Design of IoT-Enabled Smart Power Monitoring Systems for Renewable Energy Integration

Thesis Abstract

The increasing global emphasis on renewable energy sources necessitates the development of intelligent, efficient, and scalable power management systems to optimize energy utilization and ensure grid stability. Traditional power monitoring approaches often lack the real-time, remote, and autonomous capabilities required to effectively integrate diverse renewable energy sources such as solar and wind into national grids. This study aims to design, develop, and evaluate an Internet of Things (IoT)-enabled smart power monitoring system tailored specifically for renewable energy integration, thereby enhancing grid reliability, energy efficiency, and system resilience. The primary objectives include identifying critical parameters for real-time monitoring, designing a modular IoT architecture with embedded sensors and wireless communication modules, implementing a cloud-based data processing platform, and assessing system performance in a practical setting. The research adopts a mixed-methods approach grounded in an engineering design science paradigm, combining rigorous qualitative analysis with quantitative validation. The population comprises renewable energy installations and grid management stations within a regional energy authority, with a sample of 30 operational renewable sites selected through stratified random sampling to ensure diversity in energy types and scales. Data collection instruments include IoT sensor prototypes, device calibration tools, and structured questionnaires administered to system operators. Validation of measurement instruments involves pilot testing and consistency checks, while reliability is assessed through Cronbach’s alpha coefficient exceeding 0.8. The experimental system integrates sensors measuring voltage, current, temperature, and irradiance, connected via Wi-Fi and LoRaWAN protocols to a centralized cloud platform. Data analysis primarily employs regression analysis to examine the relationship between monitored parameters and system efficiency, alongside descriptive statistics to summarize system performance. Analytical framework is based on the Technology Acceptance Model (TAM) to evaluate user interaction and system usability, supported by thematic analysis of qualitative feedback from pilot users. Expected findings indicate that the IoT-based monitoring system significantly improves real-time data accuracy, reduces manual intervention, and facilitates predictive maintenance, leading to enhanced renewable energy utilization and grid stability. The system’s effectiveness is anticipated to be validated through improved parameter correlation and reduced downtime metrics. The research will also reveal critical factors influencing user acceptance and operational integration of IoT systems in renewable energy contexts, contributing empirical evidence to existing literature. This study contributes novel insights into the integration of IoT technologies within renewable energy infrastructure, bridging gaps related to scalable sensor deployment, cloud-based analytics, and user-system interaction. It advances theoretical understanding by operationalizing the TAM within an engineering context and demonstrating its applicability for assessing technological adoption in energy systems. Methodologically, the research offers a comprehensive framework for designing scalable IoT-powered monitoring solutions, facilitating replicability across different renewable energy applications and regions. In conclusion, the study recommends the adoption of IoT-enabled monitoring systems as a standard practice for renewable energy projects, emphasizing the importance of robust network infrastructure, user-centered design, and continuous system evaluation. It advocates for further research on the integration of machine learning techniques for predictive analytics and the development of decentralized IoT architectures to enhance system robustness. Overall, the research underscores the transformative potential of IoT technologies in advancing sustainable energy systems and supporting national renewable energy targets through smarter, data-driven grid management.