IoT Monitoring Framework with Physics-Based Energy Loss Modeling for Smart Microgrids: Architecture and Benchmarks

ESP32-S3-based single-phase smart meter with containerized IoT backend and residential consumption forecasting

This paper presents a low-cost IoT-based architecture for residential electricity metering and consumption forecasting, centered on a single-phase smart meter with Wi-Fi connectivity and an ESP32-S3 microcontroller. The proposed system combines local signal acquisition with an end-to-end communication infrastructure based on MQTT, enabling real-time transmission of electrical measurements from the edge device to a remote server for storage, visualization, and predictive analysis. Experimental results demonstrated satisfactory metering performance, with an average current MAE of 0.23 A and MAPE of 4.56% when compared with a CW500 reference power analyzer. From the telecommunications perspective, the communication tests showed low gateway latency, ranging from 1.53 to 12.8 ms, and server latency between 203.63 and 290.86 ms, indicating adequate responsiveness for real-time monitoring applications. For consumption forecasting, the AI models were trained and evaluated using the Low Carbon London dataset rather than data collected entirely by the prototype; the 1D CNN achieved MAE = 0.009109 and MSE = 0.000195, while the LSTM obtained MAE = 0.015597 and MSE = 0.000538. The architecture integrates open-source networking and data services, including Mosquitto, Telegraf, InfluxDB, Grafana, and Docker Compose, resulting in a replicable and scalable platform for smart energy monitoring in residential IoT environments.

Design and Development of a Solar-Powered IoT-Based Energy Consumption Monitoring System

Residential energy conservation is frequently hindered by a lack of real-time visibility into usage, widening the "behavior-action gap" between daily electricity utilization and delayed monthly utility statements. This paper presents the development of a low-cost, open-source, residential solar-powered Internet of Things (IoT) monitoring system. The proposed architecture segments data collection tasks into a Direct Current (DC) renewable generation node and an Alternating Current (AC) appliance-level consumption interface using dual ESP32 microcontrollers. The system collects data from precision inline INA226 shunt sensors on the solar and battery storage paths, alongside a ZMPT101B transformer and an SCT-013 current transformer wrapped in a physical 5-turn wire loop to lift low-power current boundaries above standard resolution floors. Telemetry is streamed asynchronously via Message Queuing Telemetry Transport (MQTT) over a cloud HiveMQ broker. To optimize edge node efficiency, chronological timestamping is completely decoupled from the hardware layer and managed server-side upon database ingestion into InfluxDB. Real-time and historical analytics are visualised using a centralized Grafana web dashboard interface. Experimental results show that the hardware sensing layer maintained high measurement precision, yielding low absolute errors (Verror ≤ 0.38% and Ierror ≤ 32%) and successfully resolving low-power household standby states down to single-digit wattages (e.g., 9W). The event-driven alert system achieved zero-latency multi-tier warnings, verifying that the proposed framework delivers an accessible, high-fidelity sub-metering platform for urban electrification programs.

An IoT-Based Real-Time Energy-Management System for Smart Load Control in a Residential Microgrid

The increasing complexity of residential energy systems and the growing penetration of distributed resources require practical energy-management solutions that extend beyond conventional metering. This paper presents the design and implementation of a real-time Internet of Things (IoT)-based energy-management system for monitoring and controlling household energy consumption under different operating conditions. The proposed system adopts a dual-processor architecture, in which a primary microcontroller performs time-critical electrical measurements and low-level load switching, while a secondary processor operates as a local IoT gateway for data handling, rule-based control decisions, local visualization, and message queuing telemetry transport (MQTT)-based cloud communication through a 4G link. The contribution of this work is not associated with the individual use of dual processing, cellular communication, cloud monitoring, load shedding, or backup power, as these technologies have been previously reported in smart-metering and home energy-management systems. Instead, the study focuses on their coordinated integration within a residential-scale prototype that combines calibrated per-load monitoring, priority-based load control, outage-resilient reporting, and credit-aware load restriction. The system measures voltage, current, active and apparent power, power factor, and energy consumption for individual loads and supports centralized visualization through a cloud-based dashboard. The prototype was experimentally evaluated under three representative scenarios: overload, main power outage, and low-credit operation. In the overload scenario, automatic priority-based load shedding reduced the total load by up to 75%. During power outages, a battery-supported subsystem maintained monitoring and communication for real-time outage reporting. In the low-credit scenario, non-essential loads were disconnected when the user balance fell below a predefined threshold, while essential loads remained energized. The results demonstrate that the implemented prototype can provide integrated monitoring, local rule-based control, cloud reporting, and backup-supported operation within a unified residential energy-management platform.

