Abstract
Objective: In order to better understand the research results of AC load prediction and carry out new research, the Air Conditioning (AC) load forecasting method plays an important role in the energy consumption of AC.
Methods: This paper summarizes the methods of building AC load prediction, mainly from the impact factors of AC operating load and the methods of AC system operating load forecasting to introduce the current status of load prediction. This paper describes some studies on load influencing factors, compares the advantages and disadvantages of modeling methods for AC operation load prediction and points out the research direction of AC load forecasting.
Results: The current research methods are summarized and analyzed. Traditional forecasting methods are no longer applicable to air conditioning systems. From the current research, combinatorial prediction has become a hot research object. This method combines two or more methods to reduce the prediction error and shorten the prediction time.
Conclusion: This paper points out some shortcomings of the present research and future research suggestions are given in the three aspects of sharing AC operation data, selecting the key factors of AC, and exploring the new methods.
Keywords: Air conditioning system, load prediction, forecasting methods, energy saving of air conditioning system, load forecasting factor, comparison of methods.
Graphical Abstract
[1]
P. Xu, and J. Yang, "Review of China’s energy industry in 2018 and outlook for 2019", Petrol. Sci. Technol., vol. 38, pp. 8-19, 2019.
[2]
C.J. Liu, and X.F. Jiang, "The overall recovery of the oil and gas order reconstruction industry -- an overview of the development of the oil and gas industry at home and abroad in 2018 and the outlook for 2019", Int. Pet. Econ., vol. 27, pp. 27-60, 2019.
[3]
M.S. Xiao, "Explore and analyze the key points of energy saving design of building HVAC project", Architec. Develop., vol. 2, pp. 100-101, 2018.
[4]
R. Jing, M. Wang, R.X. Zhang, N. Li, and Y.R. Zhao, "A study on energy performance of 30 commercial office buildings in Hong Kong", Energy Build., vol. 144, pp. 117-128, 2017.
[http://dx.doi.org/10.1016/j.enbuild.2017.03.042]
[http://dx.doi.org/10.1016/j.enbuild.2017.03.042]
[5]
X.K. Chang, Q. Xia, and X.Q. Jin, "Load forecasting of air-conditioning system based on BP improved model", Build. Therm. Vent. Air Condition., vol. 22, pp. 5-10, 2003.
[6]
S.S.K. Kwok, and E.W.M. Lee, "A study of the importance of occupancy to building cooling load in prediction by intelligent approach", Energy Convers. Manage., vol. 52, pp. 2555-2564, 2011.
[http://dx.doi.org/10.1016/j.enconman.2011.02.002]
[http://dx.doi.org/10.1016/j.enconman.2011.02.002]
[7]
K.X. Liu, M.L. Wang, Z. Tian, and C. Xiang, "The Research about the Impact of Climate Change on Power Load of Air Conditioning", In Power Ener. Eng. Conf. (APPEEC), 2012, pp. 1-4
[http://dx.doi.org/10.1109/APPEEC.2012.6307560]
[http://dx.doi.org/10.1109/APPEEC.2012.6307560]
[8]
K.M. Powell, A. Sriprasad, W.J. Cole, and T.F. Edgar, "Heating, cooling and electrical load forecasting for a large-scale district energy system", Energy, vol. 74, pp. 877-885, 2014.
[http://dx.doi.org/10.1016/j.energy.2014.07.064]
[http://dx.doi.org/10.1016/j.energy.2014.07.064]
[9]
X.N. Xu, G.S. Huang, H.W. Liu, L.Z. Chen, and Q.J. Liu, "The study of the dynamic load forecasting model about air-conditioning system based on the terminal user load", Energy Build., vol. 94, pp. 263-268, 2015.
[http://dx.doi.org/10.1016/j.enbuild.2015.01.018]
[http://dx.doi.org/10.1016/j.enbuild.2015.01.018]
[10]
Z.J. Ma, J.L. Song, and J.L. Zhang, "Energy consumption prediction of air-conditioning systems in buildings by selecting similar days based on combined weights", Energy Build., vol. 151, pp. 157-166, 2017.
