TY - JOUR

T1 - Development of an organic photovoltaic energy harvesting system for wireless sensor networks; application to autonomous building information management systems and optimisation of OPV module sizes for future applications

AU - Zhang, Shoushou

AU - Bristow, Noel

AU - David, Tudur Wyn

AU - Elliott, Fergus

AU - O'Mahony, Joe

AU - Kettle, Jeff

PY - 2022/3/1

Y1 - 2022/3/1

N2 - The emergence of internet of things (IoT) has motivated research into developing Organic Photovoltaic (OPV) devices that can efficiently convert indoor light into electricity. In this work, the performance and operation of an OPV-powered Wireless Sensor network (WSN) for Building Information management system is provided through a case study. Results are shown for the operation of the WSN and how data can be acquired to build machine learning algorithms that can forecast the indoor conditions of a building, when the system is linked to an external weather station. Remarkably, our data indicates only minor degradation of the OPV when tested under indoor conditions over a 21-month period; at a luminance level of 1000 Lux, only a −10% relative drop in performance was measured. Finally, the field data is used to optimise the size of the OPV and battery for future indoor applications which possess different energy loads. Based on the energy efficiency model, the loss of power supply probability (LPSP) of the indoor applications system is calculated for different size combinations of PV, battery sizes and load energies. This model provides a method to calculate the required OPV output power to ensure remote operation of other IoT electronics.

AB - The emergence of internet of things (IoT) has motivated research into developing Organic Photovoltaic (OPV) devices that can efficiently convert indoor light into electricity. In this work, the performance and operation of an OPV-powered Wireless Sensor network (WSN) for Building Information management system is provided through a case study. Results are shown for the operation of the WSN and how data can be acquired to build machine learning algorithms that can forecast the indoor conditions of a building, when the system is linked to an external weather station. Remarkably, our data indicates only minor degradation of the OPV when tested under indoor conditions over a 21-month period; at a luminance level of 1000 Lux, only a −10% relative drop in performance was measured. Finally, the field data is used to optimise the size of the OPV and battery for future indoor applications which possess different energy loads. Based on the energy efficiency model, the loss of power supply probability (LPSP) of the indoor applications system is calculated for different size combinations of PV, battery sizes and load energies. This model provides a method to calculate the required OPV output power to ensure remote operation of other IoT electronics.

KW - Organic photovoltaics (OPV)

KW - Energy harvesting

KW - Wireless sensor network

KW - Internet of things (IoT)

KW - Forecasting

KW - Machine learning

U2 - https://doi.org/10.1016/j.solmat.2021.111550

DO - https://doi.org/10.1016/j.solmat.2021.111550

M3 - Article

VL - 236

JO - Solar Energy Materials and Solar Cells

JF - Solar Energy Materials and Solar Cells

SN - 0927-0248

M1 - 111550

ER -