Enabling IoT-enhanced Data Models for Context-aware Hydropower Plants

Abstract

Hydroelectric power, or hydropower, harnesses the potential energy of water descending from higher to lower elevations to generate electricity. As a well-established and cost-effective renewable energy technology, it not only produces power but also supports significant water management services. The integration of Internet of Things (IoT) technologies in hydropower plants has shown significant potential in enhancing monitoring, efficiency, and control capabilities. However, current implementations often lack a holistic and standardized approach to contextual modeling. To address this gap, this paper presents a comprehensive approach to modeling the structural and operational components of hydropower plants (HPPs) using NGSI-LD data models. We propose detailed NGSI-LD data models that incorporate both static properties (e.g., location, structural attributes), relationships (e.g., component interactions, hierarchical dependencies) and dynamic properties (e.g., real-time sensor data, operational status). These models are designed to facilitate efficient data integration, support decision-making processes, and enable the development of interoperable and replicable IoT applications for smart hydropower plants. We validate our approach through deployment and testing on a federated context broker architecture using real-world data from HPPs.