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Research Article

Data-driven based estimation of HVAC energy consumption using an improved Fourier series decomposition in buildings

Fuxin Niu1, Zheng O’Neill1( ), Charles O’Neill2
Department of Mechanical Engineering, The University of Alabama, Tuscaloosa, AL, USA
Department of Aerospace and Mechanics Engineering, The University of Alabama, Tuscaloosa, AL, USA
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Abstract

Many data-driven algorithms are being explored in the field of building energy performance estimation. Choosing an appropriate method for a specific case is critical to guarantee a successful energy operation management such as measurement and verification. Currently, little research work on assessment of different data-driven algorithms using real time measurement data sets is available. In this paper, five commonly used data-driven algorithms, ARX, SS, N4S, discretized variable BN and continuous variable BN, are used to estimate HVAC related electricity energy consumption in a university dormitory. In practice, total energy consumption data is easily accessible, while separated HVAC energy consumption data is not commonly available due to expensive sub-metering and/or the complexity of mechanical and electrical layouts. A virtual sub-meter based on a decomposition method is proposed to separate HVAC energy consumption from the total building energy consumption, which is derived from an improved Fourier series based decomposition method.

Keywords

data-drivendecompositionFourier seriesHVAC energy estimation

References

 
Y Agarwal, B Balaji, R Gupta, J Lyles, M Wei, T Weng (2010). Occupancy-driven energy management for smart building automation. In: Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building, Zurich, Switzerland.
Crossref
 
H Akbari (1995). Validation of an algorithm to disaggregate whole-building hourly electrical load into end uses. Energy, 20: 1291–1301.
Crossref Google Scholar
 
H Akbari, SJ Konopacki (1998). Application of an end-use disaggregation algorithm for obtaining building energy-use data. Journal of Solar Energy Engineering, 120: 205–210.
Crossref Google Scholar
 
N Al-Anaswah, B Wilfling (2011). Identification of speculative bubbles using state-space models with Markov-switching. Journal of Banking & Finance, 35: 1073–1086.
Crossref Google Scholar
 
ASHRAE (2002). ASHRAE Guideline 14. Measurement of Energy and Demand Savings. Atlanta, GA, USA: American Society of Heating, Ventilating and Air Conditioning Engineers.
 
ASHRAE (2013). ASHRAE Handbook—Fundamentals. Atlanta, GA, USA: American Society of Heating Refrigeration and Air Conditioning Engineers.
 
J Cai, D Kim, JE Braun (2013). Application of subspace identification method in modeling multi-zone buildings. Paper Presented at the intelligent building operations workshop, Boulder, CO, USA.
 
JA Candanedo, AK Athienitis (2011). Predictive control of radiant floor heating and solar-source heat pump operation in a solar house. HVAC&R Research, 17: 235–256.
Crossref Google Scholar
 
A Dhar, TA Reddy, DE Claridge (1994). Improved Fourier series approach to modeling hourly energy use in commercial buildings. In: Proceedings of the 1994 ASME/JSME/JSES International Solar Energy Conference, San Francisco, CA, USA, pp. 455–455.
 
A Dhar, TA Reddy, DE Claridge (1995). A Fourier series approach to predict hourly heating and cooling energy use in commercial buildings with outdoor temperature as the only weather variable. In: Proceedings of the 1995 ASME/JSME/JSES International Solar Energy Conference, Maui, HI, USA, pp. 27–30.
 
A Dhar, TA Reddy, DE Claridge (1999). Generalization of the Fourier series approach to model hourly energy use in commercial buildings. Journal of Solar Energy Engineering, 121: 54–62.
Crossref Google Scholar
 
DOE (2015). DOE-2. Available at http://doe2.com.
 
Z Dong, D Yang, T Reindl, WM Walsh (2013). Short-term solar irradiance forecasting using exponential smoothing state space model. Energy, 55: 1104–1113.
Crossref Google Scholar
 
EnergyPlus (2015). Available at https://energyplus.net.
 
EVO (2015). Available at http://www.evo-world.org/index.php?lang=en.
 
