Light Intensity and Temperature Parameters Study for Solar-powered Internet of Things to Improve Photovoltaic Energy Harvest

Chew, Kim Mey and Syvester Tan, Chiang Wei (2022) Light Intensity and Temperature Parameters Study for Solar-powered Internet of Things to Improve Photovoltaic Energy Harvest. Borneo Journal of Sciences & Technology, 4 (1). pp. 37-43. ISSN 2672-7439

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Abstract

The Internet of Things (IoT) is an emerging technology that provides inter-device connectivity and is widely used in today's world. Maintaining battery life is one of the challenges of the technology mentioned and solar energy appears to be the solution. Solar energy is the conversion of energy from the sun to electricity, either directly with photovoltaic (PV), indirectly with concentrated solar energy, or a combination of both. In order to get the best out of photovoltaic energy, the development focus on improving the efficiency of the solar panels used. The variation in the intensity of sunlight contributes to a significant reduction in efficiency. The study aims to investigate the effect of light intensity and temperature parameters on the photovoltaic energy harvest. In this study, the light intensity was measured with a light-dependent resistance (LDR) sensor module. The analog output of the LDR sensor module was converted by the microcontroller to the digital output and computed using the voltage-resistance-intensity equation for the luminous intensity in lux. The solar efficiency towards the temperature was calculated based on the temperature coefficient of the solar panel used to identify the maximum output power. The solar panel efficiency graph providing insights into the maximum power that can be generated at a particular temperature. The study proves that the light intensity and temperature parameters make photovoltaic energy harvesting more efficient.

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