Online Solar Energy Prediction for Energy-Harvesting Internet of Things Devices

Low-power internet of things devices have the potential to transform multiple fields including healthcare, environmental monitoring, and digital agriculture. However, the operating life of these devices is severely constrained by their small batteries that require frequent recharging. Harvesting energy from ambient sources has emerged as an effective approach to prolong the lifetime of these devices. The harvested energy must be carefully managed to ensure that sufficient energy is available when ambient energy is scarce. Prediction of the energy available in the future can aid energy management algorithms in making better decisions about the allocation of the available energy. This paper proposes a novel hierarchical machine learning model that considers recent history and daily variations to make accurate predictions of future energy availability. We also propose using online learning to adapt the model to seasonal and spatial variations in the harvested energy. Using real-world solar energy data from the National Renewable Energy Laboratory we show that the proposed approach has mean absolute errors of 1.5 J and 1.1 J in predictions under same and different locations, respectively. We also demonstrate that using the proposed approach in an energy management algorithm leads to 54% higher utility and 95% lower battery violations than the baseline.