Ensemble Learning Model-Based Test Workbench for the Optimization of Building Energy Performance and Occupant Comfort
Ensemble Learning Model-Based Test Workbench for the Optimization of Building Energy Performance and Occupant Comfort
Blog Article
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 puffy spa headband with the uncontrolled blind condition in here 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.