Proceedings of the 6th Annual ISC Graduate Research Symposium ISC-GRS 2012 April 13, 2012, Rolla, Missouri SMART CONTROL FOR NATURAL VENTILATION HOUSE ABSTRACT A series of investigations has been conducted in the 2009 Solar House located on the Missouri University of Science and Technology campus, to develop a climate-response control for a natural ventilation system. Firstly, the research investigated the relationship between indoor thermal comfort and climate. Predicted Mean Vote (PMV) was selected to evaluate the thermal comfort, which was calculated based on parameters of environment and occupant characteristics. The climate was monitored by a weather station. Then, the PMV Prediction model was built, by which the PMV in a forecast time of 10 minutes could be estimated based on current indoor environment and outdoor climate. At last, the adaptive control was developed when combining the data acquisition system, PMV Prediction model and window actuators. At the same time, a building model was built up, and the simulation indicates that an energy conservation rate of 67.8% can be achieved when applying natural ventilation properly. 1. INTRODUCTION According to the U.S. Green Building Council, in 2010 buildings accounted for 72% electricity consumption in the United States. Of this energy, 34% is consumed by heating systems, 15% by cooling systems, and 4% by ventilation systems [1]. As a result, it will take huge advantage to implement passive strategies such as natural ventilation for cooling. Conventionally, the control of a natural ventilation or air conditioning system for residence is based on indoor temperature, by the usage of a thermostat. This method works but not so comfortable and environment-friendly. First of all, this method is an oversimplification. It ignores many other parameters associated with indoor environment, such as wind speed and relative humidity. Then it only reacts to current indoor temperature, which may cause the problem of overcooling and overheating, because of the time lag between indoor environment and exterior climate. Some researches try to predict indoor environment based on outdoor climate, but still air temperature is the only variable taken to judge the thermal comfort [2-4]. The goal of this research is to develop a climateresponse control for a natural ventilation system, which can predict indoor thermal comfort and determine the appropriate window opening level needed to provide passive cooling. This predictive control is able to minimize the use of a mechanical cooling system while preventing the over-cooling condition. In this research, PMV index is used to reflect the thermal comfort, which is associated with different indoor environment parameters rather than temperature merely. Fig. 1. The Solar Decathlon House of Missouri S&T Fig. 2. The plan of the Testing Room 1
2. DATA ACQUISITION Figure 1. shows the Solar House 2009 in Missouri S&T. It was built to easily get passively heated from solar radiation and cooled by cross ventilation. The testing room (Fig. 2) was the living room which had three large double hung windows (2-9 6-6 ) in the south and four small awning windows (2-9 1-6 ) in the north side. PMV as a universal method to evaluate indoor thermal comfort has been broadly used since first published by Fanger [5]. According to Fanger s theory, there are six parameters of environment and occupant characteristics associated with the PMV index. They are air temperature, relative humidity, air velocity, mean radiant temperature, clothes insulation and occupant activity. It is necessary to monitor all these six parameters to estimate PMV value. Fig. 3. Image of sensor pole and attached sensors Considering the function of objective building, the occupant activity and clothes insulation are almost constant. Four of the environment parameters were measured by two indoor sensor poles (marked on Fig. 2.), attached with sensors to record temperature, wind speed, mean radiation temperature, relative humidity and CO 2 concentration separately (Fig. 3.). In addition to that, several wind sensors were attached on the windows, which were to measure the air flow through the window during the natural ventilation process. The outdoor climate parameters were monitored by a mini weather station installed on the roof of the house. The weather station could record outdoor temperature, relative humidity, wind direction and speed, as well as solar radiation. All the parameters were recorded per minute to catch the dynamic indoor and outdoor environment. A total of 25205 sets of data have been collected. processes occurring between the building and the environment, such as heat transfer through envelope and air flow through the windows. These models provide equations which determine air temperature, airflow and energy cost. But usually it is hard to include all the physical process, because of the complexity of incorporating all the parameters in different dimensions. Some research provides good solution to wind-driven flows but failing to consider thermal dynamics [6]; while some research predicts indoor temperature as a result of opening window but only considers the temperature difference between indoor and outdoor air [7]. The data-driven models try to determine the relationship between the target and all the inputs without investigating the physical process directly. In this research, two kinds of data-driven models have been implemented. 