References
- Agarwal , Y. , Weng , T. and Gupta , R. The energy dashboard: improving the visibility of energy consumption at a campus-wide scale . Proceedings of the first ACM workshop on embedded sensing systems for energy-efficiency in buildings . BuildSys'09 , New York , NY : ACM .
- Andersen , K. , Madsen , H. and Hansen , L. 2000 . Modelling the heat dynamics of a building using stochastic differential equations . Energy and Buildings , 31 : 13 – 24 .
- BC Hydro . 2010 . BC hydro smart metering and infrastructure program [online] Available from: http://www.bchydro.com/planning_regulatory/projects/smart_metering_inf' rastructure_program.html [Accessed 8 April 2011]
- Beis , J. and Lowe , D. 1997 . Shape indexing using approximate nearest-neighbour search in high-dimensional spaces . IEEE international conference on computer vision and pattern recognition (CVPR'97) , : 1000 – 1006 .
- Crawley , D. EnergyPlus: an update . Proceedings of SimBuild, building sustainability and performance through simulation . August , Boulder , CO .
- Crawley , D. 2000 . EnergyPlus: energy simulation program . ASHRAE Journal , 42 ( 4 ) : 49 – 56 .
- Darby , S. 2006 . The effectiveness of feedback on energy consumption. A review for DEFRA of the literature on metering, billing and direct displays [online] Environmental Change Institute, University of Oxford. Available from: http://www.eci.ox.ac.uk/research/energy/downloads/smart-metering-report.pdf [Accessed 1 April 2011]
- Dhar , A. , Reddy , T. and Claridge , D . 1999 . A fourier series model to predict hourly heating and cooling energy use in commercial buildings with outdoor temperature as the only weather variable . Journal of Solar Energy Engineering , 121 ( 1 ) : 47 – 53 .
- Dodier , R. and Henze , G. 1996 . Statistical analysis of neural networks as applied to building energy prediction . Journal of Solar Energy Engineering ,
- Dong , B. , Cao , C. and Lee , S. 2005 . Applying support vector machines to predict building energy consumption in tropical region . Energy and Buildings , 37 : 545 – 553 .
- Georgescu , C. , Afshari , A. and Bornard , G. Optimal adpative predictive control and fault detection of residential building heating systems . Proceedings of the 3rd IEEE conference on control applications . August . pp. 1601 – 1606 . Glasgow : IEEE .
- Granderson , J. 2009 . Building energy information systems: state of technology and user case studies Technical report LBNL-2899E, Lawrence Berkeley National Laboratory
- Haberl , J.S. and Bou-Saada , T.E. 1998 . Procedures for calibrating hourly simulation models to measured building energy and environmental data . Journal of Solar Energy Engineering , 120 : 193 – 204 .
- Hastie , T. , Tibshirani , R. and Friedman , J. 2009 . The elements of statistical learning , New York : Springer .
- Henze , G. , Dodier , R. and Krarti , M. 1998 . Development of a predictive optimal controller for thermal energy storage systems . ASHRAE Transactions , 104 : 54
- Huang , S. and Shih , K. 2003 . Short-term load forecasting via ARMA model identification including non-Gaussian process considerations . IEEE Transactions on Power Systems , 18 ( 2 ) : 673 – 679 .
- Huber , P.J. 1981 . Robust statistics , New York : Wiley .
- Kalogirou , S. 2000 . Applications of artificial neural-networks for energy systems . Applied Energy , 67 : 17 – 35 .
- Karatasou , S. , Santamouris , M. and Geros , V. 2006 . Modeling and predicting building's energy use with artificial neural networks: methods and results . Energy and Buildings , 38 ( 8 ) : 949 – 958 .
- Kawashima , M. , Dorgan , C. and Mitchell , J. 1996 . Optimizing system control with load prediction by neural networks for an ice-storage system . ASHRAE Transactions , 102 ( 1 ) : 1169 – 1178 .
- Kreider , J. and Haberl , J. 1994 . Predicting hourly building energy use: the great energy predictor shoot-out. Overview and discussion of results . ASHRAE Transactions , 94 ( 17/7 ) : 1104 – 1118 .
- Kreider , J. and Haberl , J. 1996 . The great energy predictor shoot-out II: measuring retrofit savings. Overview and discussion of results . ASHRAE Transactions , 96 ( 3/4 ) : 419 – 435 .
- Lee , K. and Braun , J. 2004 . Development and application of an inverse building model for demand response in small commercial buildings . SimBuild 2004, IBPSA-USA national conference , August, Boulder, CO
- Mackay , D. 1994 . Bayesian non-linear modelling for the prediction competition . ASHRAE Transactions , 100 : 1053 – 1062 .
- Mahdavi , A. 2001 . Simulation-based control of building systems operation . Building and Environment , 36 : 789 – 796 .
- Mahdavi , A. 2004 . Reflections on computational building models . Building and Environment , 39 : 913 – 925 .
- Mills , E. and Mathew , P. 2009 . Monitoring-based commissioning: benchmarking analysis of 24 UC/CSU/IOU projects Technical report LBNL-1972E, Lawrence Berkeley National Laboratory
- Nadaraya , E . 1964 . On estimating regression . Theory of Probability and its Applications , 9 ( 1 ) : 141 – 142 .
- New Buildings Institute . 2009 . Advanced metering and energy information systems [online] Grant 83378201. Available from: http://www.newbuildings.org [Accessed 1 April 2011]
- Nocedal , J. and Wright , S. 1999 . Numerical optimization Springer Series in Operations Research New York, Berlin, Heidelberg: Springer
- Petersen , J. 2007 . Dormitory residents reduce electricity consumption when exposed to real-time visual feedback and incentives . International Journal of Sustainability in Higher Education , 8 ( 1 ) : 16 – 33 .
- Secretary of State of California . 2007 . Assembly Bill 1103: an act to add Section 25402.10 to the Public Resources Code, relating to energy [online] Available from: www.leginfo.ca.gov/pub/07-08/bill/asm/ab_1101-1150/ab_1103_bill_20071012_chaptered.pdf [Accessed 1 April 2011]
- Wand , M. and Jones , M. 1995 . Kernel smoothing , London : Chapman and Hall .
- Wouters , P. and van Dijk , D. 2007 . Energy performance building directive platform: overall context and activities . EPBD Buildings Platform , P039
- Yang , J. , Rivard , H. and Zmeureanu , R. 2005 . On-line building energy prediction using adaptive artificial neural networks . Energy and Buildings , 37 : 1250 – 1259 .