Power Estimation for Indoor Light Energy Harvesting Systems

The growing interest in indoor light energy harvesting for wireless sensor systems and low-power electronics has created a demand for systematic design methods that optimize the system implementation and component choices for different lighting scenarios. Although the spectrum of light is known to influence the efficiency and output power of a photovoltaic (PV) cell, existing power estimation methods neglect the spectrum. By contrast, the power estimation method proposed in this article takes spectral and intensity information into account. It uses low-cost sensors to measure spectral information and select an appropriate device model based on the classification of the light source. The method is evaluated under different light conditions, including individual light sources, mixed artificial light sources, and mixtures of artificial light and sunlight. The results demonstrate that the proposed implementation selects a reasonable model in most cases, including mixed light source conditions. Using light source specific models for PV panels, the resulting estimation error is low and has clear advantages over methods neglecting spectral information.

I. Introduction

Solar energy harvesting has been demonstrated to be a viable solution for energy-autonomous wireless sensor devices, particularly in outdoor applications [1]–[4]. To be able to design and dimension an energy harvesting system, good estimates of the available ambient energy that can be harvested are needed. In the case of light energy harvesting, a typical design procedure starts by characterizing the ambient light conditions. While these conditions vary over time, data must be collected during sufficiently long periods. This data can then be used to estimate how much energy can be harvested and to select and dimension components, such as photovoltaic (PV) cells and suitable energy storage elements.