An Evaluation of Model Predictive Control of Automated Shading to Optimize Passive Solar Gains

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Creator: 

Lawal, Nuriat Tiwatayo

Date: 

2015

Abstract: 

Passive houses are prone to overheating, even sometimes in the winter. Model predictive control (MPC) of automated window blinds has proved to be effective at managing solar gains to reduce the thermal loads in buildings. MPC involves predicting a system's future response to several control inputs to determine the best current control decision. This is beneficial in buildings, which experience a delayed thermal response to solar gains.

This thesis details a framework for MPC of automated blinds to optimize passive solar gains in a single-family home. Building performance simulations (BPS) were used to predict the interactions between the blind positions and the heating and cooling loads. Optimizations were performed to minimize the combined heating and cooling loads. The MPC's performance was simulated and compared to a rule-based controller (RBC), which is the standard practice in blind automation. Energy savings of up to 36% were recorded in comparison to the RBC.

Subject: 

Engineering - Mechanical

Language: 

English

Publisher: 

Carleton University

Contributor: 

Figure contributor: 
Joseph A. Clarke

Thesis Degree Name: 

Master of Applied Science: 
M.App.Sc.

Thesis Degree Level: 

Master's

Thesis Degree Discipline: 

Engineering, Sustainable Energy

Parent Collection: 

Theses and Dissertations

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