This paper presents an integrated system for thermal monitoring and anomaly detection of solar pv panels using TinyML and Edge Computing. The proposed system employs a low-resolution thermal sensor (MLX90640) in conjunction with embedded machine-learning techniques to perform early anomaly detection and preventive maintenance. This research seeks to address current challenges in the eficient management of photovoltaic installations by proposing a holistic solution that promises to significantly improve the performance and longevity of solar systems. The proposed approach aims to the processing of thermal data locally, reducing latency and improving energy eficiency. Four TinyML models were developed and compared using Edge Impulse, with the most successful model achieving 87.70% accuracy in anomaly detection. The study highlights the potential of this technology to improve the eficiency, reliability, and cost-efectiveness of PV installations, while also recognizing limitations and challenges in large-scale implementation. It highlights key areas for future studies, such as the integration of data from multiple sensors and the development of more advanced algorithms, highlighting the potential of this technology to drive the adoption of renewable energy and efectively combat climate change.
Solar PV has established itself as one of the most promising and fast-growing renewable energy sources
worldwide. Its ability to generate electricity in a clean and sustainable manner positions it as a key pillar
in the transition to a greener energy future [
High temperatures can significantly reduce the conversion eficiency and accelerate the degradation
of PV modules. Recent studies have shown that thermal variations on the surface of solar panels can
lead to the formation of hot spots, which not only decrease the overall system eficiency but can also
cause irreversible damage to the modules [
In this context, accurate and real-time thermal monitoring of solar panels becomes crucial to optimize
system performance, prevent failures, and extend the lifetime of PV installations [
The advent of Machine Learning (ML) and Edge Computing technologies ofers new possibilities to
address these challenges [
The integration of TinyML into solar panel monitoring systems ofers several significant advantages:
1. Local processing: by running ML algorithms directly on the sensing devices, the amount of data
that needs to be transmitted is drastically reduced, resulting in lower latency and more eficient
use of bandwidth [
However, the implementation of TinyML in solar thermal monitoring systems also presents unique
challenges. The limited processing and memory capacity of edge devices requires careful optimization
of ML models [
In this context, this paper presents a thermal monitoring system for solar panels based on TinyML
and Edge Computing. The proposed system uses low-cost thermal sensors and microcontrollers
with ML capabilities to perform real-time analysis of the thermal distribution of solar panels [
The rest of the article is organized as follows: Section 2 presents a comprehensive review of the state-of-the-art in thermal monitoring of solar panels, ML applications in PV systems, and advances in TinyML and Edge Computing. Section 3 describes in detail the design and architecture of the proposed system. Section 4 focuses on the development and optimization of the TinyML model. Section 5 presents the experimental results and a discussion of these. Finally, Section 6 concludes the paper and proposes future research directions.
Thermal monitoring of solar panels has been the subject of numerous studies in recent decades, given its
importance for the performance and reliability of PV systems. Buerhop et al. [
Demir et al. [
Recent advances in thermal image processing have led to the development of automatic anomaly detection techniques. For example, Oulefki et al. [31] proposed an approach based on unsupervised detection algorithms and 3D augmented reality to identify damaged areas in PV modules. Such approaches improve the objectivity and eficiency of thermal data interpretation.
Wang et al. [
Machine Learning has gained ground in various applications related to photovoltaic systems, from energy production prediction to fault detection and performance optimization.
Mellit et al. [32] demonstrated the use of TinyML for PV module fault diagnosis using the Edge Impulse platform. Their approach achieved high accuracy in classifying common faults such as hot spots, cracks, and encapsulant degradation.
Jaybhaye et al. [
Pamungkas et al. [
Hassan and Dhimish [
Bhattacharya and Pandey [33] developed a TinyML model optimized for soil quality monitoring and management in agriculture, which could be adapted for applications in photovoltaic systems. Their approach, based on sidechain and energy eficiency testing, demonstrated significant improvements in power consumption and latency.
Hayajneh et al. [34] investigated the role of TinyML in improving solar energy yield predictions. Their study compared several modern machine learning models and highlighted the potential of TinyML to provide intelligent and eficient forecasts in solar energy systems.
Cardinale-Villalobos et al. [
Hidalgo et al. [
TinyML has emerged as a promising technology for implementing ML algorithms on resource-constrained
devices. The papers [
Several platforms and tools have emerged to facilitate the development and deployment of TinyML
models:
1. Edge Impulse: An end-to-end platform for the development and deployment of TinyML models,
ofering tools for data collection, model training, and optimized code generation [32].
