The rise in electricity costs for households over the past year has driven significant changes in energy usage patterns, with many residents adopting smarter energy-efficient practices, such as improved indoor insulation and advanced home energy management systems powered by IoT and Digital Twin technologies. These measures not only mitigate rising bills but also ensure optimized thermal comfort and sustainability in typical residential settings. This paper proposes an innovative framework to facilitate the adoption of energy-efficient practices in households by leveraging the integration of Internet of Things technologies with Digital Twins. It introduces a novel approach that exploits standardized parametric 3D models, enabling the efficient simulation and optimization of home energy systems. This design significantly reduces deployment complexity, enhances scalability, and empowers users with real-time insights into energy consumption, indoor conditions, and actionable strategies for sustainable energy management. The results showcase that the proposed method significantly outperforms traditional approaches, achieving a 94% reduction in deployment time and a 98% decrease in memory usage through the use of standardized parametric models and plug-and-play IoT integration.

The efficient management of IoT systems is fundamental to advancing smart cities while enabling the seamless integration of technologies that enhance urban sustainability and resilience. This paper introduces a Hierarchical Resource Management System (HRMS) tailored for IoT-enabled smart cities, emphasizing a decentralized architecture at the building level and scaling up to city-wide applications. At its core, the system integrates the Adaptive Resilient Node (ARN), designed to autonomously manage energy resources and ensure continuous operation through self-healing capabilities. This study outlines the HRMS architecture, operational workflows, and core functionalities, demonstrating how the hierarchical framework supports real-time decision-making, fault tolerance, and scalable resource allocation. The proposed system’s lightweight inter-node communication enhances workload balancing and system responsiveness, addressing critical challenges in urban energy management. Experimental evaluations show that the system achieves up to a 50% improvement in energy efficiency and a 30% reduction in downtime across various urban environments, highlighting its transformative potential for sustainable and resilient urban growth.

The increased usage of smart sensors has introduced both opportunities and complexities in managing residential energy consumption. Despite advancements in sensor data analytics and machine learning (ML), existing energy management systems (EMS) remain limited in interpretability, adaptability, and user engagement. This paper presents EnergiQ, an intelligent, end-to-end platform that leverages sensors and Large Language Models (LLMs) to bridge the gap between technical energy analytics and user comprehension. EnergiQ integrates smart plug-based IoT sensing, time-series ML for device profiling and anomaly detection, and an LLM reasoning layer to deliver personalized, natural language feedback. The system employs statistical feature-based XGBoost classifiers for appliance identification and hybrid CNN-LSTM autoencoders for anomaly detection. Through dynamic user feedback loops and instruction-tuned LLMs, EnergiQ generates context-aware, actionable recommendations that enhance energy efficiency and device management. Evaluations demonstrate high appliance classification accuracy (94%) using statistical feature-based XGBoost and effective anomaly detection across varied devices via a CNN-LSTM autoencoder. The LLM layer, instruction-tuned on a domain-specific dataset, achieved over 91% agreement with expert-written energy-saving recommendations in simulated feedback scenarios. By translating complex consumption data into intuitive insights, EnergiQ empowers consumers to engage with energy use more proactively, fostering sustainability and smarter home practices.

Autonomous PV systems offer a substitute for or a safety net against the transmission and distribution companies’ rising and expanding electricity delivery fees. Across the world, delivery fees have climbed by about 3% annually while the cost of energy supply has only gone up by 5% over the past 15 years. In this paper, the drawbacks of PV system integration are highlighted while introducing a unique autonomous PV system. The system is also constructed in a reproducible way so that it can be duplicated. Replicability is showcased by the presentation of two separate PV systems that were created and evaluated using the same methodology. One system uses Si-based conventional photovoltaics, while the other uses dye-sensitized solar cells. The systems that are being offered also support remote connections, allowing all of the data from the PV system to be transmitted to a single endpoint (e.g., a visualization platform). Moreover, the system’s batteries were modified to accommodate the two PV systems. Finally, experimental results showcase the operability of the proposed architecture.

The crucial need for perpetual monitoring of photovoltaic (PV) systems, particularly in remote areas where routine inspections are challenging, is of major importance. This paper introduces an advanced approach to optimizing the maximum power point while ensuring real-time PV error handling. The overarching problem of securing continuous monitoring of photovoltaic systems is highlighted, emphasizing the need for reliable performance, especially in remote and inaccessible locations. The proposed methodology employs an innovative genetic algorithm (GA) designed to optimize the maximum power point of photovoltaic systems. This approach takes into account critical PV parameters and constraints. The single-diode PV modeling process, based on environmental variables like outdoor temperature, illuminance, and irradiance, plays a pivotal role in the optimization process. To specifically address the challenge of perpetual monitoring, the paper introduces a technique for handling PV errors in real time using evolutionary-based optimization. The genetic algorithm is utilized to estimate the maximum power point, with the PV voltage and current calculated on the basis of simulated values. A meticulous comparison between the expected electrical output and the actual photovoltaic data is conducted to identify potential errors in the photovoltaic system. A user interface provides a dynamic display of the PV system’s real-time status, generating alerts when abnormal PV values are detected. Rigorous testing under real-world conditions, incorporating PV-monitored values and outdoor environmental parameters, demonstrates the remarkable accuracy of the genetic algorithm, surpassing 98% in predicting PV current, voltage, and power. This establishes the proposed algorithm as a potent solution for ensuring the perpetual and secure monitoring of PV systems, particularly in remote and challenging environments.