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Building Simulation

, Volume 11, Issue 4, pp 633–645 | Cite as

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

  • Fuxin Niu
  • Zheng O’Neill
  • Charles O’Neill
Research Article Building Thermal, Lighting, and Acoustics Modeling

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-driven decomposition Fourier series HVAC energy estimation 

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Copyright information

© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringThe University of AlabamaTuscaloosaUSA
  2. 2.Department of Aerospace and Mechanics EngineeringThe University of AlabamaTuscaloosaUSA

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