C Fan, F Xiao, Y Zhao (2017). A short-term building cooling load prediction method using deep learning algorithms. Applied Energy, 195: 222–233.
Crossref Google Scholar
 
L Farinaccio, R Zmeureanu (1999). Using a pattern recognition approach to disaggregate the total electricity consumption in a house into the major end-uses. Energy and Buildings, 30: 245–259.
Crossref Google Scholar
 
W Favoreel, B De Moor, P Van Overschee (2000). Subspace state space system identification for industrial processes. Journal of Process Control, 10: 149–155.
Crossref Google Scholar
 
YT Ge, SA Tassou (2011). Performance evaluation and optimal design of supermarket refrigeration systems with supermarket model “SuperSim”, Part I: Model description and validation. International Journal of Refrigeration, 34: 527–539.
Crossref Google Scholar
 
GW Hart (1992). Nonintrusive appliance load monitoring. Proceedings of the IEEE, 80: 1870–1891.
Crossref Google Scholar
 
D Heckerman (1997). Bayesian networks for data mining. Data Mining and Knowledge Discovery, 1: 79–119.
Crossref Google Scholar
 
GP Henze, DE Kalz, S Liu, C Felsmann (2005). Experimental analysis of model-based predictive optimal control for active and passive building thermal storage inventory. HVAC&R Research, 11: 189–213.
Crossref Google Scholar
 
M Jansson (2003). Subspace identification and ARX modeling. In: Proceedings of the 13th IFAC Symposium on System Identification (SYSID), Rotterdam, The Netherlands.
Crossref
 
FV Jensen (2001). Bayesian Networks and Decision Graphs. New York: Springer.
Crossref
 
Y Ji, P Xu, Y Ye (2015). HVAC terminal hourly end-use disaggregation in commercial buildings with Fourier series model. Energy and Buildings, 97: 33–46.
Crossref Google Scholar
 
H Karlsson, CE Hagentoft (2011). Application of model based predictive control for water-based floor heating in low energy residential buildings. Building and Environment, 46: 556–569.
Crossref Google Scholar
 
A Kusiak, M Li, F Tang (2010a). Modeling and optimization of HVAC energy consumption. Applied Energy, 87: 3092–3102.
Crossref Google Scholar
 
A Kusiak, M Li, Z Zhang (2010b). A data-driven approach for steam load prediction in buildings. Applied Energy, 87: 925–933.
Crossref Google Scholar
 
C Laughman, K Lee, R Cox, S Shaw, S Leeb, L Norford, P Armstrong (2003). Power signature analysis. IEEE Power and Energy Magazine, 1: 56–63.
Crossref Google Scholar
 
S Laur (2009). Bayesian networks with continuous distributions. Available at https://courses.cs.ut.ee/2009/bayesian-networks/ bayesian-networks-with-continious-distributions.pdf.
 
J Liang, SK Ng, G Kendall, JW Cheng (2010a). Load signature study— Part I: Basic concept, structure, and methodology. IEEE Transactions on Power Delivery, 25: 551–560.
Crossref Google Scholar
 
J Liang, SKK Ng, G Kendall, JWM Cheng (2010b). Load signature study—Part II: Disaggregation framework, simulation, and applications. IEEE Transactions on Power Delivery, 25: 561–569.
Crossref Google Scholar
 
D Luo, LK Norford, SR Shaw, SB Leeb (2002). Monitoring HVAC equipment electrical loads from a centralized location—Methods and field test results. ASHRAE Transactions, 108(1): 841–857.
Google Scholar
 
J Ma, SJ Qin, B Li, T Salsbury (2011). Economic model predictive control for building energy systems. In: Proceedings of IEEE Innovative Smart Grid Technologies (ISGT), Anaheim, CA, USA.
Crossref
 
L Magnier, F Haghighat (2010). Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network. Building and Environment, 45: 739–746.
Crossref Google Scholar
 
Matlab (2015). Available at http://www.mathworks.com/products/matlab.
 
(2015). Available at https://www.modelica.org.
 
