Data-fusion for increasing temporal resolution of building energy management system data

Buildings are known to be significant energy consumers throughout the world. Thus, improving the energy efficiency of buildings is a key research goal. However, maintaining occupant comfort while improving energy efficiency in buildings requires close monitoring of the building environment and immediate control actions taken when sub-optimal behavior is identified. Such monitoring requires high frequency data from sensors. Therefore, increasing the data collection rate or the temporal resolution of sensors can lead to improved building control and state-awareness. This paper presents an on-line learning, data-fusion based methodology that uses Artificial Neural Networks (ANNs) to increase temporal resolution of building sensor data. The presented method utilizes sensor information from different sensors in the building to predict higher temporal resolution data of specific sensors. Furthermore, the presented method is capable of learning changing building behavior for improved long-term accuracy. The presented method was applied to a real-world building dataset and was shown to be able to predict high temporal resolution data with a higher accuracy compared to classical methods. Furthermore, the on-line learning was shown to increase the prediction accuracy in long-term operation.