3.1.Neural Network The Neural Network (NN) is basically a realization of nonlinear mapping from some input to the target [8]. The NN can learn the key relationship between the input and target without handling the actual mathematical or physical model. As a result it is broadly used as a method to deal with complex technique problems involving multidimensional factors. Several researchers have implemented Neural Network to predict indoor environment [9,10]. Kalogirou predicted air flow in a naturally ventilated room with Neural Network [9]. According to Kalogirou, the Back Propagation Neural Network (BPNN) works well in function approximation. In this investigation, BPNN was tested with several structures. The results show that the optimal structure is constituted with 2 hidden layers, with 15 neurons on each layer. 3.2.Regression Analysis Statistical regression seeks to infer the causal relationships between independent and dependent variables [11].Stepwise regression was first examined to determine if all the predictors were relevant to the prediction of PMV. And then the general regression was performed. 3.3.Input and Output The PMV Prediction model was firstly built based on closed window condition. Without the interfering of air flow through the windows, the indoor environment is steadier and it is possible to investigate the baseline of the indoor environment s adaptation to climate. The input and output of PMV Prediction model are tabulated as TABLE 1. 3. PMV PREDICTION MODEL DEVELOPMENT One key objective of this research is to investigate how the climate parameters affect the indoor thermal comfort. There are two kinds of models in literature: physically based models and data-driven models [2]. The former methods investigate directly physical 2
TABLE 1: Input and output for use in developing predictive model for closed conditions. Air temperature ( F) Indoor Air relative humidity (%) Air velocity (ft/min) Mean radiant temperature ( F) Input Air temperature ( F) Air relative humidity (%) Outdoor Air velocity & direction (mph & degrees) Solar radiation Output PMV* at time t, t+10, t+20, t+30, t+40, t+50, t+60 * PMV at time t represents its current value, and time t+10 represents its predicted value at a forecast time of 10 minutes. Fig. 4. Neural Network PMV prediction accuracy versus forecast time. It was stated that the window open area and how long the window position lasts will affect the indoor environment [12]. As a result, in the open window condition, two extra variables were added as inputs. One was the percentage of window open area, defined as Window Function in this research, and the other was the time by minute the Window Function lasted. The Window Function had five levels (0%, 25%, 50%, 75%, 100%). 4. DATA ANALYSIS AND RESULTS 4.1.Comparison of Regression Analysis Model and Neural Network Model The closed condition and open condition were treated separately, and each situation considered both Regression Analysis and Neural Network, respectively. The R 2 value (square of correlated coefficient) was used to indicate how well the prediction of PMV matched the real value. 4.1.1.Closed Condition At closed condition, the data were divided into subsets based on weather conditions. When relative humidity was below 80%, the weather was regarded as sunny, or else, the weather was regarded as cloudy. At the same time, the data acquired before 5:30 pm were labeled as day time, or else, were labeled night time. In that way the data were divided into four groups. Figure 4-5. shows the R 2 value of Neuron Network model and Regression Analysis, respectively. It indicates that for all the four groups, the R 2 values at different forecast times by Neural Network are much better than those by Regression Analysis. For most configurations of forecast time and weather condition, the R 2 values by Neural Network model is above 80%, while the R 2 values by Neuron Network model is much lower than 80%. Fig. 5. Regression analysis PMV prediction accuracy versus forecast time. 4.1.2.Open Condition For the open condition, the data patterns were categorized by window opening level rather than by climate conditions, and only 5 minutes and 10 minutes were considered rather than the 8 levels of forecast time that were considered in the closed condition, because the PMV prediction model only needed to predict the PMV in a forecast time of 5 and 10 minutes. Fig. 6. indicates that the accuracy of Neural Network model is considerably better than that of statistical regression model, especially for the prediction at a forecast time of 10 minutes. At the forecast time of 10 minutes, the Neural Network model can provide the accuracy of 93% on average, while the Regression analysis model provides a much lower accuracy of 60%. 3
