Probabilistic graphical models in energy systems: A review
),
Download citation
604
Views
42
Downloads
3
Crossref
3
WoS
3
Scopus
0
CSCD
Probabilistic graphical models (PGMs) can effectively deal with the problems of energy consumption and occupancy prediction, fault detection and diagnosis, reliability analysis, and optimization in energy systems. Compared with the black-box models, PGMs show advantages in model interpretability, scalability and reliability. They have great potential to realize the true artificial intelligence in energy systems of the next generation. This paper intends to provide a comprehensive review of the PGM-based approaches published in the last decades. It reveals the advantages, limitations and potential future research directions of the PGM-based approaches for energy systems. Two types of PGMs are summarized in this review, including static models (SPGMs) and dynamic models (DPGMs). SPGMs can conduct probabilistic inference based on incomplete, uncertain or even conflicting information. SPGM-based approaches are proposed to deal with various management tasks in energy systems. They show outstanding performance in fault detection and diagnosis of energy systems. DPGMs can represent a dynamic and stochastic process by describing how its state changes with time. DPGM-based approaches have high accuracy in predicting the energy consumption, occupancy and failures of energy systems. In the future, a unified framework is suggested to fuse the knowledge-driven and data-driven PGMs for achieving better performances. Universal PGM-based approaches are needed that can be adapted to various energy systems. Hybrid algorithms would outperform the basic PGMs by integrating advanced techniques such as deep learning and first-order logic.
menu
Probabilistic graphical models in energy systems: A review
),
Abstract
Probabilistic graphical models (PGMs) can effectively deal with the problems of energy consumption and occupancy prediction, fault detection and diagnosis, reliability analysis, and optimization in energy systems. Compared with the black-box models, PGMs show advantages in model interpretability, scalability and reliability. They have great potential to realize the true artificial intelligence in energy systems of the next generation. This paper intends to provide a comprehensive review of the PGM-based approaches published in the last decades. It reveals the advantages, limitations and potential future research directions of the PGM-based approaches for energy systems. Two types of PGMs are summarized in this review, including static models (SPGMs) and dynamic models (DPGMs). SPGMs can conduct probabilistic inference based on incomplete, uncertain or even conflicting information. SPGM-based approaches are proposed to deal with various management tasks in energy systems. They show outstanding performance in fault detection and diagnosis of energy systems. DPGMs can represent a dynamic and stochastic process by describing how its state changes with time. DPGM-based approaches have high accuracy in predicting the energy consumption, occupancy and failures of energy systems. In the future, a unified framework is suggested to fuse the knowledge-driven and data-driven PGMs for achieving better performances. Universal PGM-based approaches are needed that can be adapted to various energy systems. Hybrid algorithms would outperform the basic PGMs by integrating advanced techniques such as deep learning and first-order logic.
References(214)
Adamopoulou AA, Tryferidis AM, Tzovaras DK (2016). A context- aware method for building occupancy prediction. Energy and Buildings, 110: 229–244.
Aguila-Leon J, Chiñas-Palacios C, Garcia EXM, et al. (2020). A multimicrogrid energy management model implementing an evolutionary game-theoretic approach. International Transactions on Electrical Energy Systems, 30(11): e12617.
Andersen PD, Iversen A, Madsen H, et al. (2014). Dynamic modeling of presence of occupants using inhomogeneous Markov chains. Energy and Buildings, 69: 213–223.
Arroyo-Figueroa G, Sucar LE, Villavicencio A (1998). Probabilistic temporal reasoning and its application to fossil power plant operation. Expert Systems with Applications, 15: 317–324.
Arroyo-Figueroa G, Alvarez Y, Sucar L (2000). SEDRET—An intelligent system for the diagnosis and prediction of events in power plants. Expert Systems with Applications, 18: 75–86.
Baik H-S, Jeong HS, Abraham DM (2006). Estimating transition probabilities in Markov chain-based deterioration models for management of wastewater systems. Journal of Water Resources Planning and Management, 132: 15–24.
