Ensemble Learning Model-Based Test Workbench for the Optimization of Building Energy Performance and Occupant Comfort

Kumar, Sanjeev T M and Kurian, Ciji Pearl and Varghese, Susan G (2020) Ensemble Learning Model-Based Test Workbench for the Optimization of Building Energy Performance and Occupant Comfort. IEEE Access, 8. ISSN 2169-3536

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Buildingsconsumetremendousenergyfortheimprovementoflivingandworkingconditions. Control of daylight-artificial light has the potential to improve energy performance and occupant comfort in buildings. This research proposes an intelligent generalized ensemble learning technique to develop a novel control strategy for Venetian-blind positioning (up-down movement with static slat angle of 45◦) of different window orientations. The proposed model helps to maintain occupant comfort and energy saving in a commercial building. The performance of the ensemble learning approach compared against Gaussian process regression, support vector regression and artificial neural network using conventional statistical indicators. Finally, the proposed data-driven model implemented in a real-time Labview-myRIO platform for the experimental validation. The data-driven model is compared with the baseline model and with the uncontrolled blind condition in terms of daylight glare, and energy consumption of lighting and air-conditioning system in the building. The data-driven model is derived using two years of data collected from a fuzzy-based daylight-artificial light integrated scheme. The blind position providing reduced energy consumption and daylight glare along with setpoint illuminance and temperature are validated. A high dynamicrangeimagewithEVALGLAREsoftwareusedtoverifythevisualcomfortbasedondaylightglare probability. While evaluating the overall energy savings, the ensemble learning model consumes 17% less power than the uncontrolled system and 15% less power than the baseline system. Here, though we are not controlling the air-conditioning system, the experimental validation confirmed that the air-conditioning system significantly reduces its energy consumption

Item Type: Article
Uncontrolled Keywords: Window blind control, data-driven models, ensemble learning, bayesian optimization, daylight glare, labview, myRIO, energy comparison, lighting control, air-conditioning
Subjects: Engineering > MIT Manipal > Electrical and Electronics
Depositing User: MIT Library
Date Deposited: 03 Sep 2020 05:03
Last Modified: 03 Sep 2020 05:03
URI: http://eprints.manipal.edu/id/eprint/155587

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