A model-based approach to improving the dependability of IoT-enabled weather monitoring systems

Low-cost Internet of Things (IoT) weather stations enhance spatial and temporal coverage for hyperlocal forecasting, especially in remote or hard-to-reach areas where traditional monitoring infrastructure is limited. However, their dependable operation is affected by component reliability, message delivery performance, and energy-related constraints, particularly battery depletion and solar recharge variability. This paper presents a dependability analysis of a real IoT-enabled weather monitoring platform based on a Weather Monitoring Approach (WMA), modeled using Stochastic Petri Nets (SPNs) to evaluate availability and reliability, while explicitly modeling energy autonomy as a cross-cutting operational constraint that affects continuous operation. Results show that the proposed WMA significantly increases operational availability, reduces failure probability, and improves energy autonomy by reducing the likelihood of battery depletion and extending operational continuity. In addition, the optimized communication configuration substantially decreased the latency required for near-certain message delivery, highlighting the impact of transmission tuning on system dependability. The proposed WMA provides a means to analyze configuration and design changes that can further improve system dependability, demonstrating how the combination of reliability modeling, energy autonomy mechanisms, and efficient communication strategies can substantially enhance the dependability of IoT-enabled weather monitoring systems and support continuous operation in regions with limited maintenance accessibility.

A data-driven computational framework for IoT data quality assessment in solar–EV-integrated energy systems

Design and Experimental Validation of a Low-Cost Edge-IoT Architecture for Sustainable Photovoltaic Monitoring and Adaptive MPPT Control

The digitalization of photovoltaic (PV) systems can support sustainable energy deployment by improving operational efficiency, system visibility, and energy extraction. However, many existing Internet of Things (IoT)-enabled solutions address monitoring and maximum power point tracking (MPPT) separately or depend on proprietary platforms, remote cloud services, and relatively costly hardware, which may restrict their accessibility and replication in small-scale and resource-constrained applications. This study presents the implementation and laboratory-scale experimental evaluation of an edge-IoT architecture that integrates real-time PV monitoring, embedded adaptive MPPT control, local data management, and visualization using low-cost hardware and open-source software. The proposed architecture combines an ESP32 microcontroller with a Raspberry Pi (RPi) local server to enable environmental and electrical sensing, edge-based control, message queuing telemetry transport (MQTT) communication, local data storage, and interactive visualization through the open-source Node-RED, InfluxDB, and Grafana platforms. An adaptive perturb-and-observe (AP&O) algorithm is implemented on the ESP32 to dynamically adjust the duty cycle of a DC–DC boost converter in response to changing operating conditions. The system is experimentally evaluated using a PV test bench equipped with a custom boost converter and sensing modules measuring eleven electrical and environmental parameters. The architecture achieved an average communication latency of 193 ± 23 ms and an average MPPT efficiency of 97.3 ± 0.54%. It also provided a power gain of 0.7 ± 0.5% compared with the conventional fixed-step perturb-and-observe method. By combining local processing, open-source software, low-cost components, and integrated monitoring and control, the proposed system reduces dependence on external cloud infrastructure while supporting responsive and accessible PV energy management. These results demonstrate its potential as a replicable technological framework for improving the operational sustainability and digital management of small-scale PV installations.