Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy

Applied Energy

Volumn 123, Issue , 2014, Pages 168-178

  Jain, Rishee K ^a   Smith, Kevin M ^b   Culligan, Patricia J ^b   Taylor, John E ^c  

^a POLYTECHNIC UNIVERSITY   (United States)

^b Department of Earth and Environmental Engineering Columbia University ^*  (United States)

^c State University   (United States)

Author keywords

Forecasting; Machine learning; Monitoring; Prediction; Residential building; Support vector regression

Indexed keywords

ELECTRIC MEASURING INSTRUMENTS; ENERGY EFFICIENCY; ENERGY UTILIZATION; FLOORS; FORECASTING; LEARNING SYSTEMS; MONITORING; OFFICE BUILDINGS; REGRESSION ANALYSIS; SENSORS;

BUILDING ENERGY CONSUMPTION; COMPLEX RELATIONSHIPS; ENVIRONMENTAL EMISSIONS; MACHINE LEARNING TECHNIQUES; RESIDENTIAL BUILDING; SMART METERING TECHNOLOGY; SUPPORT VECTOR REGRESSION (SVR); TEMPORAL AND SPATIAL;

ELECTRIC LOAD FORECASTING;

BUILDING; DATA SET; ENERGY EFFICIENCY; ENERGY USE; FORECASTING METHOD; REGRESSION ANALYSIS; RESIDENTIAL LOCATION; SENSOR; SPATIOTEMPORAL ANALYSIS; URBAN AREA;

NEW YORK [NEW YORK (STT)]; NEW YORK [UNITED STATES]; UNITED STATES;

EID: 84896085639     PISSN: 03062619     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apenergy.2014.02.057     Document Type: Article

References (33)

1. U.S. Department of Energy. Buildings energy data Book; 2013. Retrieved from: http://buildingsdatabook.eren.doe.gov/.
2. PlaNYC - Green buildings & energy efficiency. The City of New York; 2013.
3. Petersen S., Svendsen S. Method and simulation program informed decisions in the early stages of building design. Energy Build 2010, 42:1113-1119.
4. Virote J., Neves-silva R. Stochastic models for building energy prediction based on occupant behavior assessment. Energy Build 2012, 53:183-193.
5. Kusiak A., Xu G. Modeling and optimization of HVAC systems using a dynamic neural network. Energy 2012, 42:241-250.
6. Howard B., Parshall L., Thompson J., Hammer S., Dickinson J., Modi V. Spatial distribution of urban building energy consumption by end use. Energy Build 2012, 45:141-151.
7. Edwards R.E., New J., Parker L.E. Predicting future hourly residential electrical consumption: a machine learning case study. Energy Build 2012, 49:591-603.
8. Zhao H-x., Magoulès F. A review on the prediction of building energy consumption. Renew Sustain Energy Rev 2012, 16:3586-3592.
9. Kreider J.F., Haberl J.S. Predicting hourly building energy usage 1994, ASHRAE Journal (American Society of Heating, Refrigerating and Air-Conditioning Engineers), United States, p. 36.
10. Mackay D.J.C. Bayesian nonlinear modeling for the prediction competition. ASHRAE Trans 1994, 100:1053-1062.
11. Yang J., Rivard H., Zmeureanu R. On-line building energy prediction using adaptive artificial neural networks. Energy Build 2005, 37:1250-1259.
12. Dong B., Cao C., Lee S.E. Applying support vector machines to predict building energy consumption in tropical region. Energy Build 2005, 37:545-553.
13. Karatasou S., Santamouris M., Geros V. Modeling and predicting building's energy use with artificial neural networks: methods and results. Energy Build 2006, 38:949-958.
14. Li Q., Meng Q., Cai J., Yoshino H., Mochida A. Predicting hourly cooling load in the building: a comparison of support vector machine and different artificial neural networks. Energy Convers Manage 2009, 50:90-96.
15. Li Q., Meng Q., Cai J., Yoshino H., Mochida A. Applying support vector machine to predict hourly cooling load in the building. Appl Energy 2009, 86:2249-2256.
16. Wong S.L., Wan K.K.W., Lam T.N.T. Artificial neural networks for energy analysis of office buildings with daylighting. Appl Energy 2010, 87:551-557.
17. Kolter JZ, Ferreira J. A large-scale study on predicting and contextualizing building energy usage. In: Proceedings of the twenty-fifth AAAI conference on artificial intelligence: AAAI. p. 1349-56.
18. Li K., Su H., Chu J. Forecasting building energy consumption using neural networks and hybrid neuro-fuzzy system: a comparative study. Energy Build 2011, 43:2893-2899.
19. Kavaklioglu K. Modeling and prediction of Turkey's electricity consumption using support vector regression. Appl Energy 2011, 88:368-375.
20. Zhao H-x, Magoulès F. Feature selection for predicting building energy consumption based on statistical learning method. J Algor Comput Technol 2012, 6:59-78.
21. Zhang W.Y., Hong W.-C., Dong Y., Tsai G., Sung J.-T., Fan G-f. Application of SVR with chaotic GASA algorithm in cyclic electric load forecasting. Energy 2012, 45:850-858.
22. Espinoza M., Espinoza M., Suykens J.A.K., Moor B.D. Fixed-size least squares support vector machines: a large scale application in electrical load forecasting. CMS 2006, 3:113-129.
23. Drucker H., Burges C.J., Kaufman L., Smola A., Vapnik V. Support vector regression machines. Adv Neural Inform Process Syst 1997, 155-161.
24. Ruas GIS, Bragatto TAC, Lamar MV, Aoki AR, de Rocco SM. Electrical energy demand prediction using artificial neural networks and support vector regression. In: 3rd International Symposium on Communications, Control and Signal Processing, ISCCSP 2008, IEEE; 2008.
25. Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., et al. Scikit-learn: machine learning in python. J Mach Learn Res 2011, 12:2825-2830.
26. Chang C.-C., Lin C.-J. LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2011, 2:27.
27. Vapnik V. The nature of statistical learning theory 2000, Springer.
28. Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. In: IJCAI 1995. p. 1137-45.
29. Gulbinas R., Jain R., Taylor J., Peschiera G., Golparvar-Fard M. Network eco-informatics: development of a social eco-feedback system to drive energy efficiency in residential buildings. ASCE J Comput Civil Eng 2014, 28(1):89-98.
30. Emery A.F., Kippenhan C.J. A long term study of residential home heating consumption and the effect of occupant behavior on homes in the Pacific Northwest constructed according to improved thermal standards. Energy 2006, 31:677-693.
31. Lü X., Lu T., Kibert C.J., Viljanen M. A novel dynamic modeling approach for predicting building energy performance. Appl Energy 2014, 114:91-103.
32. Yu Z., Fung B.C.M., Haghighat F., Yoshino H., Morofsky E. A systematic procedure to study the influence of occupant behavior on building energy consumption. Energy Build 2011, 43:1409-1417.
33. Venkatesan N., Solanki J., Solanki S.K. Residential demand response model and impact on voltage profile and losses of an electric distribution network. Appl Energy 2012, 96:84-91.