Balekelayi N, Tesfamariam S (2019). Graph-theoretic surrogate measure to analyze reliability of water distribution system using Bayesian belief network–based data fusion technique. Journal of Water Resources Planning and Management, 145: 04019028.
Bassamzadeh N, Ghanem R (2017). Multiscale stochastic prediction of electricity demand in smart grids using Bayesian networks. Applied Energy, 193: 369–380.
Bevrani H, Daneshfar F, Hiyama T (2012). A new intelligent agent- based AGC design with real-time application. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 42: 994–1002.
Bigaud D, Charki A, Caucheteux A, et al. (2019). Detection of faults and drifts in the energy performance of a building using Bayesian networks. Journal of Dynamic Systems, Measurement, and Control, 141(10): 101011.
Borunda M, Jaramillo OA, Reyes A, et al. (2016). Bayesian networks in renewable energy systems: A bibliographical survey. Renewable and Sustainable Energy Reviews, 62: 32–45.
Cai B, Liu Y, Fan Q, et al. (2014). Multi-source information fusion based fault diagnosis of ground-source heat pump using Bayesian network. Applied Energy, 114: 1–9.
Cai B, Huang L, Xie M (2017). Bayesian networks in fault diagnosis. IEEE Transactions on Industrial Informatics, 13: 2227–2240.
Candanedo LM, Feldheim V, Deramaix D (2017). A methodology based on Hidden Markov Models for occupancy detection and a case study in a low energy residential building. Energy and Buildings, 148: 327–341.
Carta JA, Velázquez S, Matías JM (2011). Use of Bayesian networks classifiers for long-term mean wind turbine energy output estimation at a potential wind energy conversion site. Energy Conversion and Management, 52: 1137–1149.
Chang Y, Zhang C, Shi J, et al. (2019). Dynamic Bayesian network based approach for risk analysis of hydrogen generation unit leakage. International Journal of Hydrogen Energy, 44: 26665– 26678.
Chen Z, Xu J, Soh YC (2015). Modeling regular occupancy in commercial buildings using stochastic models. Energy and Buildings, 103: 216–223.
Cheng Q, Wang S, Yan C (2016). Robust optimal design of chilled water systems in buildings with quantified uncertainty and reliability for minimized life-cycle cost. Energy and Buildings, 126: 159–169.
Chen Z, Zhu Q, Masood MK, et al. (2017). Environmental sensors- based occupancy estimation in buildings via IHMM-MLR. IEEE Transactions on Industrial Informatics, 13: 2184–2193.
Codetta-Raiteri D, Bobbio A, Montani S, et al. (2012). A dynamic Bayesian network based framework to evaluate cascading effects in a power grid. Engineering Applications of Artificial Intelligence, 25: 683–697.
D'Amico G, Petroni F, Prattico F (2013). Reliability measures of second-order semi-Markov chain applied to wind energy production. Journal of Renewable Energy, 2013: 1–6.
D'Amico G, Petroni F, Prattico F (2015). Reliability measures for indexed semi-Markov chains applied to wind energy production. Reliability Engineering & System Safety, 144: 170–177.
D'Angelo MFSV, Palhares RM, Cosme LB, et al. (2014). Fault detection in dynamic systems by a Fuzzy/Bayesian network formulation. Applied Soft Computing, 21: 647–653.
de Bessa Ⅳ, Palhares RM, D'Angelo MFSV, et al. (2016). Data-driven fault detection and isolation scheme for a wind turbine benchmark. Renewable Energy, 87: 634–645.
De Caro F, Vaccaro A, Villacci D (2019). A Markov chain-based model for wind power prediction in congested electrical grids. The Journal of Engineering, 2019: 4961–4964.
Deb S, Patra S (2016). A new methodology for severity analysis of power system network by using Bayesian network. MAYFEB Journal of Electrical and Computer Engineering, 1: 1–6.
Dey D, Dong B (2016). A probabilistic approach to diagnose faults of air handling units in buildings. Energy and Buildings, 130: 177–187.
Dhople SV, Dominguez-Garcia AD (2012). Estimation of photovoltaic system reliability and performance metrics. IEEE Transactions on Power Systems, 27: 554–563.