2. TensorFlow Lite for Microcontrollers: An optimized version of TensorFlow designed specifically
for embedded systems [
Model optimization is crucial for the efective deployment of TinyML. Common techniques include:
1. Quantization: Reducing the precision of model weights from floating point to integers, resulting
in smaller, computationally eficient models [
Liu et al. [38] proposed TinyTS, a memory-eficient TinyML model compilation framework for microcontrollers. Their approach demonstrated significant improvements in memory usage and execution time for machine learning models on resource-constrained devices.
In the context of solar applications, Gruosso and Gajani [39] performed a comparison of ML algorithms for performance evaluation of PV power forecasting and management in the TinyML framework. Their results demonstrated the feasibility of implementing complex prediction models on edge devices.
Oliveira and Moreira [
Wardana et al. [30] demonstrated the application of TinyML models for low-cost air quality monitoring devices. Their approach of using low-cost sensors combined with optimized ML models is directly applicable to solar panel monitoring.
Despite significant advances in thermal monitoring of solar panels and ML applications in photovoltaic
systems, several challenges remain:
1. Balance between accuracy and eficiency: Implementing complex ML models on
resourceconstrained devices requires a careful balance between model accuracy and computational
eficiency [
These limitations and challenges present significant opportunities for research and development of
innovative solutions that combine advances in TinyML, edge computing, and sensor technologies to
create more eficient, scalable, and adaptive monitoring and optimization systems for PV installations
[
The proposed system is based on a three-tier architecture that combines the capabilities of TinyML and Edge Computing to provide a scalable and eficient solution for the thermal monitoring of solar panels. This architecture is specifically tailored to the needs of photovoltaic systems. The main components of the system are: 1. 100 W monocrystalline and polycrystalline solar panels 2. 2. MLX90640 sensor. 3. Microcontrollers with TinyML capabilities such as Arduino Nano 33 BLE Sense. 4. Additional sensors for ambient temperature, humidity, and solar radiation.
5. Local server based on Raspberry Pi 5 for data visualization and sending to the cloud.
In this initial testing phase, we use a public dataset that simulates the data that would be captured by thermal sensors in real solar panels. This approach allows us to validate the efectiveness of the system before its implementation on real hardware. The dataset [40] is selected because it is the one with twenty thousand data classified into 12 groups. Ten thousand data correspond to panels in normal conditions and the other ten thousand data are divided into anomalies such as hot spots, shadows, ruptures, etc. The files are in JPG format and have a resolution of 24x40 pixels, which is close to the 24x32 pixels of the MLX90640.
The model used in the Edge Impulse software is set for an Arduino Nano 33 BLE Sense. This device is fully supported in the software, is low cost and its processing capabilities, power consumption and size make it an ideal device for our application.
For this initial phase and as mentioned above, a public dataset [40] containing thermal measurements of solar panels in diferent conditions and with twelve labels or classes is selected. A description of each class is given in Table 1. Figure 1 shows random data for each class. Hot spot occurring with a square geometry in a single cell.
Hot spots occurring with a square geometry in multiple cells.
Module anomaly caused by cracking on the module surface.
Hot Spot on a thin film module.
Multiple hot spots on a thin film module.
Sunlight obstructed by vegetation, man-made structures, or adjacent rows.
Activated bypass diode, typically 1/3 of the module.
Multiple activated bypass diodes, typically afecting 2/3 of the module.
Panels blocked by vegetation.
Dirt, dust, or other debris on the surface of the module.
Entire module is heated.
Nominal solar module.
Edge Impulse is used for the development and training of the TinyML model, taking advantage of its optimization capabilities for edge devices. The process includes: 1. Importing the prepared dataset into Edge Impulse. 2. Separating the training and test data, using a ratio of 80/20 for each label.
