Detection and interpretation of anomalies in building energy use through inverse modeling

References

• Abdelalim, A., W. O’Brien, and Z. Shi. 2017. Development of Sankey diagrams to visualize real HVAC performance. Energy and Buildings 149:282–97.
   Web of Science ®Google Scholar
• Abushakra, B. 1997. An inverse model to predict and evaluate the energy performance of large commercial and institutional buildings. Building Simulation, Prague, Czech Republic. http://www.ibpsa.org/proceedings/BS1997/BS97_P183.pdf IBPSA.
   Google Scholar
• Abushakra, B., and D. Claridge. 2001. Accounting for the occupancy variable in inverse building energy baselining models. International Conference for Enhanced Building Operations. https://oaktrust.library.tamu.edu/handle/1969.1/5161, Texas A&M University.
   Google Scholar
• Abushakra, B. 2001. Accounting for the occupancy variable in inverse building energy baselining models, https://oaktrust.library.tamu.edu/handle/1969.1/154781, Texas A&M University.
   Google Scholar
• Abushakra, B., and M.T. Paulus. 2016. An hourly hybrid multi-variate change-point inverse model using short-term monitored data for annual prediction of building energy performance, part I: Background (1404-RP). Science and Technology for the Built Environment 22(7):976–83.
   Web of Science ®Google Scholar
• Abushakra, B., and M.T. Paulus. 2016. An hourly hybrid multi-variate change-point inverse model using short-term monitored data for annual prediction of building energy performance, part II: Methodology (1404-RP). Science and Technology for the Built Environment 22(7):984–95.
   Web of Science ®Google Scholar
• Abushakra, B., and M.T. Paulus. 2016. An hourly hybrid multi-variate change-point inverse model using short-term monitored data for annual prediction of building energy performance, part III: Results and analysis (1404-RP). Science and Technology for the Built Environment 22(7):996–1009.
   Web of Science ®Google Scholar
• Ahmad, M.W., M. Mourshed, and Y. Rezgui. 2017. Trees vs neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption. Energy and Buildings 147:77–89.
   Web of Science ®Google Scholar
• Araya, D.B., K. Grolinger, H.F. ElYamany, M.A. Capretz, and G. Bitsuamlak. 2017. An ensemble learning framework for anomaly detection in building energy consumption. Energy and Buildings 144:191–206.
   Web of Science ®Google Scholar
• ASHRAE. 2011. Procedures for Commercial Building Energy Audits, 2nd ed. Atlanta: ASHRAE.
   Google Scholar
• ASHRAE. 2014. Guideline 14: Measurement of Energy, Demand, and Water Savings. Atlanta: ASHRAE.
   Google Scholar
• Bourassa, N.J., M.A. Piette, and N. Motegi. 2004. Evaluation of retrocommissioning persistence in large commercial buildings. National Conference on Building Commissioning. https://cloudfront.escholarship.org/dist/prd/content/qt9tj6h68r/qt9tj6h68r.pdf.
   Google Scholar
• Braun, J.E., and N. Chaturvedi. 2002. An inverse gray-box model for transient building load prediction. HVAC&R Research 8(1):73–99.
   Web of Science ®Google Scholar
• Breiman, L., J. Friedman, C.J. Stone, and R.A. Olshen. 1984. Classification and Regression Trees. Boca Raton, FL: CRC Press.
   Google Scholar
• Carpenter, J., K.A. Woodbury, and Z. O'Neill. 2018. Using change-point and Gaussian process models to create baseline energy models in industrial facilities: A comparison. Applied Energy 213:415–25.
   Web of Science ®Google Scholar
• Chae, Y.T., R. Horesh, Y. Hwang, and Y.M. Lee. 2016. Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings. Energy and Buildings 111:184–94.
   Web of Science ®Google Scholar
• Cho, S. Y. 2008. The persistence of savings obtained from commissioning of existing buildings. Master's thesis, Texas A&M University. http://oaktrust.library.tamu.edu/handle/1969.1/55022
   Google Scholar
• Dong, B., C. Cao, and S.E. Lee. 2005. Applying support vector machines to predict building energy consumption in tropical region. Energy and Buildings 37(5):545–53.
