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Development of an Gaussian Process Model using a Data Filtering Method
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 Title & Authors
Development of an Gaussian Process Model using a Data Filtering Method
Ahn, Ki Uhn; Kim, Deuk-Woo; Kim, Young-Jin; Park, Cheol Soo;
For better energy management of existing buildings, an accurate and fast prediction model is required. For this purpose, this study reports the development of a GP (Gaussian Process) model for an AHU fan of the real high-rise office building. The GP Model is a statistical data driven model, and requires far less inputs and demands less computing time than the whole building simulation tools. In this paper, the following is addressed: 1) the characteristics of the GP model, 2) the development the GP model, and 3)removal of outliers gathered from BEMS data, 4) validation of the GP model. In particular, RANSAC (RANdom SAmple Consensus) was employed for detecting the outliers of the measured data. It is concluded that the GP model accurately predict the fan energy consumption, and can be used for real time optimal control and fault detection of building systems in near future.
Gaussian Process;Data Filtering;RANSAC;Building Energy Management System (BEMS);
 Cited by
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