3. Design of the model architecture, the classification block is used. 4. Model training.
5. Model evaluation by analyzing metrics such as accuracy, recall, and F1-score.
Considering the characteristics of the selected dataset and the imbalance between the amount of data in the twelve labels, it was decided to develop four models in Edge Impulse and perform the respective comparison to determine which one best fits the application developed in this paper. The models developed are the following: 1. Model 1a: Model using all twelve labels, 20 training cycles, and the predetermined architecture for the neural network. The predetermined architecture consists of a 2D conv/pool layer (16 iflters, 3 kernel size, 1 layer), a 2D conv/pool layer (32 filters, 3 kernel size, 1 layer), a flattened layer, and a Dropout (rate 0.25). 2. Model 1b: Model using all twelve labels, 30 training cycles and with the following architecture: a 2D conv/pool layer (16 filters, 3 kernel size, 1 layer), a 2D conv/pool layer (32 filters, 3 kernel size, 1 layer), a 2D conv/pool layer (64 filters, 3 kernel size, 2 layers), a 2D conv/pool layer (128 filters, 3 kernel size, 2 layers), a Flatten layer, a Dense layer (128 neurons), a Dropout (rate 0.25) and a Dense layer (12 neurons). 3. Model 2a: Model using only two labels, normal and abnormal, using the No-Anomaly label as normal and the other eleven labels as abnormal. This model uses 20 training cycles and with the same architecture as Model 1a. 4. Model 2b: Model using only two labels, same as Model 2a. This model uses 20 training cycles and with the following architecture: a 2D conv/pool layer (16 filters, 3 kernel size, 1 layer), a 2D conv/pool layer (32 filters, 3 kernel size, 1 layer), a 2D conv/pool layer (64 filters, 3 kernel size, 2 layers), a 2D conv/pool layer (128 filters, 3 kernel size, 2 layers), a Flatten layer, a Dense layer (128 neurons), a Dropout (rate 0.25) and a Dense layer (2 neurons).
The results of the Edge Impulse software for Model 2b, which uses only the Normal and Abnormal classes and the custom architecture, are presented below. Figure 3 shows some of the most relevant data produced by the Edge Impulse software for Model 2b. The results of the other models follow the same structure.
Evaluating the performance of the four TinyML models developed in Edge Impulse, the following is obtained:
Analyzing the computational eficiency of the model, the following is determined:
According to the data shown in Tables 2 and 3, the results can be interpreted as follows: • The best performing TinyML model (Model 2b) achieved an accuracy of 87.70% in detecting thermal anomalies. To enhance this accuracy, future work could explore techniques such as data augmentation for underrepresented anomaly classes, implementing ensemble learning methods, or applying advanced model optimization techniques. Also, data augmentation could greatly improve the performance of Models 1a and 1b. • The float32 and int8 models have similar accuracy, which demonstrates the feasibility of using
Edge devices in this type of application.
• Models 2a and 2b have a lower loss, which is expected since it is a simpler problem with only two
classes.
• There is a trade-of between the accuracy, which was achieved with a more complex architecture,
and the Computational metrics. This is expected, as a more complex architecture requires more
memory and processing power. However, this trade-of is not as significant as expected, with
about two times the amount of Inferencing Time and six to 9 times the amount of flash usage.
• Running ML algorithms directly on edge devices allows for more detailed, real-time analysis of
thermal data. As noted by Pamungkas et al [29], early and accurate fault detection can significantly
reduce downtime and associated maintenance costs. In addition, the ability to distinguish between
diferent types of thermal anomalies, such as hot spots, cracks, or encapsulant degradation, allows
for a more targeted and efective response to each problem [
Despite the promising results, it is important to recognize several limitations and challenges of our study:
The use of a public dataset, while useful for the initial validation of the concept, presents significant limitations that must be addressed in future research: • Representativeness: The current dataset, though diverse, may not fully capture the complexity and variability of thermal conditions in solar panels across diferent real-world scenarios. Real PV installations are subject to a wide range of environmental factors, installation configurations, and operational conditions that may not be adequately represented in the public dataset. • Parameter mismatch: The data used, although close, does not have the exact pixel size of the data to be captured with the MLX90640 thermal sensor. This discrepancy could lead to potential inaccuracies when transitioning from the model trained on the dataset to real-world applications. • Lack of temporal data: The current dataset likely consists of static images, whereas real-world thermal patterns in solar panels evolve over time. This temporal aspect is crucial for understanding the progression of anomalies and for developing more accurate predictive models.
There is a critical need for validation with real data to address these limitations. A thorough validation with real field data is required to confirm the efectiveness of the system under actual operating conditions. This involves collecting thermal data from a diverse range of operational solar installations and capturing data across diferent times of day, seasons, and weather conditions. It is imperative to establish a protocol for continuous, long-term data collection from multiple PV installations for the analysis of thermal patterns over extended periods, the study of the relationship between thermal anomalies and long-term panel degradation, and the development of more robust and adaptable models. Also, it is important to validate the thermal anomalies detected by the system against actual, physically confirmed faults in solar panels. Furthermore, it is necessary to conduct extensive testing with the actual MLX90640 sensor to be used in the final system, ensuring that the model’s performance translates accurately to the specific characteristics of this sensor and that any discrepancies between the training data and real sensor output are identified and addressed. Finally, regarding scalability, it is important to implement the system across multiple panels and arrays to identify any unforeseen challenges in managing a network of edge devices.
Although our study focused purely on the use of Edge Impulse, implementation on real hardware presents several challenges: • Resource constraints: microcontrollers have significant limitations in terms of memory and processing power. On a positive note, Edge Impulse is fully integrated with the Arduino device to be used, so estimates are very close to reality. • Reliability and durability: Devices deployed in the field will be exposed to harsh environmental conditions. Ensuring their long-term reliability and durability represents a significant challenge.