[http://dx.doi.org/10.1016/j.enbuild.2017.06.053]
[http://dx.doi.org/10.1016/j.enbuild.2017.06.053]
[11]
X. Yang, J.Q. Yu, C.L. Guo, Y.J. Hua, and A.J. Zhao, "Dynamic prediction model of cooling load of ice storage air conditioning based on improved PSO-BP neural network", Int. J. Civ. Environ. Eng., vol. 41, pp. 168-174, 2019.
[12]
"H.J. LI, and D.S. He,Application of artificial neural network model to improve the generalization ability in air conditioning load prediction", Build. Sci., vol. 25, pp. 90-94, 2009.
[13]
J.R. Forrester, and W.J. Wepfer, "Formulation of a Load Prediction Algorithm for a Large Commercial Building", ASHRAE Trans., vol. 90, pp. 536-551, 1984.
[15]
Y. Guo, E. Nazarian, J. Ko, and K. Rajurkar, "Hourly cooling load forecasting using time-indexed ARX models with two-stage weighted least squares regression", Energy Convers. Manage., vol. 80, pp. 46-53, 2014.
[http://dx.doi.org/10.1016/j.enconman.2013.12.060]
[http://dx.doi.org/10.1016/j.enconman.2013.12.060]
[16]
Q. Guo, Z. Tian, and Y. Ding, "An improved office building cooling load prediction model based on multivariable linear regression", Energy Build., vol. 107, pp. 445-455, 2015.
[http://dx.doi.org/10.1016/j.enbuild.2015.08.041]
[http://dx.doi.org/10.1016/j.enbuild.2015.08.041]
[17]
G.H. Zhang, and J. Hu, "Multi-parameter regression air-conditioning load prediction control method based on dynamic feedback", Electromechan. Engineer. Technol., vol. 47, no. 11, pp. 64-69, 2018.
[18]
X.R. Tian, N.H. Cai, and Z.W. Zhang, "Prediction of air-conditioning load in summer based on meteorological factors and machine learning regression algorithm", Meteorol. Sci., vol. 39, pp. 548-555, 2019.
[19]
C.L. Fan, Y.D. Liao, and Y.F. Ding, "Development of a cooling load prediction model for air-conditioning system control of office buildings", Int. J. Low Carbon Technol., vol. 14, pp. 70-75, 2019.
[http://dx.doi.org/10.1093/ijlct/cty057]
[http://dx.doi.org/10.1093/ijlct/cty057]
[20]
J.W. MacArthur, A. Mathur, and J. Zhao, "On-line recursive estimation for load profile prediction", ASHRAE Trans., vol. 95, pp. 621-628, 1989.
[21]
A. Kimbara, S. Kurosu, R. Endo, K. Kamimura, T. Matsuba, and A. Yamada, "On-line prediction for load profile of an air-conditioning system", ASHRAE Trans., vol. 101, pp. 198-207, 1995.
[22]
J.E. Seem, and J.E. Braun, "Adaptive methods for real-time forecasting of building electrical demand", ASHRAE Trans., vol. 97, pp. 710-721, 1991.
[23]
D.S. He, and X. Zhang, "Analysis of air conditioning load prediction by modified seasonal exponential smoothing model", J. Tongji Univ., vol. 33, pp. 1672-1676, 2005.
[24]
L. Chen, "Application of wavelet time series in load forecasting of air conditioning", Fluid Machine., vol. 2, pp. 83-86, 2008.
[25]
M. Kawashima, C.E. Dorgan, and J.W. Mitchell, "Hourly thermal load prediction for the next 24 h by ARIMA, EWMA, LR, and an artificial neural network (Part 1)", ASHRAE Trans., vol. 101, pp. 186-200, 1995.
