Estimating Harvestable Energy in Time-Varying Indoor Light Conditions

Abstract

Ambient light energy harvesting is a cost-effective and mature approach for supplying low-power sensor systems with power in many indoor applications. Although the spectral information of a light source is known to influence the efficiency and output power of a photovoltaic cell, the spectrum of the ambient illumination is due to measurement complexity often neglected when characterizing light conditions for power estimation purposes. In this paper we evaluate the influence of considering spectral information on the energy estimation accuracy. We create a dataset of varying light conditions in a typical indoor environment based on eight locations. For each location, we compare the energy estimation accuracy with and without spectral considerations. The results of this investigation demonstrate that a spectrum-based method leads to significant performance improvements in cases where the light condition is not defined by a single light source.