Issue |
Natl Sci Open
Volume 3, Number 3, 2024
Special Topic: Energy Systems of Low Carbon Buildings
|
|
---|---|---|
Article Number | 20230068 | |
Number of page(s) | 22 | |
Section | Engineering | |
DOI | https://doi.org/10.1360/nso/20230068 | |
Published online | 02 February 2024 |
- Zhao Y, Zhang C, Zhang Y, et al. A review of data mining technologies in building energy systems: Load prediction, pattern identification, fault detection and diagnosis. Energy Built Environ 2020; 1: 149–164 [NASA ADS] [CrossRef] [Google Scholar]
- Chen Z, O’Neill Z, Wen J, et al. A review of data-driven fault detection and diagnostics for building HVAC systems. Appl Energy 2023; 339: 121030. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Dong B, Prakash V, Feng F, et al. A review of smart building sensing system for better indoor environment control. Energy Build 2019; 199: 29-46. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Halhoul Merabet G, Essaaidi M, Ben Haddou M, et al. Intelligent building control systems for thermal comfort and energy-efficiency: A systematic review of artificial intelligence-assisted techniques. Renew Sustain Energy Rev 2021; 144: 110969. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Chen Y, Xu P, Gu J, et al. Measures to improve energy demand flexibility in buildings for demand response (DR): A review. Energy Build 2018; 177: 125-139. [Article] [CrossRef] [Google Scholar]
- Weiss K, Khoshgoftaar TM, Wang DD. A survey of transfer learning. J Big Data 2016; 3: 1-40. [Article] [CrossRef] [Google Scholar]
- van Engelen JE, Hoos HH. A survey on semi-supervised learning. Mach Learn 2020; 109: 373-440. [Article] [CrossRef] [MathSciNet] [Google Scholar]
- Gm H, Gourisaria MK, Pandey M, et al. A comprehensive survey and analysis of generative models in machine learning. Comput Sci Rev 2020; 38: 100285. [Article] [CrossRef] [MathSciNet] [Google Scholar]
- Kaelbling LP, Littman ML, Moore AW. Reinforcement learning: A survey. J Artif Intell Res 1996; 4: 237-285. [Article] [CrossRef] [Google Scholar]
- Ye R, Dai Q. Implementing transfer learning across different datasets for time series forecasting. Pattern Recogn 2021; 109: 107617. [Article] [CrossRef] [Google Scholar]
- Cheplygina V, de Bruijne M, Pluim JPW. Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis. Med Image Anal 2019; 54: 280-296. [Article] [CrossRef] [PubMed] [Google Scholar]
- Nagy Z, Henze G, Dey S, et al. Ten questions concerning reinforcement learning for building energy management. Build Environ 2023; 241: 110435. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Weinberg D, Wang Q, Timoudas TO, et al. A review of reinforcement learning for controlling building energy systems from a computer science perspective. Sustain Cities Soc 2023; 89: 104351. [Article] [CrossRef] [Google Scholar]
- Chen Z, Xiao F, Guo F, et al. Interpretable machine learning for building energy management: A state-of-the-art review Advances Appl Energy 2023; 9:100123 [CrossRef] [Google Scholar]
- Zhang C, Tian X, Zhao Y, et al. Automated machine learning-based building energy load prediction method. J Build Eng 2023; 80: 108071. [Article] [CrossRef] [Google Scholar]
- Mao Y, Yu J, Zhang N, et al. A hybrid model of commercial building cooling load prediction based on the improved NCHHO-FENN algorithm. J Build Eng 2023; 78: 107660. [Article] [CrossRef] [Google Scholar]
- Lu C, Li S, Reddy Penaka S, et al. Automated machine learning-based framework of heating and cooling load prediction for quick residential building design. Energy 2023; 274: 127334. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Wang X, Yao Z, Papaefthymiou M. A real-time electrical load forecasting and unsupervised anomaly detection framework. Appl Energy 2023; 330: 120279. [Article] [CrossRef] [Google Scholar]