PD Moroşan, R Bourdais, D Dumur, J Buisson (2010). Building temperature regulation using a distributed model predictive control. Energy and Buildings, 42: 1445–1452.
Crossref Google Scholar
 
NS Nise (2007). Control Systems Engineering, 5th edn. Hoboken NJ, USA: John Wiley & Sons.
 
LK Norford, SB Leeb (1996). Non-intrusive electrical load monitoring in commercial buildings based on steady-state and transient load-detection algorithms. Energy and Buildings, 24: 51–64.
Crossref Google Scholar
 
Z O’Neill, X Pang, M Shashanka, P Haves, T Bailey (2014). Model-based real-time whole building energy performance monitoring and diagnostics. Journal of Building Performance Simulation, 7: 83–89.
Crossref Google Scholar
 
Z O’Neill, C O’Neill (2016). Development of a probabilistic graphical model for predicting building energy performance. Applied Energy, 164: 650–658.
Crossref Google Scholar
 
SM Pandit, SM Wu (1983). Time Series and System Analysis with Applications. New York: John Wiley & Sons.
 
MS Spyrou, MJ Cook, J Mardaljevic, J Pitcher, A McMullen, R Lee (2015). The operational efficiency of commercial food refrigeration systems: A data mining approach. Paper presented at ASHRAE Winter Conference, Chicago, USA.
 
A Sretenović, B Živković, R Jovanović (2017). Multiple linear regression, support vector machines and neural networks for prediction of commercial building energy consumption. Zbornik Međunarodnog kongresa o KGH, 46: 383–393.
Google Scholar
 
J Sun, H Zuo, W Wang, MG Pecht (2012). Application of a state space modeling technique to system prognostics based on a health index for condition-based maintenance. Mechanical Systems and Signal Processing, 28: 585–596.
Crossref Google Scholar
 
TRNSYS (2015). Available at http://sel.me.wisc.edu/trnsys.
 
Z Wang, Y Wang, RS Srinivasan (2018). A novel ensemble learning approach to support building energy use prediction. Energy and Buildings, 159: 109–122.
Crossref Google Scholar
 
WattNode (2015). Available at http://www.ccontrolsys.com/w/ WattNode_Pulse.
 
K Yun, R Luck, PJ Mago, H Cho (2012). Building hourly thermal load prediction using an indexed ARX model. Energy and Buildings, 54: 225–233.
Crossref Google Scholar
 
H Yoshida, S Kumar (2001). Development of ARX model based off-line FDD technique for energy efficient buildings. Renewable Energy, 22: 53–59.
Crossref Google Scholar
 
J Zhang, F Haghighat (2010). Development of Artificial Neural Network based heat convection algorithm for thermal simulation of large rectangular cross-sectional area Earth-to-Air Heat Exchangers. Energy and Buildings, 42: 435–440.
Crossref Google Scholar
 
Y Zhang, Z ONeill, T Wagner, G Augenbroe (2013). An inverse model with uncertainty quantification to estimate the energy performance of an office building. In: Proceedings of the 13th International IBPSA Building Simulation Conference, Chambéry, France.
 
Y Zhang, Z O’Neill, B Dong, G Augenbroe (2015). Comparisons of inverse modeling approaches for predicting building energy performance. Building and Environment, 86: 177–190.
Crossref Google Scholar
 
HX Zhao, F Magoulès (2012). A review on the prediction of building energy consumption. Renewable and Sustainable Energy Reviews, 16: 3586–3592.
Crossref Google Scholar
Building Simulation
Volume 11 Issue 4,
August 2018
Pages 633-645
DOI: 10.1007/s12273-018-0431-2
Cite this article:
Niu F, O’Neill Z, O’Neill C. Data-driven based estimation of HVAC energy consumption using an improved Fourier series decomposition in buildings. Building Simulation, 2018, 11(4): 633-645. https://doi.org/10.1007/s12273-018-0431-2

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Received: 23 September 2017
Revised: 14 December 2017
Accepted: 02 January 2018
Published: 24 January 2018
© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018
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