Dataset

Energy harvesting is finding widespread use as a long-term energy supply for the Internet of Things (IoT). The dependency of these devices on the spatially and temporally variable environment further complicates reliable application design. We present a long-term indoor solar harvesting dataset to support the modeling, analysis, calibration and evaluation of energy harvesting systems. More than 2 years of joint high accuracy power and ambient condition traces were collected at 6 diverse indoor locations. The dataset provides a solid foundation for the design and validation of energy prediction, energy management and run-time adaptation schemes. The detailed description of the measurement setup and the resulting dataset is accompanied by the public release of the dataset, as well as the hardware design of the measurement platform and code examples for post-processing the dataset in R and Python.