Ding W, Meng F (2020). Point and interval forecasting for wind speed based on linear component extraction. Applied Soft Computing, 93: 106350.
Dinwoodie I, McMillan D, Revie M, et al. (2013). Development of a combined operational and strategic decision support model for offshore wind. Energy Procedia, 35: 157–166.
Dong B, Lam KP (2014). A real-time model predictive control for building heating and cooling systems based on the occupancy behavior pattern detection and local weather forecasting. Building Simulation, 7: 89–106.
Duhirwe PN, Hwang JK, Ngarambe J, et al. (2021). A novel deep learning-based integrated photovoltaic, energy storage system and electric heat pump system: Optimising energy usage and costs. International Journal of Energy Research, 45: 9306–9325.
Eniola V, Suriwong T, Sirisamphanwong C, et al. (2019). Hour-ahead forecasting of photovoltaic power output based on hidden Markov model and genetic algorithm. International Journal of Renewable Energy Research, 9: 933–943.
Fan C, Wang J, Gang W, et al. (2019). Assessment of deep recurrent neural network-based strategies for short-term building energy predictions. Applied Energy, 236: 700–710.
Fan H, Zhang X, Mei S, et al. (2021). A Markov regime switching model for ultra-short-term wind power prediction based on toeplitz inverse covariance clustering. Frontiers in Energy Research, 9: 638797.
Fleming KN (2004). Markov models for evaluating risk-informed in-service inspection strategies for nuclear power plant piping systems. Reliability Engineering & System Safety, 83: 27–45.
Fu Z, Chen Q, Zhang L, et al. (2021). Research on energy management strategy of fuel cell power generation system based on Grey–Markov chain power prediction. Energy Reports, 7: 319–325.
Futakuchi M, Takayama S, Ishigame A (2021). Scheduled operation of wind farm with battery system using deep reinforcement learning. IEEJ Transactions on Electrical and Electronic Engineering, 16: 687–695.
Gang W, Wang S, Xiao F, et al. (2015). Robust optimal design of building cooling systems considering cooling load uncertainty and equipment reliability. Applied Energy, 159: 265–275.
Ghasvarian Jahromi K, Gharavian D, Mahdiani H (2020). A novel method for day-ahead solar power prediction based on hidden Markov model and cosine similarity. Soft Computing, 24: 4991– 5004.
Gu Y-K, Xu B, Huang H, et al. (2020). A fuzzy performance evaluation model for a gearbox system using hidden Markov model. IEEE Access, 8: 30400–30409.
Gunduz H, Jayaweera D (2018). Reliability assessment of a power system with cyber-physical interactive operation of photovoltaic systems. International Journal of Electrical Power & Energy Systems, 101: 371–384.
Guo Y, Tan Z, Chen H, et al. (2018). Deep learning-based fault diagnosis of variable refrigerant flow air-conditioning system for building energy saving. Applied Energy, 225: 732–745.
He S, Wang Z, Wang Z, et al. (2016). Fault detection and diagnosis of chiller using Bayesian network classifier with probabilistic boundary. Applied Thermal Engineering, 107: 37–47.
Hong Y-Y, Chang W-C, Chang Y-R, et al. (2017). Optimal sizing of renewable energy generations in a community microgrid using Markov model. Energy, 135: 68–74.
Hong Y-Y, Wu M-Y (2019). Markov model-based energy storage system planning in power systems. IEEE Systems Journal, 13: 4313–4323.
Hou K, Jia H, Xu X, et al. (2016). A continuous time Markov chain based sequential analytical approach for composite power system reliability assessment. IEEE Transactions on Power Systems, 31: 738–748.
Hu M, Chen H, Shen L, et al. (2018). A machine learning Bayesian network for refrigerant charge faults of variable refrigerant flow air conditioning system. Energy and Buildings, 158: 668–676.
Huang S, Malara ACL, Zuo W, et al. (2018a). A Bayesian network model for the optimization of a chiller plant's condenser water set point. Journal of Building Performance Simulation, 11: 36–47.