[26]
B.F. Zhao, Y.G. Wen, H. Yang, and Z.J. Hou, "“Analysis and comparison of four air-conditioning load prediction methods”, Build. Therm. Ventil", Air-conditioning, vol. 30, no. 54, pp. 65-67, 2011.
[27]
F.J. Ferrano, and K.V. Wong, "Prediction of thermal storage loads using a neural network", ASHRAE Trans., vol. 96, pp. 723-726, 1990.
[28]
L.X. Yan, X.C. Hong, G. Wang, and Y.Z. Qiu, "A Comparison of NARX and BP Neural Network in Short-Term Building Cooling Load Prediction", Appl. Mech. Mater., vol. 2987, pp. 1545-1548, 2014.
[29]
Z.K. Wang, X.Y. Zhang, M.Y. Lai, and B.P. Liu, "Predicting the Air-Conditioning Load under Drought Conditions Based on ELM", Key Eng. Mater., vol. 1244, pp. 1326-1329, 2011.
[http://dx.doi.org/10.4028/www.scientific.net/KEM.474-476.1326]
[http://dx.doi.org/10.4028/www.scientific.net/KEM.474-476.1326]
[30]
A.E. Ben-Nakhi, and M.A. Mahmoud, "Cooling load prediction for buildings using general regression neural networks", Energy Convers. Manage., vol. 45, pp. 2127-2141, 2003.
[http://dx.doi.org/10.1016/j.enconman.2003.10.009]
[http://dx.doi.org/10.1016/j.enconman.2003.10.009]
[31]
Y. Yao, Z.W. Lian, Z.J. Hou, and W.W. Liu, "An innovative air-conditioning load forecasting model based on RBF neural network and combined residual error correction", Int. J. Refrig., vol. 29, pp. 528-538, 2005.
[http://dx.doi.org/10.1016/j.ijrefrig.2005.10.008]
[http://dx.doi.org/10.1016/j.ijrefrig.2005.10.008]
[32]
Q. Li, and Q.L. Meng, "Prediction model of hourly air conditioning load of building based on RBF neural network", J. South China Univ. Technol., vol. 36, no. 10, pp. 25-30, 2008.
[33]
O. Ahmed, and K. Lemke, "System and method for predicting building thermal loads", U.S. Patent 7502768.
[34]
G.C. Liao, "Hybrid Improved Differential Evolution and Wavelet Neural Network with load forecasting problem of air conditioning", Int. J. Electr. Power Energy Syst., vol. 61, pp. 637-682, 2014.
[http://dx.doi.org/10.1016/j.ijepes.2014.04.014]
[http://dx.doi.org/10.1016/j.ijepes.2014.04.014]
[35]
Y.Y. Sun, W. Wang, Y.H. Zhao, and S. Pan, Predicting cooling loads for the next 24 hours based on general regression neural network: Methods and results., vol. 5. Adv. Mech. Eng., 2013.
[http://dx.doi.org/10.1155/2013/954185]
[http://dx.doi.org/10.1155/2013/954185]
[36]
Z.G. Yang, Y.B. Che, and K.W.E. Cheng, "Genetic algorithm-based RBF neural network load forecasting model", In 2007 IEEE Power Engineering Society General Meeting, Tampa, USA, 2007, pp. 1-6
[37]
G.N. Duan, and Y.R. Wang, "Accurate prediction simulation of HVAC energy consumption in super high-rise buildings", Computer Simulation, vol. 35, pp. 317-379, 2018.
[38]
C.W. Hu, and D. Wei, "Prediction on hourly cooling load of buildings based on neural networks", Int. J. Smart Home, vol. 9, pp. 35-52, 2015.
[http://dx.doi.org/10.14257/ijsh.2015.9.2.04]
[http://dx.doi.org/10.14257/ijsh.2015.9.2.04]
[39]
S. Chai, Feed-forward variable temperature difference control for central air-conditioning cooling water system based on load prediction., Beijing University of Civil Engineering and Architecture, 2019.