Large-scale implementation of the system presents additional challenges: • Device management: Managing a large fleet of edge devices presents logistical and technical challenges, especially in terms of firmware and model updates. • Data aggregation: Efectively integrating data from multiple devices for plant-level analytics requires advanced data aggregation and fusion strategies. • Security and privacy: The distributed nature of the system poses challenges in terms of data security and protection against malicious attacks.
The proposed system has the potential to significantly improve the eficiency and longevity of PV installations: • Early failure detection: The ability to identify thermal anomalies at early stages can prevent further damage and extend the lifetime of solar panels. This capability is particularly valuable considering the impact of temperature on panel performance. • Dynamic optimization: Continuous, real-time monitoring allows for dynamic adjustments in system operation, maximizing eficiency in diferent environmental conditions. • Predictive maintenance: The ability to predict failures more accurately and in advance allows for more eficient maintenance planning, reducing downtime and associated costs.
The system design, based on Edge Computing and TinyML principles, ofers significant advantages in terms of scalability and adaptability: • Flexible deployment: The distributed architecture allows for flexible and scalable deployment, suitable for both small installations and large solar plants. • Adaptation to local conditions: The ability to retrain models locally allows adaptation to site-specific conditions, improving the accuracy and relevance of detections. • Integration with existing systems: The system can be integrated with existing monitoring and control infrastructures, providing an additional layer of intelligence and optimization.
Based on the identified results and limitations, we propose the following directions for future research: • Validation with Real Data:Conduct extensive field testing with real thermal data from solar panels under various operating and environmental conditions. This is thoroughly discussed on section 5.4.1. • Integration of Multiple Data Sources:Expand the model to incorporate meteorological data (e.g., ambient temperature, humidity, solar irradiance) to improve contextual understanding of thermal patterns. Integrate electrical performance data (e.g., voltage, current, power output) to correlate thermal anomalies with electrical behavior. Explore the inclusion of visual inspection data to complement thermal analysis, potentially using multi-modal machine learning approaches. • Developing More Advanced Models:Explore techniques for continual learning and model adaptation to allow the system to improve its performance over time without requiring complete retraining. Develop ensemble methods that combine multiple lightweight models to improve overall accuracy and robustness. • Scalability and Network Optimization:Research eficient methods for managing and updating large fleets of edge devices in distributed solar installations. Develop advanced data aggregation and compression techniques to minimize bandwidth usage while maintaining high-fidelity analytics at the system level. Investigate peer-to-peer communication protocols that allow edge devices to share insights and anomaly detections without relying on central servers. • Enhanced Anomaly Detection and Classification: Develop more granular classification models that can distinguish between diferent types of thermal anomalies (e.g., hot spots, bypass diode failures, cell cracks) with high accuracy. Explore unsupervised and semi-supervised learning techniques to identify novel or previously unseen types of thermal anomalies. • Long-Term Reliability and Degradation Studies: Conduct studies to evaluate the long-term impact of using TinyML-based thermal monitoring on PV system performance and longevity. Develop models that can predict long-term degradation patterns based on thermal and operational data collected over extended periods. Investigate the reliability and durability of edge devices and sensors in harsh environmental conditions typical of solar installations. • Economic and Environmental Impact Analysis:Conduct comprehensive cost-benefit analyses of implementing TinyML-based monitoring systems at various scales of PV installations. Evaluate the potential reduction in carbon footprint achieved through improved PV system eficiency and reduced maintenance requirements. Explore the broader implications of widespread adoption of this technology on the solar energy industry and renewable energy adoption rates.
In this paper, we establish the significant potential of thermal monitoring systems based on TinyML and Edge Computing to transform the operation and maintenance of PV installations. The results obtained in terms of anomaly detection accuracy are promising and suggest that this approach can efectively address many of the current challenges in the solar industry. However, it is crucial to recognize the limitations of our study, particularly regarding the use of public datasets and the need for validation under real operating conditions. Future research should address these aspects, as well as explore proposed directions for system development and refinement. Successful implementation of this technology has the potential to significantly improve the eficiency, reliability, and cost-efectiveness of PV installations, thus contributing to the acceleration of the global transition to renewable energy sources. However, it is important to carefully address ethical and social considerations to ensure that the benefits of this technology are distributed in an equitable and sustainable manner. Ultimately, the continued development and adoption of advanced technologies such as TinyML in the solar energy sector not only promises technical and economic improvements but can also play a crucial role in combating climate change and building a more sustainable energy future.
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