- Fan C, Xiao F, Zhao Y, et al. Analytical investigation of autoencoder-based methods for unsupervised anomaly detection in building energy data. Appl Energy 2018; 211: 1123-1135. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Blum D, Wang Z, Weyandt C, et al. Field demonstration and implementation analysis of model predictive control in an office HVAC system. Appl Energy 2022; 318: 119104. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Sepehri A, Pavlak GS. Evaluating optimal control of active insulation and HVAC systems in residential buildings. Energy Build 2023; 281: 112728. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Zhao T, Zhang B, Li M, et al. Handling fault detection and diagnosis in incomplete sensor measurements for BAS based HVAC system. J Build Eng 2023; 80: 108098. [Article] [CrossRef] [Google Scholar]
- Chen Z, Xiao F, Guo F. Similarity learning-based fault detection and diagnosis in building HVAC systems with limited labeled data. Renew Sustain Energy Rev 2023; 185: 113612. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Heimar Andersen K, Pommerencke Melgaard S, Johra H, et al. Barriers and drivers for implementation of automatic fault detection and diagnosis in buildings and HVAC systems: An outlook from industry experts. Energy Build 2024; 303: 113801. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Wen S, Zhang W, Sun Y, et al. An enhanced principal component analysis method with Savitzky-Golay filter and clustering algorithm for sensor fault detection and diagnosis. Appl Energy 2023; 337: 120862. [Article] [CrossRef] [Google Scholar]
- Gunay HB, Shi Z. Cluster analysis-based anomaly detection in building automation systems. Energy Build 2020; 228: 110445. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Yang XB, Jin XQ, Du ZM, et al. A hybrid model-based fault detection strategy for air handling unit sensors. Energy Build 2013; 57: 132-143. [Article] [CrossRef] [Google Scholar]
- Liang A, Hu Y, Li G. The impact of improved PCA method based on anomaly detection on chiller sensor fault detection. Int J Refriger 2023; 155: 184-194. [Article] [CrossRef] [Google Scholar]
- Cheng F, Cai W, Zhang X, et al. Fault detection and diagnosis for Air Handling Unit based on multiscale convolutional neural networks. Energy Build 2021; 236: 110795. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Li G, Chen L, Fan C, et al. Interpretation and explanation of convolutional neural network-based fault diagnosis model at the feature-level for building energy systems. Energy Build 2023; 295: 113326. [Article] [CrossRef] [Google Scholar]
- Wang X, Kang X, An J, et al. Reinforcement learning approach for optimal control of ice-based thermal energy storage (TES) systems in commercial buildings. Energy Build 2023; 301: 113696. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Georgiou GS, Christodoulides P, Kalogirou SA. Optimizing the energy storage schedule of a battery in a PV grid-connected nZEB using linear programming. Energy 2020; 208: 118177. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Čongradac V, Kulić F. Recognition of the importance of using artificial neural networks and genetic algorithms to optimize chiller operation. Energy Build 2012; 47: 651-658. [Article] [CrossRef] [Google Scholar]
- Delgarm N, Sajadi B, Kowsary F, et al. Multi-objective optimization of the building energy performance: A simulation-based approach by means of particle swarm optimization (PSO). Appl Energy 2016; 170: 293-303. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Chan KC, Wong VTT, Yow AKF, et al. Development and performance evaluation of a chiller plant predictive operational control strategy by artificial intelligence. Energy Build 2022; 262: 112017. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Qiu S, Li Z, Fan D, et al. Chilled water temperature resetting using model-free reinforcement learning: Engineering application. Energy Build 2022; 255: 111694. [Article] [CrossRef] [Google Scholar]
- Genkin M, McArthur J. A transfer learning approach to minimize reinforcement learning risks in energy optimization for automated and smart buildings. Energy Build 2024; 303: 113760 [NASA ADS] [CrossRef] [Google Scholar]
- Zhang Q, Tian Z, Niu J, et al. A study on transfer learning in enhancing performance of building energy system fault diagnosis with extremely limited labeled data. Build Environ 2022; 225: 109641. [Article] [CrossRef] [Google Scholar]