Fig. 6. Comparison of accuracy of NN model and Regression model versus window opening level. The control logic consists two parts, the first part is the PMV prediction, and then is to give the command of window function and activate the actuators attached on the windows. The control logic diagram is shown as Fig. 7. At first the PMV Prediction model is built based on the data base, and then PMV of a forecast time of 10 minutes is predicted with the model and current indoor environment and exterior climate. The third step is to produce the Window Function 1, which can be +25%, -25% or No change, depending on the PMV prediction. About 5 minutes after the Window Function 1 performs, PMV Prediction model runs again. The purpose for the 2 nd prediction is to make sure if the tendency of thermal comfort is in demand. Then Window Function 2 is produced based on the 2 nd prediction. In that way, the control logic has two chances to adjust the windows in 10 minutes. 4.2.Window control logic development Fig. 7. Window actuator control logic diagram. 4.3.Energy performance by Natural Ventilation According to the Comfort Model for Natural Ventilation provided by ASHRAE 55-2010 [13], it is possible to keep thermal comfort with natural ventilation, when the outdoor temperature is in the range of 65~80 o F. The Missouri S&T Solar House 2009 lies in the city of Rolla, MO, which is in Climate Zone A4. The outdoor temperature (TMY2) in typical days in warm seasons is shown in Fig. 8. Figure 8. indicates that in swing season, like May and September, the outdoor temperature is below 80 o F nearly 4
all the time; in June and August, the temperature is below 80 o F before the noon; even in the hottest month July, the temperature is below 80 o F before 10 am. In general, Fig. 8. shows great chance for using natural ventilation, especially in swing season and the morning in summer time. Fig. 8. Typical days in warm season. To estimate monthly and yearly energy conservation by natural ventilation, a building energy simulation was conducted by equest software [14]. Figure 9. shows the possible cooling energy reduction through the use of a combination of mechanical and natural ventilation system. The simulation results indicate significant energy savings to be achieved by proper usage of natural ventilation, particularly between April and October. Using natural ventilation, the annual electrical consumption for cooling is only 502kWh, much smaller than the counterpart of baseline, 1558kWh. About 67.8% of electricity consumption has been saved over the course of a typical year. Fig. 9. Potential reduction in cooling energy consumption using a combined system in the house 5. CONCLUSION In this research, a sort of climate-response control for natural ventilation system has been developed. This control logic consists of data acquisition, PMV Prediction model and Window Function producing. The control was realized by a computer linking all the indoor sensors, weather station and window actuators. The use of a Back Propagation Neural Network works well to predict the PMV. At a forecast time of 10 minutes the BPNN can predict PMV with the accuracy of 93% on average, while the Regression analysis model provides a much lower accuracy of 60%. For this PMV Prediction model, the optimal structure is to have 2 hidden layers, with 15 neurons on each. The simulation results indicate that significant energy savings can be achieved by proper usage of natural ventilation. Ideally about 67.8% of electricity consumption can be saved over the course of a typical year. 6. ACKNOWLEDGMENTS The author gratefully acknowledges the financial support provided by the U.S. Environmental Protection Agency, the support of the Intelligent Systems Center, the Missouri S&T Department of Civil, Environmental and Architectural Engineering, the assist from Office of Sustainable Energy & Environmental Engagement in Missouri S&T, the supervision from the advisor and hard work from every student engaged in this project. 7. REFERENCES [1] Green Building Research. U.S. Green Building Council (USGBC). Available at http://www.usgbc.org/displaypage.aspx?cmspageid =1718 (accessed on Feb. 10, 2010). [2] Spindler, H.C., Norford, H.K., 2009, Naturally ventilated and mixed-mode buildings Part II: Optimal control, Building and Environment 44, pp. 750 761. [3] Eftekhari, M., Marjanovic, L., Angelov, P., 2003, Design and performance of a rule-based controller in a naturally ventilated room, Computers in Industry 51, pp. 299 326. [4] Heiselberg, P., Perino, M., 2010, Short-term airing by natural ventilation implication on IAQ and thermal comfort, Indoor Air 20, pp. 126 140. [5] P. O. Fanger, 1970, Thermal Comfort, Danish Technical Press. [6] Li, Y., Delsante, A., Chen, Z., Sandberg, M., Andersen, A., Bjerre, M., Heiselberg, P., 2001, Some examples of solution multiplicity in natural ventilation, Building and Environment 36:851 8. [7] Carillho, da., Linden, PF. and Haves, P., 2003, Design and testing of a control strategy for a large, 5
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