Yu, Min & Niu, Dongxiao & Zhao, Jinqiu & Li, Mingyu & Sun, Lijie & Yu, Xiaoyu, 2023. "Building cooling load forecasting of IES considering spatiotemporal coupling based on hybrid deep learning model," Applied Energy, Elsevier, vol. 349(C).
  31. Marcos Manoel Lopes Junior & Claudia Aparecida de Mattos & Fábio Lima, 2024. "Toward Cleaner Production by Evaluating Opportunities of Saving Energy in a Short-Cycle Time Flowshop," Sustainability, MDPI, vol. 16(6), pages 1-23, March.
  32. Qiu, Changyu & Yi, Yun Kyu & Wang, Meng & Yang, Hongxing, 2020. "Coupling an artificial neuron network daylighting model and building energy simulation for vacuum photovoltaic glazing," Applied Energy, Elsevier, vol. 263(C).
  33. Benedetti, Miriam & Bonfa', Francesca & Bertini, Ilaria & Introna, Vito & Ubertini, Stefano, 2018. "Explorative study on Compressed Air Systems’ energy efficiency in production and use: First steps towards the creation of a benchmarking system for large and energy-intensive industrial firms," Applied Energy, Elsevier, vol. 227(C), pages 436-448.
  34. 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.
  35. Ahmadi-Karvigh, Simin & Ghahramani, Ali & Becerik-Gerber, Burcin & Soibelman, Lucio, 2018. "Real-time activity recognition for energy efficiency in buildings," Applied Energy, Elsevier, vol. 211(C), pages 146-160.
  36. Vito Introna & Annalisa Santolamazza & Vittorio Cesarotti, 2024. "Integrating Industry 4.0 and 5.0 Innovations for Enhanced Energy Management Systems," Energies, MDPI, vol. 17(5), pages 1-16, March.
  37. 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.
  38. Qing Yin & Chunmiao Han & Ailin Li & Xiao Liu & Ying Liu, 2024. "A Review of Research on Building Energy Consumption Prediction Models Based on Artificial Neural Networks," Sustainability, MDPI, vol. 16(17), pages 1-30, September.
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