Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Data-driven based estimation of HVAC energy consumption using an improved Fourier series decomposition in buildings
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.
Data-driven based estimation of HVAC energy consumption using an improved Fourier series decomposition in buildings
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.
Data-driven based estimation of HVAC energy consumption using an improved Fourier series decomposition in buildings
Niu, Fuxin (Autor:in) / O’Neill, Zheng (Autor:in) / O’Neill, Charles (Autor:in)
Building Simulation ; 11 ; 633-645
24.01.2018
13 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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