References
- J.W. Moon, J.J. Kim, ANN-based thermal control methods for residential buildings. Build. Environ. 2010, 45, 1612-1625. https://doi.org/10.1016/j.buildenv.2010.01.009
- J.W. Moon, ANN-Based Model-Free Thermal Controls for Residential Buildings. Ph.D. Thesis, Taubman College of Architecture and Urban Planning, University of Michigan, Ann Arbor, MI, USA, 2009.
- W. McCulloch, W. Pitts, A logical calculus of ideas immanent in nervous activity. The Bull. Math. Biophys. 1943, 5, 115-133. https://doi.org/10.1007/BF02478259
- J.W. Moon, S.K. Jung, Y. Kim, S. Han, Comparative study of artificial intelligence-based building thermal control methods- Application of fuzzy, adaptive neuro-fuzzy inference system, and artificial neural network. Appl. Therm. Eng. 2011, 31, 2422-2429. https://doi.org/10.1016/j.applthermaleng.2011.04.006
- S.A. Kalogirou, C.C.Neocleous, C.N. Schizas, Building heating load estimation using artificial neural networks. In Proceedings of the International Conference CLIMA 2000, Brussels, Belgium, 30 August-2 September 1997; pp.1-8.
- K.W. Shin, Y.S. Lee, The study on cooling load forecast of an unit building using neural networks. Int. J. Air Cond. Refrig. 2003, 11, 170-177.
- S.H. Kim, B.S. Kim, Building load prediction using artificial neural networks in office renovation. In Proceeding of 3rd International Symposium on Architectural Interchanges in Asia. The Architectural Institute of Korea, Cheju, Korea, 23-25 February 2000; pp. 604-612.
- D. Datta, S.A. Tassou, D. Marriott, Application of neural networks for the prediction of the energy consumption in a supermarket. In Proceedings of the International Conference CLIMA 2000, Brussels, Belgium, 30 August-2 September 1997; pp. 98-107.
- S.A. Kalogirou, M. Bojic, Artificial neural networks for the prediction of the energy consumption of a passive solar building. Energy 2000, 25, 479-491. https://doi.org/10.1016/S0360-5442(99)00086-9
- J.F. Kreider, X.A. Wang, D. Anderson, J. Dow, Expert systems, neural networks and artificial intelligence applications in commercial building HVAC operations. Autom. Constr. 1992, 1, 225-238. https://doi.org/10.1016/0926-5805(92)90015-C
- K.M. Aydinalp, V.I. Ugursal, Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end use energy consumption in the residential sector. Appl. Energy 2008, 85, 271-296. https://doi.org/10.1016/j.apenergy.2006.09.012
- M. Aydinalp, V.I. Ugursal, A.S. Fung, Modeling of the space and domestic hot-water heating energy-consumption in the residential sector using neural network. Appl. Energy 2004, 79, 159-178. https://doi.org/10.1016/j.apenergy.2003.12.006
- R. Platon, V.R. Dehkordi, J. Martel, Hourly prediction of a building's electricity consumption using case-based reasoning, artificial neural networks and principal component analysis. Energy Build. 2015, 92, 10-18. https://doi.org/10.1016/j.enbuild.2015.01.047
- R.Z. Jovanovic, A.A. Sretenovic, B.D. Zivkovic, Ensemble of various neural networks for prediction of heating energy consumption. Energy Build. 2015, 94, 189-199. https://doi.org/10.1016/j.enbuild.2015.02.052
- B. Yuce, H. Li, Y. Rezgui, L. Petri, B.; Yang, C. Jayan, Utilizing artificial neural network to predict energy consumption and thermal comfort level: An indoor swimming pool case study. Energy Build. 2014, 80, 45-56. https://doi.org/10.1016/j.enbuild.2014.04.052
- S. Pandey, D.A. Hindoliya, R. Mod, Artificial neural networks for predicting indoor temperature using roof passive cooling techniques in buildings in different climatic conditions, Appl. Soft Comput. 2012, 12, 1214-1226 https://doi.org/10.1016/j.asoc.2011.10.011
- N. Morel, M. Bauer, M. El-Khoury, J. Krauss, Neurobat, a predictive and adaptive heating control system using artificial neural networks. Int. J. Sol. Energy 2001, 21, 161-201. https://doi.org/10.1080/01425910108914370
- A. Abbassi, L. Bahar, Application of neural network for the modeling and control of evaporative condenser cooling load. Appl. Therm. Eng. 2005, 25, 3176-3186. https://doi.org/10.1016/j.applthermaleng.2005.04.006
- A. Marvuglia, A. Messineo, G. Nicolosi, Coupling a neural network temperature predictor and a fuzzy logic controller to perform thermal comfort regulation in an office building. Build. Environ. 2014, 72, 287-299. https://doi.org/10.1016/j.buildenv.2013.10.020
- M. Mohanraj, S. Jayaraj, C. Muraleedharan, Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems-A review. Renew. Sustain. Energy Reviews 2012, 16, 1340-1358. https://doi.org/10.1016/j.rser.2011.10.015
- J.W. Moon, J.H. Lee, Y. Yoon, S. Kim, Determining optimum control of double skin envelope for indoor thermal environment based on artificial neural network. Energy Build. 2014, 69, 175 -183. https://doi.org/10.1016/j.enbuild.2013.10.016
- J.W. Moon, Performance of ANN based predictive and adaptive thermal control methods for disturbances in and around residential buildings. Build. Environ. 2011, 48, 15-26.
- Y.K. Baik. J.W. Moon, Development and performance evaluation of optimal control logics for the two position and variableheating systems in double skin façade buildings. The Int. J. Korea Inst. Ecol. Archit. Environ. 2014, 14, 71-77.
- J. Yang, H. Rivard, R. Zmeureanu, Online building energy prediction using adaptive artificial neural networks. Energy Build. 2005, 37, 1250-1259. https://doi.org/10.1016/j.enbuild.2005.02.005
- American Society of Heating, Refrigerating, and Air-Conditioning Engineer. ASHRAE Guideline14-Measurement of energy and demand savings. ASHRAE Inc.; 2002.
Cited by
- Modeling and Optimizing a Chiller System Using a Machine Learning Algorithm vol.12, pp.15, 2017, https://doi.org/10.3390/en12152860
- Development of Occupant Pose Classification Model Using Deep Neural Network for Personalized Thermal Conditioning vol.13, pp.1, 2020, https://doi.org/10.3390/en13010045
- Development of a Prediction Model and an Adaptive Control Algorithm for the Data Center Thermal Environment vol.20, pp.6, 2017, https://doi.org/10.12813/kieae.2020.20.6.107