Abstract
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.
Highlights
IN today’s world, most people spend over 80 - 90% of their time in indoors [1], [2]
This work develops the standard machine learning models with Bayesian hyperparameter optimization technique [55]–[60] to adjust the blind position concerning the external daylight on the window (EDW), temperature difference (TDIFF) and the solar altitude (ALT), for any window orientation based on the time and day of the year [31], [33]
The accuracy of the proposed model was calculated based on the conventional statistical indicators (RMSE, MAPE and R2)
Summary
IN today’s world, most people spend over 80 - 90% of their time in indoors [1], [2]. Buildings consume more than 80% of energy through lighting and air-conditioning systems to provide a functional and comfortable indoor environment to the occupants [3]–[5]. The rule-based window blind control techniques [27], [29] have importance to the daylight-artificial light integration scheme to keep the comfort level to the occupants in the building during the retrofitting stage. In their initial proposed [37] machine learning-based predictive model for the daylight-artificial light integration scheme, developed four different blind control models for four sides of windows. We are working on daylight – artificial light integrated scheme with control of LED luminaire and motorized Venetian blinds for improving the energy efficiency of the building and comfort of the occupants. Proposed an intelligent data-driven based technique to develop a generalized novel control strategy for windowblind-positioning of all the four-window orientation for the daylight-artificial light integration scheme. The glare control strategy is validated using the DGP metric, making use of an HDR image-based system
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