[40]
Y.S. Li, J. Lu, Y.C. Li, and C.D. Gu, "Research on operation strategy of ice storage air conditioning system based on load prediction", Heating Ventilation Air Conditioning (HVAC), vol. 49, pp. 129-143, 2019.
[41]
Q. Li, Q.L. Meng, J.J. Cai, H. Yoshino, and A. Mochida, "Applying support vector machine to predict hourly cooling load in the building", Appl. Energy, vol. 86, pp. 2249-2256, 2008.
[http://dx.doi.org/10.1016/j.apenergy.2008.11.035]
[http://dx.doi.org/10.1016/j.apenergy.2008.11.035]
[42]
Q. Li, Q.L. Meng, J.J. Cai, H. Yoshino, and A. Mochida, "Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks", Energy Convers. Manage., vol. 50, no. 1, pp. 90-96, 2008.
[http://dx.doi.org/10.1016/j.enconman.2008.08.033]
[http://dx.doi.org/10.1016/j.enconman.2008.08.033]
[43]
X.M. Li, J.H. Lv, L.X. Ding, G. Xu, and J.B. Li, "Building cooling load forecasting model based on LS-SVM", In 2009 Asia-Pacific Conference on Information Processing, Guangdong, China, 2009, pp. 55-58
[44]
J.Y. An, S.J. Deng, X. Deng, F.H. Kuang, X.H. Li, and B.G. Xu, "“Comprehensive optimization of energy consumption of central air-conditioning and refrigeration system based on load forecasting”, Build. Therm. Vent", Air-conditioning, vol. 38, pp. 1-5, 2019.
[45]
X.M. Li, M. Shao, L.X. Ding, G. Xu, and J.B. Li, "Particle Swarm Optimization-based LS-SVM for Building Cooling Load Prediction", J. Comput. (Taipei), vol. 5, pp. 614-621, 2010.
[http://dx.doi.org/10.4304/jcp.5.4.614-621]
[http://dx.doi.org/10.4304/jcp.5.4.614-621]
[46]
Z.H. Chen, Y. Sun, G.L. Yang, T.F. Wu, G.Z. Li, and L.B. Xin, "Air conditioning load prediction based on DE-SVM algorithm", In Third International Symposium on Intelligent Information Technology and Security Informatics, Jinggangshan, China, 2010, pp. 276-279
[http://dx.doi.org/10.1109/IITSI.2010.41]
[http://dx.doi.org/10.1109/IITSI.2010.41]
[47]
X. Zhou, and J. Yang, "Parameters optimization of air conditioning load prediction model based on PSO-SVR", In 32nd China Control Conference, Xian, China, 2013, pp. 137-142
[48]
Y.M. Wen, and Y.Y. Chen, "Modified Parallel Cat Swarm Optimization in SVM Modeling for Short-term Cooling Load Forecasting", J. Softw., vol. 9, pp. 2093-2104, 2014.
[http://dx.doi.org/10.4304/jsw.9.8.2093-2104]
[http://dx.doi.org/10.4304/jsw.9.8.2093-2104]
[49]
X. Zhou, Q.D. Liu, and J.W. Yan, "Air-conditioning load prediction of office buildings based on wavelet decomposition and support vector machine", Heat. Vent. Air Conditioning (HVAC), vol. 46, pp. 114-107, 2016.
[50]
Y.X. Tao, H.R. Yan, H. Gao, Y.Y. Sun, and G. Li, "Application of SVR optimized by Modified Simulated Annealing (MSA-SVR) air conditioning load prediction model", J. Indus. Inform. Integr., vol. 15, pp. 247-251, 2019.
[http://dx.doi.org/10.1016/j.jii.2018.04.003]
[http://dx.doi.org/10.1016/j.jii.2018.04.003]
[51]
Y. Yao, Z.W. Lian, Z.J. Hou, and X.J. Zhou, "Combined forecasting for air-conditioning load with analytic hierarchy process", J. Harbin Inst. Technol., vol. 36, pp. 1269-1275, 2004.
