Operationalizing Solar Energy Predictions for Sustainable, Autonomous IoT Device Management

• Published in: IEEE internet of things journal 2020-12, Vol.7 (12), p.11803-11814
• Main Authors: Kraemer, Frank Alexander, Palma, David, Braten, Anders Eivind, Ammar, Doreid
• Format: Article
• Language: English
• Subjects: Device Management; Distance learning; energy harvesting; Internet of Things; Machine learning; Mathematical models; Predictive models; Schedules; Solar energy; Task analysis; Task scheduling; Volatility; Weather forecasting
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2020.3002330

For sustainable Internet-of-Things (IoT) systems, the solar power prediction is an essential element to optimize performance, allowing devices to schedule energy-intensive tasks in periods with excess energy. In regions with volatile weather, this requires taking the weather forecast into account. The problem is how to provide such solar energy predictions with high accuracy for large-scale IoT systems with various devices in an autonomous way, without manual adaptation effort. We present a detailed study on machine-learning approaches for the prediction of solar power intake for large-scale IoT systems. We examine which machine learning models, feature sets, and sampling rates gain the best results for a medium-term forecasting horizon. We also explore an operational setting in which devices are deployed without prior data and machine learning models are retrained for each sensor continuously as a form of online learning. Our results show that prediction errors can be reduced by 20 % compared to the state of the art, despite strong weather volatility.