   Web of Science ®Google Scholar
• Ferreira, M., M. Almeida, A. Rodrigues, and S.M. Silva. 2016. Comparing cost-optimal and net-zero energy targets in building retrofit. Building Research & Information 44(2):188–201.
   Web of Science ®Google Scholar
• Friedman, H., A. Potter, T. Haasl, D. Claridge, and S. Cho. 2003. Persistence of benefits from new building commissioning. National Conference on Building Commissioning. https://aceee.org/files/proceedings/2002/data/papers/SS02_Panel3_Paper11.pdf.
   Google Scholar
• Fux, S.F., A. Ashouri, M.J. Benz, and L. Guzzella. 2014. EKF based self-adaptive thermal model for a passive house. Energy and Buildings 68:811–17.
   Web of Science ®Google Scholar
• Golden, A., K. Woodbury, J. Carpenter, and Z. O’Neill. 2017. Change point and degree day baseline regression models in industrial facilities. Energy and Buildings 144:30–41.
   Web of Science ®Google Scholar
• Gunay, B., and W. Shen. 2017. Connected and distributed sensing in buildings: Improving operation and maintenance. IEEE Systems, Man, and Cybernetics Magazine 3(4):27–34.
   Web of Science ®Google Scholar
• Gunay, B., W. Shen, and G. Newsham. 2017a. Inverse blackbox modeling of the heating and cooling load in office buildings. Energy and Buildings 142:200–10.
   Web of Science ®Google Scholar
• Gunay, B., W. Shen, and C. Yang. 2017b. Characterization of a building's operation using automation data: A review and case study. Building and Environment 118:196–210.
   Web of Science ®Google Scholar
• Haberl, J.S., and S. Thamilseran. 1996. Great energy predictor shootout II: Measuring retrofit savings—Overview and discussion of results. Atlanta: ASHRAE.
   Google Scholar
• Jacob, D., S. Dietz, S. Komhard, C. Neumann, and S. Herkel. 2010. Black-box models for fault detection and performance monitoring of buildings. Journal of Building Performance Simulation 3(1):53–62.
   Web of Science ®Google Scholar
• Jalori, S., and T.A. Reddy. 2015a. A new clustering method to identify outliers and diurnal schedules from building energy interval data. ASHRAE Transactions 121:33.
   Google Scholar
• Jalori, S., and T.A. Reddy. 2015b. A unified inverse modeling framework for whole-building energy interval data: Daily and hourly baseline modeling and short-term load forecasting. ASHRAE Transactions 121:156.
   Google Scholar
• Karatasou, S., M. Santamouris, and V. Geros. 2006. Modeling and predicting building's energy use with artificial neural networks: Methods and results. Energy and Buildings 38(8):949–58.
   Web of Science ®Google Scholar
• Katipamula, S., and M. R. Brambley. 2005a. Review article: Methods for fault detection, diagnostics, and prognostics for building systems—A review, Part I. HVAC&R Research 11(1):3–25.
   Web of Science ®Google Scholar
• Katipamula, S., and M. R. Brambley. 2005b. Review article: Methods for fault detection, diagnostics, and prognostics for building systems—A review, Part II. HVAC&R Research 11(2):169–87.
   Web of Science ®Google Scholar
• Kelsey, J., and D. Pearson. 2011. Updated procedures for commercial building energy audits. ASHRAE Transactions 117(2):374–81.
   Google Scholar
• Kim, W., and S. Katipamula. 2017. A Review of fault detection and diagnostics methods for building systems. Science and Technology for the Built Environment 24:3–21.
   Web of Science ®Google Scholar
• Krarti, M., J.F. Kreider, D. Cohen, and P. Curtiss. 1998. Estimation of energy savings for building retrofits using neural networks. Journal of Solar Energy Engineering 120(3):211–16.
   Web of Science ®Google Scholar
• Li, Q., Q. Meng, J. Cai, H. Yoshino, and A. Mochida. 2009. Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks. Energy Conversion and Management 50(1):90–96.
