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<p>This research proposes a MATLAB-/Simulink-based IoT framework for quantitative evaluation of data quality in solar–electric vehicle-integrated energy systems. The observed data of photovoltaic power, solar irradiance, load demand, ambient temperature, and electric car state of charge are collected at a sampling rate of 1&#xa0;Hz using an Internet of Things-based sensor framework. The MQTT protocol is used to send the detected data over IEEE 802.11 Wi-Fi to the central gateway, where it is then processed in MATLAB. Data reliability is evaluated using a multi-dimensional Data Quality Index (DQI), which comprises normalized metrics of timeliness, completeness, consistency, and noise level calculated through statistical analysis, signal-to-noise ratio estimation, and communication delay evaluation in the Simulink environment. With DQI values ranging from 0.55 to 0.92, the obtained simulation results demonstrate that data quality varies over time. The primary causes of DQI degradation are communication delays, sensor noise, and inconsistent data; packet loss has a minor effect. The proposed framework provides an organized method for assessing IoT data quality in applications related to energy systems.</p>

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

  • 期刊论文

  • 发表日期：

    2026年8月1日

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  • Ramya Kuppusamy,
  • Yuvaraja Teekaraman

摘要

This research proposes a MATLAB-/Simulink-based IoT framework for quantitative evaluation of data quality in solar–electric vehicle-integrated energy systems. The observed data of photovoltaic power, solar irradiance, load demand, ambient temperature, and electric car state of charge are collected at a sampling rate of 1 Hz using an Internet of Things-based sensor framework. The MQTT protocol is used to send the detected data over IEEE 802.11 Wi-Fi to the central gateway, where it is then processed in MATLAB. Data reliability is evaluated using a multi-dimensional Data Quality Index (DQI), which comprises normalized metrics of timeliness, completeness, consistency, and noise level calculated through statistical analysis, signal-to-noise ratio estimation, and communication delay evaluation in the Simulink environment. With DQI values ranging from 0.55 to 0.92, the obtained simulation results demonstrate that data quality varies over time. The primary causes of DQI degradation are communication delays, sensor noise, and inconsistent data; packet loss has a minor effect. The proposed framework provides an organized method for assessing IoT data quality in applications related to energy systems.