- Jin X, Xiao F, Zhang C, et al. GEIN: An interpretable benchmarking framework towards all building types based on machine learning. Appl Energy 2022; 260: 111909 [Google Scholar]
- Zhong F, Calautit JK, Wu Y. Fault data seasonal imbalance and insufficiency impacts on data-driven heating, ventilation and air-conditioning fault detection and diagnosis performances for energy-efficient building operations. Energy 2023; 282: 128180. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Pan SJ, Yang Q. A survey on transfer learning. IEEE Trans Knowl Data Eng 2009; 22: 1345-1359. [Article] [Google Scholar]
- Wang T, Huan J, Zhu M. Instance-based deep transfer learning. In: Proceedings of the 2019 IEEE Winter Conference on Applications of Computer Vision. Waikoloa, 2019. 367–375 [CrossRef] [Google Scholar]
- Niu S, Hu Y, Wang J, et al. Feature-based distant domain transfer learning. In: Proceedings of the Programs and Abstracts of the 2020 IEEE International Conference on Big Data. Atlanta, 2020. 5164–5171 [Google Scholar]
- Blome C, Schoenherr T, Eckstein D. The impact of knowledge transfer and complexity on supply chain flexibility: A knowledge-based view. Int J Prod Econ 2014; 147: 307-316. [Article] [CrossRef] [Google Scholar]
- Pinto G, Messina R, Li H, et al. Sharing is caring: An extensive analysis of parameter-based transfer learning for the prediction of building thermal dynamics. Energy Build 2022; 276: 112530. [Article] [CrossRef] [Google Scholar]
- Fan C, He W, Liu Y, et al. A novel image-based transfer learning framework for cross-domain HVAC fault diagnosis: From multi-source data integration to knowledge sharing strategies. Energy Build 2022; 262: 111995. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Fan C, Sun Y, Xiao F, et al. Statistical investigations of transfer learning-based methodology for short-term building energy predictions. Appl Energy 2020; 262: 114499. [Article] [CrossRef] [Google Scholar]
- Fan C, Lei Y, Sun Y, et al. Data-centric or algorithm-centric: Exploiting the performance of transfer learning for improving building energy predictions in data-scarce context. Energy 2022; 240: 122775. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Lu Y, Tian Z, Zhou R, et al. A general transfer learning-based framework for thermal load prediction in regional energy system. Energy 2021; 217: 119322. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Fang X, Gong G, Li G, et al. A hybrid deep transfer learning strategy for short term cross-building energy prediction. Energy 2021; 215: 119208. [Article] [CrossRef] [Google Scholar]
- Liu J, Zhang Q, Li X, et al. Transfer learning-based strategies for fault diagnosis in building energy systems. Energy Build 2021; 250: 111256. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Zhu X, Chen K, Anduv B, et al. Transfer learning based methodology for migration and application of fault detection and diagnosis between building chillers for improving energy efficiency. Build Environ 2021; 200: 107957. [Article] [CrossRef] [Google Scholar]
- Liang X, Li P, Chen S, et al. Partial domain adaption based prediction calibration methodology for fault detection and diagnosis of chillers under variable operational condition scenarios. Build Environ 2022; 217: 109099. [Article] [Google Scholar]
- Shaikh PH, Nor NBM, Nallagownden P, et al. A review on optimized control systems for building energy and comfort management of smart sustainable buildings. Renew Sustain Energy Rev 2014; 34: 409-429. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Chen Y, Tong Z, Zheng Y, et al. Transfer learning with deep neural networks for model predictive control of HVAC and natural ventilation in smart buildings. J Clean Prod 2020; 254: 119866. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Lissa P, Schukat M, Barrett E. Transfer learning applied to reinforcement learning-based HVAC control. SN Comput Sci 2020; 1: 127. [Article] [CrossRef] [Google Scholar]