Huang S, Zuo W, Sohn MD (2018b). A Bayesian network model for predicting cooling load of commercial buildings. Building Simulation, 11: 87–101.
Jun H-B, Kim D (2017). A Bayesian network-based approach for fault analysis. Expert Systems with Applications, 81: 332–348.
Kim WS, Eom H, Kwon Y (2021). Optimal design of photovoltaic connected energy storage system using Markov chain models. Sustainability, 13: 3837.
Kouadri A, Hajji M, Harkat M-F, et al. (2020). Hidden Markov model based principal component analysis for intelligent fault diagnosis of wind energy converter systems. Renewable Energy, 150: 598–606.
Kulikov GG, Arkov V, Abdulnagimov AI (2010). Markov modelling for energy efficient control of gas turbine power plant. IFAC Proceedings Volumes, 43: 63–67.
Kumar ES, Behera DK, Sarkar A (2012). Markov chain modeling of performance degradation of photovoltaic system. IJCSMS International Journal of Computer Science and Management Studies, 12: 39–44.
Lakehal A, Ghemari Z (2015). Availability assessment of electric power based on switch reliability modelling with dynamic Bayesian networks: Case study of electrical distribution networks. Journal of Mathematics and System Science, 5: 289–295.
Li B, Roche R (2020). Optimal scheduling of multiple multi-energy supply microgrids considering future prediction impacts based on model predictive control. Energy, 197: 117180.
Li T, Zhao Y, Zhang C, et al. (2021a). A knowledge-guided and data-driven method for building HVAC systems fault diagnosis. Building and Environment, 198: 107850.
Li W, Jia X, Li X, et al. (2021b). A Markov model for short term wind speed prediction by integrating the wind acceleration information. Renewable Energy, 164: 242–253.
Lisnianski A, Elmakias D, Laredo D, et al. (2012). A multi-state Markov model for a short-term reliability analysis of a power generating unit. Reliability Engineering & System Safety, 98: 1–6.
Liu Z, Liu Y, Cai B (2014). Reliability analysis of the electrical control system of subsea blowout preventers using Markov models. PLoS ONE, 9: e113525.
Liu Z, Liu Y, Shan H, et al. (2015a). A fault diagnosis methodology for gear pump based on EEMD and Bayesian network. PLoS ONE, 10: e0125703.
Liu Z, Liu Y, Zhang D, et al. (2015b). Fault diagnosis for a solar assisted heat pump system under incomplete data and expert knowledge. Energy, 87: 41–48.
Loeliger H (2004). An Introduction to factor graphs. IEEE Signal Processing Magazine, 21: 28–41.
Lou C, Li X, Atoui MA (2020). Bayesian network based on an adaptive threshold scheme for fault detection and classification. Industrial & Engineering Chemistry Research, 59: 15155–15164.
Mahani K, Liang Z, Parlikad AK, et al. (2019). Joint optimization of operation and maintenance policies for solar-powered microgrids. IEEE Transactions on Sustainable Energy, 10: 833–842.
Martins MR, Schleder AM, Droguett EL (2014). A methodology for risk analysis based on hybrid Bayesian networks: Application to the regasification system of liquefied natural gas onboard a floating storage and regasification unit. Risk Analysis, 34: 2098– 2120.
Mathieu JL, Koch S, Callaway DS (2013). State estimation and control of electric loads to manage real-time energy imbalance. IEEE Transactions on Power Systems, 28: 430–440.
Micevski T, Kuczera G, Coombes P (2002). Markov model for storm water pipe deterioration. Journal of Infrastructure Systems, 8: 49–56.
Muralidharan V, Sugumaran V (2012). A comparative study of Naïve Bayes classifier and Bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysis. Applied Soft Computing, 12: 2023–2029.
Muratori M, Roberts MC, Sioshansi R, et al. (2013). A highly resolved modeling technique to simulate residential power demand. Applied Energy, 107: 465–473.
Nademi H, Vanfretti L, Pretlove J (2020). Fault detection method in subsea power distribution systems using statistical optimisation. IET Energy Systems Integration, 2: 144–150.
