Data-driven based estimation of HVAC energy consumption using an improved Fourier series decomposition in buildings
data-driven, decomposition, Fourier series, HVAC energy estimation
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.
Tsinghua University Press
Fuxin Niu, Zheng O’Neill, Charles O’Neill. Data-driven based estimation of HVAC energy consumption using an improved Fourier series decomposition in buildings. Build Simul, 2018, 11(4): 633–645.