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.