Optimal Control Method of Variable Air Volume Terminal Unit System
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Gianluca Serale & Massimo Fiorentini & Alfonso Capozzoli & Daniele Bernardini & Alberto Bemporad, 2018. "Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities," Energies, MDPI, vol. 11(3), pages 1-35, March.
- Yoon, Sungmin & Yu, Yuebin, 2018. "Hidden factors and handling strategies on virtual in-situ sensor calibration in building energy systems: Prior information and cancellation effect," Applied Energy, Elsevier, vol. 212(C), pages 1069-1082.
- Zhang, Rongpeng & Hong, Tianzhen, 2017. "Modeling of HVAC operational faults in building performance simulation," Applied Energy, Elsevier, vol. 202(C), pages 178-188.
- Chul-Ho Kim & Seung-Eon Lee & Kwang-Ho Lee & Kang-Soo Kim, 2019. "Detailed Comparison of the Operational Characteristics of Energy-Conserving HVAC Systems during the Cooling Season," Energies, MDPI, vol. 12(21), pages 1-29, October.
- Sholahudin, S. & Han, Hwataik, 2016. "Simplified dynamic neural network model to predict heating load of a building using Taguchi method," Energy, Elsevier, vol. 115(P3), pages 1672-1678.
- Hyo-Jun Kim & Young-Hum Cho, 2017. "A Study on a Control Method with a Ventilation Requirement of a VAV System in Multi-Zone," Sustainability, MDPI, vol. 9(11), pages 1-17, November.
- Young Min Kim & Ki Uhn Ahn & Cheol Soo Park, 2016. "Issues of Application of Machine Learning Models for Virtual and Real-Life Buildings," Sustainability, MDPI, vol. 8(6), pages 1-15, June.
- Li, Qiong & Meng, Qinglin & Cai, Jiejin & Yoshino, Hiroshi & Mochida, Akashi, 2009. "Applying support vector machine to predict hourly cooling load in the building," Applied Energy, Elsevier, vol. 86(10), pages 2249-2256, October.
- Behzad Rismanchi & Juan Mahecha Zambrano & Bryan Saxby & Ross Tuck & Mark Stenning, 2019. "Control Strategies in Multi-Zone Air Conditioning Systems," Energies, MDPI, vol. 12(3), pages 1-14, January.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Panagiotis Michailidis & Iakovos Michailidis & Dimitrios Vamvakas & Elias Kosmatopoulos, 2023. "Model-Free HVAC Control in Buildings: A Review," Energies, MDPI, vol. 16(20), pages 1-45, October.
- Muideen Adegoke & Alaka Hafiz & Saheed Ajayi & Razak Olu-Ajayi, 2022. "Application of Multilayer Extreme Learning Machine for Efficient Building Energy Prediction," Energies, MDPI, vol. 15(24), pages 1-21, December.
- Ling, Jihong & Zhang, Bingyang & Dai, Na & Xing, Jincheng, 2023. "Coupling input feature construction methods and machine learning algorithms for hourly secondary supply temperature prediction," Energy, Elsevier, vol. 278(C).
- Hong, Yejin & Yoon, Sungmin & Kim, Yong-Shik & Jang, Hyangin, 2021. "System-level virtual sensing method in building energy systems using autoencoder: Under the limited sensors and operational datasets," Applied Energy, Elsevier, vol. 301(C).
- Koo, Jabeom & Yoon, Sungmin, 2022. "In-situ sensor virtualization and calibration in building systems," Applied Energy, Elsevier, vol. 325(C).
- Lara Ramadan & Isam Shahrour & Hussein Mroueh & Fadi Hage Chehade, 2021. "Use of Machine Learning Methods for Indoor Temperature Forecasting," Future Internet, MDPI, vol. 13(10), pages 1-18, September.
- Gao, Zhikun & Yu, Junqi & Zhao, Anjun & Hu, Qun & Yang, Siyuan, 2022. "A hybrid method of cooling load forecasting for large commercial building based on extreme learning machine," Energy, Elsevier, vol. 238(PC).
- Hossein Moayedi & Amir Mosavi, 2021. "Synthesizing Multi-Layer Perceptron Network with Ant Lion Biogeography-Based Dragonfly Algorithm Evolutionary Strategy Invasive Weed and League Champion Optimization Hybrid Algorithms in Predicting He," Sustainability, MDPI, vol. 13(6), pages 1-20, March.
- Lu, Xing & O'Neill, Zheng & Li, Yanfei & Niu, Fuxin, 2020. "A novel simulation-based framework for sensor error impact analysis in smart building systems: A case study for a demand-controlled ventilation system," Applied Energy, Elsevier, vol. 263(C).
- 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).
- Carpenter, Joseph & Woodbury, Keith A. & O'Neill, Zheng, 2018. "Using change-point and Gaussian process models to create baseline energy models in industrial facilities: A comparison," Applied Energy, Elsevier, vol. 213(C), pages 415-425.
- Gao, Zhikun & Yang, Siyuan & Yu, Junqi & Zhao, Anjun, 2024. "Hybrid forecasting model of building cooling load based on combined neural network," Energy, Elsevier, vol. 297(C).
- Jinrong Wu & Su Nguyen & Damminda Alahakoon & Daswin De Silva & Nishan Mills & Prabod Rathnayaka & Harsha Moraliyage & Andrew Jennings, 2024. "A Comparative Analysis of Machine Learning-Based Energy Baseline Models across Multiple Building Types," Energies, MDPI, vol. 17(6), pages 1-18, March.
- Xiaoyu Gao & Chengying Qi & Guixiang Xue & Jiancai Song & Yahui Zhang & Shi-ang Yu, 2020. "Forecasting the Heat Load of Residential Buildings with Heat Metering Based on CEEMDAN-SVR," Energies, MDPI, vol. 13(22), pages 1-19, November.
- Shen, Yuxuan & Pan, Yue, 2023. "BIM-supported automatic energy performance analysis for green building design using explainable machine learning and multi-objective optimization," Applied Energy, Elsevier, vol. 333(C).
- Guo, Yurun & Wang, Shugang & Wang, Jihong & Zhang, Tengfei & Ma, Zhenjun & Jiang, Shuang, 2024. "Key district heating technologies for building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
- Zhong, Fangliang & Calautit, John Kaiser & Wu, Yupeng, 2022. "Assessment of HVAC system operational fault impacts and multiple faults interactions under climate change," Energy, Elsevier, vol. 258(C).
- Khalid Almutairi & Salem Algarni & Talal Alqahtani & Hossein Moayedi & Amir Mosavi, 2022. "A TLBO-Tuned Neural Processor for Predicting Heating Load in Residential Buildings," Sustainability, MDPI, vol. 14(10), pages 1-19, May.
- Yang, Shiyu & Wan, Man Pun & Ng, Bing Feng & Dubey, Swapnil & Henze, Gregor P. & Chen, Wanyu & Baskaran, Krishnamoorthy, 2020. "Experimental study of model predictive control for an air-conditioning system with dedicated outdoor air system," Applied Energy, Elsevier, vol. 257(C).
- Evelina Di Corso & Tania Cerquitelli & Daniele Apiletti, 2018. "METATECH: METeorological Data Analysis for Thermal Energy CHaracterization by Means of Self-Learning Transparent Models," Energies, MDPI, vol. 11(6), pages 1-24, May.
More about this item
Keywords
variable air volume system; terminal unit; optimal control; prediction model; sensor calibration;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:22:p:7527-:d:676814. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.