Nagaraja HN (2006). Inference in hidden Markov models. Technometrics, 48: 574–575.
Najafi M, Auslander DM, Bartlett PL, et al. (2012). Application of machine learning in the fault diagnostics of air handling units. Applied Energy, 96: 347–358.
O'Neill Z, O'Neill C (2016). Development of a probabilistic graphical model for predicting building energy performance. Applied Energy, 164: 650–658.
Ouyang T, Zha X, Qin L, et al. (2019). Prediction of wind power ramp events based on residual correction. Renewable Energy, 136: 781–792.
Ozer I, Efe SB, Ozbay H (2021). A combined deep learning application for short term load forecasting. Alexandria Engineering Journal, 60: 3807–3818.
Page J, Robinson D, Morel N, et al. (2008). A generalised stochastic model for the simulation of occupant presence. Energy and Buildings, 40: 83–98.
Radaideh A, Vaidya U, Ajjarapu V (2019). Sequential set-point control for heterogeneous thermostatically controlled loads through an extended Markov chain abstraction. IEEE Transactions on Smart Grid, 10: 116–127.
Ramírez PAP, Utne IB (2015). Use of dynamic Bayesian networks for life extension assessment of ageing systems. Reliability Engineering & System Safety, 133: 119–136.
Reffas O, Sahraoui Y, Nahal M, et al. (2020). Reactive energy compensator effect on the reliability of a complex electrical system using Bayesian networks. Eksploatacja i Niezawodnosc - Maintenance and Reliability, 22: 684–693.
Roje T, Marín LG, Sáez D, et al. (2017). Consumption modeling based on Markov chains and Bayesian networks for a demand side management design of isolated microgrids. International Journal of Energy Research, 41: 365–376.
Salimi S, Liu Z, Hammad A (2019). Occupancy prediction model for open-plan offices using real-time location system and inhomogeneous Markov chain. Building and Environment, 152: 1–16.
Sanjari MJ, Gooi HB, Nair N-KC (2020). Power generation forecast of hybrid PV–wind system. IEEE Transactions on Sustainable Energy, 11: 703–712.
Shahnazari H, Mhaskar P, House JM, et al. (2019). Modeling and fault diagnosis design for HVAC systems using recurrent neural networks. Computers & Chemical Engineering, 126: 189–203.
Shariatkhah M-H, Haghifam M-R, Parsa-Moghaddam M, et al. (2015). Modeling the reliability of multi-carrier energy systems considering dynamic behavior of thermal loads. Energy and Buildings, 103: 375–383.
Shelat S, Daamen W, Kaag B, et al. (2020). A Markov-chain activity- based model for pedestrians in office buildings. Collective Dynamics, 5: A78.
Shi Z, O'Brien W, Gunay HB (2018). Development of a distributed building fault detection, diagnostic, and evaluation system. ASHRAE Transactions, 124(2), 23–37.
Sindhu S, Nehra V, Malik SC (2017). Reliability estimation of photovoltaic system using Markov process and dynamic programming approach. International Journal of Reliability and Safety, 11: 132.
Šnipas M, Radziukynas V, Valakevičius E (2017). Modeling reliability of power systems substations by using stochastic automata networks. Reliability Engineering & System Safety, 157: 13–22.
Sobolewski RA (2015). Wind farm reliability modelling using Bayesian networks and semi-Markov processes. Acta Energetica, 3: 71–76.
Soudari M, Srinivasan S, Balasubramanian S, et al. (2016). Learning based personalized energy management systems for residential buildings. Energy and Buildings, 127: 953–968.
Sýkora M, Marková J, Diamantidis D (2018). Bayesian network application for the risk assessment of existing energy production units. Reliability Engineering & System Safety, 169: 312–320.
Taal A, Itard L (2020). P&ID-based symptom detection for automated energy performance diagnosis in HVAC systems. Automation in Construction, 119: 103344.
Taal A, Itard L, Zeiler W (2018). A reference architecture for the integration of automated energy performance fault diagnosis into HVAC systems. Energy and Buildings, 179: 144–155.
