Planning and Monitoring of Building Energy Demands under Uncertainties by Using IoT Data

Building energy models have under predicted actual electricity consumption by about 30%. These models are typically based on physical attributes of an actual building and simulate its operation at static indoor conditions. The attributes of building designs and systems may not represent accurate properties of the building because the data becomes available only as architectural and engineering design progresses. The lack of building data details creates uncertainties in the model. Internet of things (IoT) data enable us to obtain reliable building operational data. This research addresses a major challenge for using IoT data during a building energy prediction and operation. IoT data currently pose two major challenges: 1) IoT data has been generated in existing buildings, but it is often unused, and 2) uncertainties in building energy predictions have resulted in unreliability of the simulation results. The research objective is to present a methodology of IoT-initiated building energy modeling and monitoring to increase the reliability of energy predictions and energy management. The uncertainty-integrated energy modeling enables for reliable energy planning prior to building construction, and for resilient monitoring during post construction and facility operation.