Grantee Research Project Results

Final Report: Climate-Responsive Adaptive Controls for Natural Ventilation

EPA Grant Number: SU835073
Title: Climate-Responsive Adaptive Controls for Natural Ventilation
Investigators: Choi, Joon-Ho , Baur, Stuart W. , Smith, Annelise , Shen, Chou , Holt, Dennis , Laughery, Lucas , Klover, Sean
Institution: Missouri University of Science and Technology
EPA Project Officer: Hahn, Intaek
Phase: I
Project Period: August 15, 2011 through August 14, 2012
Project Amount: $15,000
RFA: P3 Awards: A National Student Design Competition for Sustainability Focusing on People, Prosperity and the Planet (2011) RFA Text |  Recipients Lists
Research Category: Pollution Prevention/Sustainable Development , P3 Awards , P3 Challenge Area - Air Quality , P3 Challenge Area - Sustainable and Healthy Communities , Sustainable and Healthy Communities

Objective:

The purpose of this project is the development of control models capable of forecasting indoor thermal comfort, and the application of these models to enabling passive strategies such as natural ventilation. The objective of this is to reduce energy consumption by mechanical systems by instead making use of passive strategies. In Phase I of this project, multiple sensors including air temperature, relative humidity, air velocity, and mean radiant temperature sensors were installed in the Missouri S&T 2009 Solar Decathlon house, and a weather station was mounted on the exterior of the house. These sensory devices continuously recorded both indoor and outdoor climate parameters. 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. The use of such an advanced control system is estimated to reduce cooling system energy consumption by 68%.

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 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 is the development of a predictive control system that is capable of forecasting indoor thermal comfort and proactively taking measures to maintain comfort via the use of passive strategies.

Summary/Accomplishments (Outputs/Outcomes):

Two models were investigated for the prediction of PMV: a neural network (one of artificial intelligence logic), and a statistical regression model. These models were both developed based on closed window conditions. The neural network model proved to be the most accurate in predicting PMV, and moreover had the added benefit of being capable of continuously adapting to its environment. After gathering data for various open window conditions, this neural network model was expanded to include window opening level as a prediction parameter. By connecting actuators to a central computer, the new model was used to automate the opening and closing of windows within the test structure. This system is capable of producing a comfortable indoor environment at considerably reduced cooling energy by 68%, while maintaining a thermal comfort condition.

Conclusions:

Currently, the developed system has proven effective at reducing energy consumption, with an estimated annual reduction in energy of 68%. The use of a neural network rather than a statistical regression model allows the system to continuously adapt its prediction to reflect real conditions with a predicted accuracy of 93% on average, while the statistical regression model provides a predicted accuracy of less than 60%. Since the developed control logic has a functional feature of self-learning and self-adaptation to various indoor and outdoor climate conditions, this is a very useful feature for the widespread implementation of such a system. However, the current model is limited to use as a cooling 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 heating and lighting components into the prediction, as well as different layouts and building materials.

Journal Articles:

No journal articles submitted with this report: View all 5 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