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.