Taheri S, Jooshaki M, Moeini-Aghtaie M (2021). Long-term planning of integrated local energy systems using deep learning algorithms. International Journal of Electrical Power & Energy Systems, 129: 106855.
Tanimoto J, Hagishima A (2005). State transition probability for the Markov Model dealing with on/off cooling schedule in dwellings. Energy and Buildings, 37: 181–187.
Tchangani AP, Noyes D (2006). Modeling dynamic reliability using dynamic Bayesian networks. Journal Européen Des Systèmes Automatisés, 40: 915–935.
Theristis M, Papazoglou IA (2014). Markovian reliability analysis of standalone photovoltaic systems incorporating repairs. IEEE Journal of Photovoltaics, 4: 414–422.
Ullah I, Ahmad R, Kim D (2018). A prediction mechanism of energy consumption in residential buildings using hidden Markov model. Energies, 11: 358.
Ulmeanu AP, Barbu VS, Tanasiev V, et al. (2017). Hidden Markov Models revealing the household thermal profiling from smart meter data. Energy and Buildings, 154: 127–140.
Veeramany A, Pandey MD (2011a). Reliability analysis of nuclear piping system using semi-Markov process model. Annals of Nuclear Energy, 38: 1133–1139.
Veeramany A, Pandey MD (2011b). Reliability analysis of nuclear component cooling water system using semi-Markov process model. Nuclear Engineering and Design, 241: 1799–1806.
Veeramany A, Pandey MD (2011c). Reliability analysis of digital feedwater regulating valve controller system using a semi- Markov process model. International Journal of Nuclear Energy Science and Technology, 6: 298.
Verbert K, Babuška R, de Schutter B (2017). Combining knowledge and historical data for system-level fault diagnosis of HVAC systems. Engineering Applications of Artificial Intelligence, 59: 260–273.
Wall J, Guo Y, Li J, et al. (2011). A dynamic machine learning-based technique for automated fault detection in HVAC systems. ASHRAE Transactions, 117(2): 449–456.
Wang C, Yan D, Jiang Y (2011). A novel approach for building occupancy simulation. Building Simulation, 4: 149–167.
Wang B, Wang Y, Chen X (2013a). Research on wind turbine generator dynamic reliability test system based on feature recognition. Research Journal of Applied Sciences, Engineering and Technology, 6: 3065–3071.
Wang J-J, Fu C, Yang K, et al. (2013b). Reliability and availability analysis of redundant BCHP (building cooling, heating and power) system. Energy, 61: 531–540.
Wang W, Lin Z, Chen J (2017a). Promoting energy efficiency of HVAC operation in large office spaces with a Wi-Fi probe enabled Markov time window occupancy detection approach. Energy Procedia, 143: 204–209.
Wang Z, Wang Z, He S, et al. (2017b). Fault detection and diagnosis of chillers using Bayesian network merged distance rejection and multi-source non-sensor information. Applied Energy, 188: 200–214.
Wang Z, Wang L, Liang K, et al. (2018b). Enhanced chiller fault detection using Bayesian network and principal component analysis. Applied Thermal Engineering, 141: 898–905.
Wang Z, Wang Z, Gu X, et al. (2018c). Feature selection based on Bayesian network for chiller fault diagnosis from the perspective of field applications. Applied Thermal Engineering, 129: 674–683.
Wang J, Zhang Q, Yoon S, et al. (2019). Reliability and availability analysis of a hybrid cooling system with water-side economizer in data center. Building and Environment, 148: 405–416.
Wang Y, Kong Y, Tang X, et al. (2020a). Short-term industrial load forecasting based on ensemble hidden Markov model. IEEE Access, 8: 160858–160870.
Wang Z, Dong Y, Liu W, et al. (2020b). A novel fault diagnosis approach for chillers based on 1-D convolutional neural network and gated recurrent unit. Sensors, 20: 2458.
Wang Z, Wang L, Tan Y, et al. (2021a). Fault detection based on Bayesian network and missing data imputation for building energy systems. Applied Thermal Engineering, 182: 116051.
