Final Report: A Climate-Responsive Adaptive Control for a Combination Passive Solar Shading and Natural Ventilation

EPA Grant Number: SU835520
Title: A Climate-Responsive Adaptive Control for a Combination Passive Solar Shading and Natural Ventilation
Investigators: Baur, Stuart W. , Annis, Nicole , Elder, Melody , Jensby, Danielle , Jensby, Joshua
Institution: University of Missouri - Rolla
EPA Project Officer: Hahn, Intaek
Phase: I
Project Period: August 15, 2013 through August 14, 2014
Project Amount: $15,000
RFA: P3 Awards: A National Student Design Competition for Sustainability Focusing on People, Prosperity and the Planet (2013) RFA Text |  Recipients Lists
Research Category: P3 Challenge Area - Built Environment , P3 Challenge Area - Energy , Pollution Prevention/Sustainable Development , P3 Awards , Sustainability


The purpose of this project is the development of control models capable of forecasting indoor thermal comfort, and the application of these models to enable passive effective strategies for such as natural ventilation and daylighting. The objective of this research is to develop a component of smart technology that will use passive heating and cooling strategies to ultimately reduce energy consumption. Based on a prior study in 2011 that included the use of multiple sensors including temperature, relative humidity and mean radiant temperature sensors were installed in the interior spaces of the Missouri S&T Solar House. A weather station was mounted on the exterior of the house. On the interior of the home a series of interior sensors were installed to measure the temperature, humidity and daylighting. These sensory devices continuously recorded both indoor and outdoor climate parameters. In a previous study these parameters were used to develop a prediction model capable of accurately forecasting predicted mean vote (PMV), a measure of thermal comfort. Using this model, control logic was developed to actuate the opening of windows to facilitate natural ventilation, thus maintaining indoor comfort while preventing over-cooling or over-heating.

Per information from the Energy Information Administration, energy consumed by mechanical systems for cooling buildings represents 21% of the total energy use in the United States. The utilization of passive cooling and heating strategies can help to reduce this consumption significantly, and in doing so benefit not only the environment, but also building occupants through optimizing their thermal comfort conditions. To that end, the objective of Phase I of this research was the development of a predictive control system capable of forecasting indoor thermal comfort and proactively taking measures to maintain comfort via the use of passive strategies.

Summary/Accomplishments (Outputs/Outcomes):

Two goals of phase I were to build upon previous research integrating the use of natural ventilation and daylighting strategies to develop a predictive response based on a computer simulated model, and to further develop the neural network model. The simulated model provided a basis that incorporated contributions of daylighting strategies with natural ventilation. As part of this ongoing study the monitoring systems have been placed in the actual home to compare the predictive model to the accuracy of the simulated model. The neural network model that was developed in the previous study had the added benefit of being capable of continuously adapting to its environment. By connecting the sensors and actuators to a central computer, the new model was used to automate the opening and closing of windows within the test structure. Based on the simulated model this system is capable of producing a comfortable indoor environment reducing cool energy by 68%, while maintaining a thermal comfort condition.


The proposed system has proven effective at reducing energy consumption, with an estimated annual reduction in energy of 68%. Based on the research conducted the use of a climatic monitoring sensors allows the system to continuously adapt its prediction to current weather conditions. However, the current sensory model is limited in use as a thermal system for open floor plan residences. In order for such a system to be of greater benefit across the country, additional research is required to incorporate daylighting aspects into the prediction, as well as different layouts in homes and building materials.

Journal Articles:

No journal articles submitted with this report: View all 3 publications for this project

Supplemental Keywords:

Predictive control; user-friendly web interface; remote control; human behaviors; behavioral guidance; energy efficiency; passive systems; indoor building environments.

Relevant Websites:

Phase I of this project has created a web-interface which enables a user to monitor building indoor and outdoor climate conditions and real time energy benefits. The web-interface is available at Exit . Project details are available on the first page of the website.