Ensemble Learning Model-Based Test Workbench for the Optimization of Building Energy Performance and Occupant Comfort
Buildings consume tremendous energy for the improvement of living and working conditions. 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...
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doaj-c4e8e81de01247338e871554911769172021-03-30T02:16:58ZengIEEEIEEE Access2169-35362020-01-018960759608710.1109/ACCESS.2020.29965469097865Ensemble Learning Model-Based Test Workbench for the Optimization of Building Energy Performance and Occupant ComfortSanjeev Kumar T.M.0Ciji Pearl Kurian1https://orcid.org/0000-0002-5222-3041Susan G. Varghese2Department of Electrical and Electronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal, IndiaDepartment of Electrical and Electronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal, IndiaDepartment of Electrical and Electronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal, IndiaBuildings consume tremendous energy for the improvement of living and working conditions. 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 dynamic range image with EVALGLARE software used to verify the visual comfort based on daylight glare 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.https://ieeexplore.ieee.org/document/9097865/Window blind controldata-driven modelsensemble learningbayesian optimizationdaylight glarelabview |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sanjeev Kumar T.M. Ciji Pearl Kurian Susan G. Varghese |
spellingShingle |
Sanjeev Kumar T.M. Ciji Pearl Kurian Susan G. Varghese Ensemble Learning Model-Based Test Workbench for the Optimization of Building Energy Performance and Occupant Comfort IEEE Access Window blind control data-driven models ensemble learning bayesian optimization daylight glare labview |
author_facet |
Sanjeev Kumar T.M. Ciji Pearl Kurian Susan G. Varghese |
author_sort |
Sanjeev Kumar T.M. |
title |
Ensemble Learning Model-Based Test Workbench for the Optimization of Building Energy Performance and Occupant Comfort |
title_short |
Ensemble Learning Model-Based Test Workbench for the Optimization of Building Energy Performance and Occupant Comfort |
title_full |
Ensemble Learning Model-Based Test Workbench for the Optimization of Building Energy Performance and Occupant Comfort |
title_fullStr |
Ensemble Learning Model-Based Test Workbench for the Optimization of Building Energy Performance and Occupant Comfort |
title_full_unstemmed |
Ensemble Learning Model-Based Test Workbench for the Optimization of Building Energy Performance and Occupant Comfort |
title_sort |
ensemble learning model-based test workbench for the optimization of building energy performance and occupant comfort |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
Buildings consume tremendous energy for the improvement of living and working conditions. 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 dynamic range image with EVALGLARE software used to verify the visual comfort based on daylight glare 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. |
topic |
Window blind control data-driven models ensemble learning bayesian optimization daylight glare labview |
url |
https://ieeexplore.ieee.org/document/9097865/ |
work_keys_str_mv |
AT sanjeevkumartm ensemblelearningmodelbasedtestworkbenchfortheoptimizationofbuildingenergyperformanceandoccupantcomfort AT cijipearlkurian ensemblelearningmodelbasedtestworkbenchfortheoptimizationofbuildingenergyperformanceandoccupantcomfort AT susangvarghese ensemblelearningmodelbasedtestworkbenchfortheoptimizationofbuildingenergyperformanceandoccupantcomfort |
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