Wang Z, Wang L, Tan Y, et al. (2021b). Fault diagnosis using fused reference model and Bayesian network for building energy systems. Journal of Building Engineering, 34: 101957.
Wolf S, Møller JK, Bitsch MA, et al. (2019). A Markov-Switching model for building occupant activity estimation. Energy and Buildings, 183: 672–683.
Wu G, Tong J, Zhang L, et al. (2018). Framework for fault diagnosis with multi-source sensor nodes in nuclear power plants based on a Bayesian network. Annals of Nuclear Energy, 122: 297–308.
Xiao F, Zhao Y, Wen J, et al. (2014). Bayesian network based FDD strategy for variable air volume terminals. Automation in Construction, 41: 106–118.
Xu B, Li H, Pang W, et al. (2019). Bayesian network approach to fault diagnosis of a hydroelectric generation system. Energy Science & Engineering, 7: 1669–1677.
Xu C, Chen H (2020). Abnormal energy consumption detection for GSHP system based on ensemble deep learning and statistical modeling method. International Journal of Refrigeration, 114: 106–117.
Yan Y, Luh PB, Pattipati KR (2017). Fault diagnosis of HVAC air-handling systems considering fault propagation impacts among components. IEEE Transactions on Automation Science and Engineering, 14: 705–717.
Yan Y, Cai J, Li T, et al. (2021). Fault prognosis of HVAC air handling unit and its components using hidden-semi Markov model and statistical process control. Energy and Buildings, 240: 110875.
Yang L, He M, Zhang J, et al. (2015). Support-vector-machine- enhanced Markov model for short-term wind power forecast. IEEE Transactions on Sustainable Energy, 6: 791–799.
Ye Y, Grossmann IE, Pinto JM, et al. (2019). Modeling for reliability optimization of system design and maintenance based on Markov chain theory. Computers & Chemical Engineering, 124: 381–404.
Youngblood GM, Cook DJ (2007). Data mining for hierarchical model creation. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 37: 561–572.
Yu DC, Nguyen TC, Haddawy P (1999). Bayesian network model for reliability assessment of power systems. IEEE Transactions on Power Systems, 14: 426–432.
Yun P, Ren Y, Xue Y (2018). Energy-storage optimization strategy for reducing wind power fluctuation via Markov prediction and PSO method. Energies, 11: 3393.
Zeng Z, Fang Y-P, Zhai Q, et al. (2021). A Markov reward process-based framework for resilience analysis of multistate energy systems under the threat of extreme events. Reliability Engineering & System Safety, 209: 107443.
Zhai S, Sun Y, Cui H, et al. (2020). Adjustable loads control and stochastic stability analysis for multi-energy generation system based on Markov model. Neural Computing and Applications, 32: 1517–1529.
Zhao Y, Xiao F, Wang S (2013). An intelligent chiller fault detection and diagnosis methodology using Bayesian belief network. Energy and Buildings, 57: 278–288.
Zhao Y, Wen J, Wang S (2015). Diagnostic Bayesian networks for diagnosing air handling units faults—Part Ⅱ: Faults in coils and sensors. Applied Thermal Engineering, 90: 145–157.
Zhao Y, Wen J, Xiao F, et al. (2017). Diagnostic Bayesian networks for diagnosing air handling units faults—part Ⅰ: Faults in dampers, fans, filters and sensors. Applied Thermal Engineering, 111: 1272–1286.
Zhao Y, Li T, Zhang X, et al. (2019b). Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future. Renewable and Sustainable Energy Reviews, 109: 85–101.
Zhao Y, Zhang C, Zhang Y, et al. (2020). A review of data mining technologies in building energy systems: Load prediction, pattern identification, fault detection and diagnosis. Energy and Built Environment, 1: 149–164.
Publication history
Copyright
Acknowledgements
This work was supported by the National Key Research and Development Program of China (No. 2018YFE0116300]; and the National Natural Science Foundation of China (No. 51978601).
FEEDBACK 
TOP