- Zhang T, Afshari M, Musilek P, et al. Diversity for transfer in learning-based control of buildings. In: Proceedings of the Thirteenth ACM International Conference on Future Energy Systems. Association for Computing Machinery. New York, 2022. 556–564 [CrossRef] [Google Scholar]
- Coraci D, Brandi S, Hong T, et al. Online transfer learning strategy for enhancing the scalability and deployment of deep reinforcement learning control in smart buildings. Appl Energy 2023; 333: 120598. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Chong Y, Ding Y, Yan Q, et al. Graph-based semi-supervised learning: A review. Neurocomputing 2020; 408: 216-230. [Article] [CrossRef] [Google Scholar]
- Triguero I, García S, Herrera F. Self-labeled techniques for semi-supervised learning: Taxonomy, software and empirical study. Knowl Inf Syst 2015; 42: 245-284. [Article] [CrossRef] [Google Scholar]
- Karger DR, Stein C. A new approach to the minimum cut problem. J ACM 1996; 43: 601-640. [Article] [CrossRef] [MathSciNet] [Google Scholar]
- Zang F, Zhang JS. Label propagation through sparse neighborhood and its applications. Neurocomputing 2012; 97: 267-277. [Article] [CrossRef] [Google Scholar]
- Fan C, Liu Y, Liu X, et al. A study on semi-supervised learning in enhancing performance of AHU unseen fault detection with limited labeled data. Sustain Cities Soc 2021; 70: 102874. [Article] [CrossRef] [Google Scholar]
- Jiang F, Ma J, Li Z, et al. Prediction of energy use intensity of urban buildings using the semi-supervised deep learning model. Energy 2022; 249: 123631. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Jin X, Xiao F, Zhang C, et al. Semi-supervised learning based framework for urban level building electricity consumption prediction. Appl Energy 2022; 328: 120210. [Article] [CrossRef] [Google Scholar]
- Tian X, Jiao W, Liu T, et al. Intelligent detection method of low-pressure gas system leakage based on semi-supervised anomaly diagnosis. Expert Syst Appl 2022; 209: 118376. [Article] [CrossRef] [Google Scholar]
- Yan K, Zhong C, Ji Z, et al. Semi-supervised learning for early detection and diagnosis of various air handling unit faults. Energy Build 2018; 181: 75-83. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Fan C, Liu X, Xue P, et al. Statistical characterization of semi-supervised neural networks for fault detection and diagnosis of air handling units. Energy Build 2021; 234: 110733. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Li B, Cheng F, Cai H, et al. A semi-supervised approach to fault detection and diagnosis for building HVAC systems based on the modified generative adversarial network. Energy Build 2021; 246: 111044. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Fan C, Lin Y, Piscitelli MS, et al. Leveraging graph convolutional networks for semi-supervised fault diagnosis of HVAC systems in data-scarce contexts. Build Simul 2023; 16: 1499–1517 [CrossRef] [Google Scholar]
- Bond-Taylor S, Leach A, Long Y, et al. Deep generative modelling: A comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models. In: Proceedings of the Programs and Abstracts of the IEEE Transactions on Pattern Analysis and Machine Intelligence. Institute of Electrical and Electronics Engineers, Piscataway, 2021. 7327–7347 [Google Scholar]
- Creswell A, White T, Dumoulin V, et al. Generative adversarial networks: An overview. IEEE Signal Proc Mag 2018; 35: 53-65. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Kingma DP, Welling M. An introduction to variational autoencoders. FNT Machine Learn 2019; 12: 307-392. [Article] [Google Scholar]
- Abufadda M, Mansour K. A survey of synthetic data generation for machine learning. In: Proceedings of the Programs and Abstracts of the 22nd International Arab Conference on Information Technology. Muscat, 2021 [Google Scholar]
- Debnath A, Waghmare G, Wadhwa H, et al. Exploring generative data augmentation in multivariate time series forecasting: Opportunities and challenges. In: Proceedings of the 7th KDD Workshop on Mining and Learning from Time Series. Singapore, 2021 [Google Scholar]