[52]
Z.J. Hou, Z.W. Lian, Y. Yao, and X.J. Yuan, "Cooling-load prediction by the combination of rough set theory and an artificial neural-network based on data-fusion technique", Appl. Energy, vol. 83, pp. 1033-1046, 2005.
[http://dx.doi.org/10.1016/j.apenergy.2005.08.006]
[http://dx.doi.org/10.1016/j.apenergy.2005.08.006]
[53]
Y. Yao, Z.W. Lian, S.Q. Liu, and Z.J. Hou, "Hourly cooling load prediction by a combined forecasting model based on analytic hierarchy process", Int. J. Therm. Sci., vol. 43, pp. 1107-1118, 2004.
[http://dx.doi.org/10.1016/j.ijthermalsci.2004.02.009]
[http://dx.doi.org/10.1016/j.ijthermalsci.2004.02.009]
[54]
X.M. Li, L.X. Ding, M. Shao, G. Xu, and J.B. Li, "A novel air-conditioning load prediction based on ARIMA and BPNN model", In 2009 Asia-Pacific Conference on Information Processing, Shenzhen, China, 2009, pp. 51-54
[55]
R.Z. Jovanović, A.A. Sretenović, and B.D. Živković, "Ensemble of
various neural networks for prediction of heating energy
consumption"", Energy Build., vol. 94, pp. 189-199, 2015.
[http://dx.doi.org/10.1016/j.enbuild.2015.02.052]
[http://dx.doi.org/10.1016/j.enbuild.2015.02.052]
[56]
F. Zhang, and S.L. Li, "Building air-conditioning load prediction based on BP neural network", Intell. Build. Smart Cities, vol. 7, pp. 34-41, 2019.
[57]
J.Q. Yu, W.Q. Jing, A.J. Zhao, Y.H. Ren, M. Zhou, and D. Proto, "Application of Improved PSO-BP Neural Network in Cold Load Forecasting of Mall Air-Conditioning", J. Contr. Sci. Eng., 2019.
[http://dx.doi.org/10.1155/2019/2428176]
[http://dx.doi.org/10.1155/2019/2428176]
[58]
Z.J. Hou, Z.W. Lian, Y. Yao, and X.J. Yuan, "Cooling load prediction based on the combination of rough set theory and support vector machine", HVAC & R Res., vol. 12, pp. 227-352, 2006.
[http://dx.doi.org/10.1080/10789669.2006.10391182]
[http://dx.doi.org/10.1080/10789669.2006.10391182]
[59]
X.M. Li, L.X. Ding, J.H. Lv, and G. Xu, "A novel hybrid approach of KPCA and SVM for building cooling load prediction", In Third International Conference on Knowledge Discovery and Data Mining, Phuket, Thailand, 2010, pp. 522-526
[60]
G.Y. Fu, "Deep belief network based ensemble approach for cooling load forecasting of air-conditioning system", Energy, vol. 148, pp. 269-282, 2018.
[http://dx.doi.org/10.1016/j.energy.2018.01.180]
[http://dx.doi.org/10.1016/j.energy.2018.01.180]
Related Journals
International Journal of Sensors, Wireless Communications and Control
Related Books
Challenges and Opportunities for Deep Learning Applications in Industry 4.0
Recent Advances in IoT and Blockchain Technology
Augmented Intelligence: Deep Learning, Machine Learning, Cognitive Computing, Educational Data Mining
Human-Computer Interaction and Beyond: Advances Towards Smart and Interconnected Environments Part II
Machine Learning and Its Application: A Quick Guide for Beginners
Computational Intelligence For Data Analysis
Applied Machine Learning and Multi-Criteria Decision-Making in Healthcare
Advanced Computing Techniques: Implementation, Informatics and Emerging Technologies
How to Design Optimization Algorithms by Applying Natural Behavioral Patterns
Human-Computer Interaction and Beyond: Advances Towards Smart and Interconnected Environments (Part I)
Article Metrics
15