   Web of Science ®Google Scholar
• Liu, F., H. Jiang, Y.M. Lee, J. Snowdon, and M. Bobker. 2011a. Statistical modeling for anomaly detection, forecasting and root cause analysis of energy consumption for a portfolio of buildings. Building Simulation, Sydney, Australia. http://www.ibpsa.org/proceedings/BS2011/P_1789.pdf, IBPSA.
   Google Scholar
• Liu, J., J.-C. Baltazar, and D.E. Claridge. 2011b. Improvements to a methodology for estimating potential energy savings from existing building-commissioning/retrofit measures. ASHRAE Transactions 117(1):868–84.
   Google Scholar
• Mills, E. 2011. Building commissioning: A golden opportunity for reducing energy costs and greenhouse gas emissions in the United States. Energy Efficiency 4(2):145–73.
   Web of Science ®Google Scholar
• NRCan. 2011. Energy savings toolbox: An energy audit manual and tool. Ottawa, Canada: Natural Resources Canada.
   Google Scholar
• Paulus, M.T., D.E. Claridge, and C. Culp. 2015. Algorithm for automating the selection of a temperature dependent change point model. Energy and Buildings 87:95–104.
   Web of Science ®Google Scholar
• Pérez-Lombard, L., J. Ortiz, R. González, and I.R. Maestre. 2009. A review of benchmarking, rating and labelling concepts within the framework of building energy certification schemes. Energy and Buildings 41(3):272–78.
   Web of Science ®Google Scholar
• Reddy, T. A. 2011. Applied Data Analysis and Modeling for Energy Engineers and Scientists. New York: Springer Science & Business Media.
   Google Scholar
• Singh, V., T.A. Reddy, and B. Abushakra. 2013. Predicting annual energy use in buildings using short-term monitoring and utility bills: The hybrid inverse model using daily data (HIM-D). ASHRAE Transactions 119(2):169–80.
   Google Scholar
• Singh, V., T.A. Reddy, and B. Abushakra. 2014. Predicting annual energy use in buildings using short-term monitoring: The dry-bulb temperature analysis (DBTA) method. ASHRAE Transactions 120:397–405.
   Google Scholar
• Srivastav, A., A. Tewari, and B. Dong. 2013. Baseline building energy modeling and localized uncertainty quantification using Gaussian mixture models. Energy and Buildings 65:438–47.
   Web of Science ®Google Scholar
• Thamilseran, S., and J. Haberl. 1995. A bin method for calculating energy conservation retrofit savings in commercial buildings. ASME-JSES-JSME. Solar Energy Conference, Maui, Hawaii. https://oaktrust.library.tamu.edu/bitstream/handle/1969.1/6640/ESL-HH-94-05-21.pdf
   Google Scholar
• Thomas Ng, S., M. Skitmore, and K.F. Wong. 2008. Using genetic algorithms and linear regression analysis for private housing demand forecast. Building and Environment 43(6):1171–84.
   Web of Science ®Google Scholar
• Touzani, S., J. Granderson, and S. Fernandes. 2018. Gradient boosting machine for modeling the energy consumption of commercial buildings. Energy and Buildings 158:1533–43.
   Web of Science ®Google Scholar
• Wang, Z., and R.S. Srinivasan. 2017. A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models. Renewable and Sustainable Energy Reviews 75:796–808.
   Web of Science ®Google Scholar
• Witten, I.H., E. Frank, M. Hall, and C. Pal. 2016. Data Mining: Practical Machine Learning Tools and Techniques. Burlington, MA: Morgan Kaufmann.
   Google Scholar
• Yu, Z., F. Haghighat, B.C.M. Fung, and H. Yoshino. 2010. A decision tree method for building energy demand modeling. Energy and Buildings 42(10):1637–46.
   Web of Science ®Google Scholar
• Zhang, Y., Z. O'Neill, B. Dong, and G. Augenbroe. 2015. Comparisons of inverse modeling approaches for predicting building energy performance. Building and Environment 86:177–90.
   Web of Science ®Google Scholar
• Zhou, Q., S. Wang, X. Xu, and F. Xiao. 2008. A grey-box model of next-day building thermal load prediction for energy-efficient control. International Journal of Energy Research 32(15):1418–31.
   Web of Science ®Google Scholar