In order to reduce energy consumption throughout the whole life-cycle of buildings and increase the comfort performance of building sectors, an energy-harvesting building facade is explored by integrating a self-powered wireless sensing unit into the building facade. The demo self-powered wireless sensing unit presented is specially designed for a unitized window system, which is placed and sealed inside the window section and extracts energy from the ambient environment for its own power supply. By taking advantage of the excellent thermal break performance of modern window systems, ambient thermal energy from the temperature gradient across building facades is harvested via thermal electric generator. The harvested energy will be stored in rechargeable batteries and managed by an integrated circuit to power up sensors and wireless communication devices to transfer sensed data to the data server of the network, which will inform a building information system (BIS) for the optimal control of building environment.
The buildings sector is the largest consumer of electricity. It was reported that the buildings sector accounts for about 76% of electricity use and 40% of all U.S. primary energy
The architecture of a TPWSN is schematically drawn in Figure 1, with two major units: the energy harvesting and the wireless sensor network. The data from the TPWSN node will
Generally, the thermal insulation is the key to quantify the performance of a window/façade system. The thermal bridging and infiltration losses shall be minimized as much as possible, to
The energy harvested from the environment is majorly consumed via the wireless sensor network (WSN) unit, which is responsible for ambient environment monitoring, initial data processing, and wireless communication to
The testing configuration is demonstrated in Figure 8. The bottom of the window frame is immersed into the ice water for the cooling and the top is attached with a
A self-powered wireless sensor network unit has been designed and tested inside a window frame, to provide a platform for the application of different sensors and processors for smart window
The authors thank the technical support from Prof. Yi-Chung Chen from the Department of Electrical and Computer Engineering, Tennessee State University.
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