MACHINE LEARNING TECHNIQUES APPLIED TO SENSOR DATA CORRECTION IN BUILDING TECHNOLOGIES

Since commercial and residential buildings account for nearly half of the United States' energy consumption, making them more energy-efficient is a vital part of the nation's overall energy strategy. Sensors play an important role in this research by collecting data needed to analyze performance of components, systems, and whole-buildings. Given this reliance on sensors, ensuring that sensor data are valid is a crucial problem. The solution we are researching is machine learning techniques, namely: artificial neural networks and Bayesian Networks. Types of data investigated in this study are: (1) temperature; (2) humidity; (3) refrigerator energy consumption; (4) heat pump liquid pressure; and (5) water flow. These data are taken from Oak Ridge National Laboratory's (ORNL) ZEBRAlliance research project which is composed of four single-family homes in Oak Ridge, TN. Results show that for the temperature, humidity, pressure, and flow sensors, data can mostly be predicted with root-mean-square error of less than 10% of the respective sensor's mean value. Results for the energy sensor were not as good; root-mean-square errors were centered about 100% of the mean value and were often well above 200%. Bayesian networks had smaller errors, but took substantially longer to train.