- Zhai J, Zhang S, Chen J, et al. Autoencoder and its various variants. In: Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics. Miyazaki, 2018. 415–419 [Google Scholar]
- Gui J, Sun Z, Wen Y, et al. A review on generative adversarial networks: Algorithms, theory, and applications. IEEE Trans Pattern Anal Mach Intell 2021; 35: 3313–3332 [Google Scholar]
- Tian C, Li C, Zhang G, et al. Data driven parallel prediction of building energy consumption using generative adversarial nets. Energy Build 2019; 186: 230-243. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Zhang Y, Zhou Z, Liu J, et al. Data augmentation for improving heating load prediction of heating substation based on TimeGAN. Energy 2022; 260: 124919. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Fan C, Chen M, Tang R, et al. A novel deep generative modeling-based data augmentation strategy for improving short-term building energy predictions. Build Simul 2022; 15: 197-211. [Article] [CrossRef] [Google Scholar]
- Heidrich B, Mannsperger L, Turowski M, et al. Boost short-term load forecasts with synthetic data from transferred latent space information. Energy Inform 2022; 5: 1-20. [Article] [CrossRef] [Google Scholar]
- Zhong C, Yan K, Dai Y, et al. Energy efficiency solutions for buildings: Automated fault diagnosis of air handling units using generative adversarial networks. Energies 2019; 12: 527. [Article] [CrossRef] [Google Scholar]
- Yan K, Huang J, Shen W, et al. Unsupervised learning for fault detection and diagnosis of air handling units. Energy Build 2020; 210: 109689. [Article] [CrossRef] [Google Scholar]
- Yan K, Chong A, Mo Y. Generative adversarial network for fault detection diagnosis of chillers. Build Environ 2020; 172: 106698. [Article] [Google Scholar]
- Fan C, Li X, Zhao Y, et al. Quantitative assessments on advanced data synthesis strategies for enhancing imbalanced AHU fault diagnosis performance. Energy Build 2021; 252: 111423. [Article] [CrossRef] [Google Scholar]
- Zhang F, Saeed N, Sadeghian P. Deep learning in fault detection and diagnosis of building HVAC systems: A systematic review with meta analysis. Energy AI 2023; 12: 100235 [CrossRef] [Google Scholar]
- Li Y, Zhang M, Chen C. A Deep-Learning intelligent system incorporating data augmentation for Short-Term voltage stability assessment of power systems. Appl Energy 2022; 308: 118347. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Wang Y, Liu Y, Yang Q. Operational scenario generation and forecasting for integrated energy systems. IEEE Trans Indust Inf 2023; 20: 2920–2931 [Google Scholar]
- Lin J, Zhang Y, Xu S. Improved generative adversarial behavioral learning method for demand response and its application in hourly electricity price optimization. J Modern Power Syst Clean Energy 2022; 10: 1358-1373. [Article] [Google Scholar]
- Feng Z, Xu C, Tao D. Self-supervised representation learning from multi-domain data. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. Seoul, 2019. 3245–3255 [Google Scholar]
- Fan C, Lei Y, Sun Y, et al. Novel transformer-based self-supervised learning methods for improved HVAC fault diagnosis performance with limited labeled data. Energy 2023; 278: 127972. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Ramirez-Loaiza ME, Sharma M, Kumar G, et al. Active learning: an empirical study of common baselines. Data Min Knowl Disc 2017; 31: 287-313. [Article] [CrossRef] [Google Scholar]
- Zhang L, Wen J. Active learning strategy for high fidelity short-term data-driven building energy forecasting. Energy Build 2021; 244: 111026. [Article] [CrossRef] [Google Scholar]
- Fan C, Wu Q, Zhao Y, et al. Integrating active learning and semi-supervised learning for improved data-driven HVAC fault diagnosis performance. Appl Energy 2024; 356: 122356. [Article] [CrossRef] [Google Scholar]
- Jacoby M, Tan SY, Henze G, et al. A high-fidelity residential building occupancy detection dataset. Sci Data 2021; 8: 280. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
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