1. Introduction

Why We Need Smarter Solar Energy Management

Solar power is one of the most promising renewable energy sources, but keeping it running efficiently is not as simple as just setting up panels and letting them do their job. Solar panel performance depends on factors like weather changes, shading, temperature fluctuations, and aging, all of which impact energy output. For example, excessive heat reduces efficiency, dust accumulation obstructs sunlight, and seasonal shifts influence power generation (Dobrilovic et al., 2024).

Now, with more and more IoT-based sensor networks popping up—think smart cities, automated farms, and industrial monitoring—powering these devices reliably is a big challenge. Many of them are deployed in remote areas where traditional energy grids aren’t an option. Solar power makes the most sense, but monitoring and predicting energy output are essential for keeping everything running smoothly (Dobrilovic et al., 2024).

The Challenge: How Do We Predict Solar Panel Performance?

You can’t just check each panel manually—it’s costly, time-consuming, and often not possible for widely distributed sensor networks. Traditionally, systems rely on voltage sensors attached to each panel, but this approach isn’t scalable (Dobrilovic et al., 2024).

That’s where machine learning (ML) comes in. ML models can predict how much energy a solar panel will generate, based purely on environmental sensor data like UV levels and light intensity. This means even sensor nodes without solar panels can estimate solar potential, helping us optimize renewable energy use without expensive monitoring setups (Dobrilovic et al., 2024).

2. Solar Panel Performance: Why Solar Power Is the Best Choice for Sensor Nodes

Comparing Renewable Energy Sources

IoT networks need a reliable energy source. While there are a few options, solar is hands-down the most practical choice for sensor nodes deployed in remote locations (Dobrilovic et al., 2024).

Here’s a look at how solar stacks up against other renewable energy sources:

Energy Source	Best Use Case	Challenges
Wind Power	Works well in consistently windy areas	Requires turbines, high setup cost
Hydro Power	Great near rivers and lakes	Not suitable for dry or remote locations
Solar Power	Can be deployed almost anywhere	Efficiency depends on weather conditions

Unlike wind or hydro, solar panels can function completely off-grid, making them perfect for IoT devices in remote areas (Dobrilovic et al., 2024).

Solar Panel Performance: Why Solar-Powered Sensor Nodes Reduce Dependency on External Grids

Relying on grid electricity for IoT devices is not practical, especially for sensor networks spread across large areas. Solar-powered nodes give you independent energy generation, solving problems like:

• Frequent battery replacements, which drive up maintenance costs.
• The need for physical wiring, which isn’t possible for widely distributed sensors.
• Power interruptions, which can cause data loss or system failures.

Machine learning helps optimize these systems, ensuring efficient solar energy use even in fluctuating environmental conditions (Dobrilovic et al., 2024).

The Challenges in Maintaining Solar Panel Efficiency

Even though solar power is a fantastic energy source, panels don’t always work at full capacity. Several factors affect their efficiency, including:

• Weather fluctuations—cloudy days reduce sunlight exposure.
• Panel orientation—incorrect positioning lowers power output.
• Aging effects—older panels lose efficiency over time.

With traditional monitoring, these issues can be hard to track across large networks. But AI-powered solar performance prediction solves this by analyzing UV sensor and light intensity data to estimate solar voltage output (Dobrilovic et al., 2024).

3. Solar Panel Performance: Machine Learning for Solar Panel Performance Estimation

Why AI Helps Improve Solar Energy Efficiency

Traditionally, solar panel efficiency is measured using voltage sensors directly attached to the panel. This method works, but it has a huge limitation: it requires extra hardware that isn’t always available, especially for IoT nodes spread across different locations (Dobrilovic et al., 2024).

AI is changing the game by predicting solar performance based on environmental sensor data. Instead of physical voltage readings, machine learning models analyze light intensity, UV exposure, and temperature trends to estimate solar panel output.

This means sensor nodes can forecast solar power potential, even if they don’t have a panel installed—giving engineers and researchers insight into which locations are best suited for solar-powered upgrades (Dobrilovic et al., 2024).

Solar Panel Performance: How Scientists Collect Solar Panel Performance Data

To train machine learning models, researchers used sensor-based solar data collection systems. The setup included:

• BH1750 Light Sensor – Measures visible light intensity in lux.
• UV Sensor – Detects ultraviolet radiation levels.
• DHT22 Temperature & Humidity Sensor – Tracks weather conditions.
• TMP36 Temperature Sensor – Records the solar panel’s temperature.
• Voltage Sensor – Provides open-circuit voltage measurements for testing accuracy.

They compared various sensor combinations to find the best model for solar panel prediction. Here’s how different sensor setups performed in the tests:

Sensor Combination	R² Score	MSE	MAE
UV + BH1750 Light Sensor	0.96	0.08	0.24
BH1750 Light Sensor Only	0.97	0.08	0.22
UV Sensor Only	0.94	0.10	0.29

The results confirmed that UV and BH1750 sensors together gave the most accurate predictions, making them ideal inputs for AI-based solar forecasting models (Dobrilovic et al., 2024).

Using ML Models Like Random Forest and Decision Tree to Predict Solar Voltage

After collecting data, scientists tested multiple ML models to see which one worked best for predicting solar panel voltage output. The top-performing models were Random Forest and Decision Tree regressors (Dobrilovic et al., 2024).

Machine Learning Model	MSE	MAE	R² Score
Random Forest (UV Sensor)	0.10	0.07	0.93
Random Forest (BH1750 Light Sensor)	0.10	0.04	0.93
Decision Tree (UV Sensor)	0.13	0.07	0.90
Decision Tree (BH1750 Light Sensor)	0.13	0.04	0.89

The Random Forest model consistently delivered higher accuracy, making it the preferred choice for solar panel prediction on edge devices (Dobrilovic et al., 2024).

4. The Role of Edge Computing in Solar Panel Performance Optimization

Why Edge Intelligence Matters in Solar Energy Management

Most solar monitoring systems rely on cloud-based processing. This approach works, but it creates latency issues and eats up bandwidth when thousands of sensors are transmitting data at once (Dobrilovic et al., 2024).

Edge intelligence (EI) solves this problem by moving solar data processing directly to the sensor nodes, eliminating delays and unnecessary network congestion. Instead of sending raw data to a cloud server, sensors process information on-site, providing real-time solar performance estimates without waiting for a cloud response.

How Edge Computing Reduces Latency and Bandwidth Usage

By integrating ML directly into sensor nodes, researchers cut down on response time and energy consumption in solar-powered networks. This method offers several advantages:

• Instant power efficiency analysis, removing the need for cloud processing.
• Faster decision-making, allowing IoT nodes to adjust energy usage on-site.
• Reduced bandwidth demand, preventing data overload in large-scale networks.

By embedding AI models into ESP8266-based sensor devices, researchers successfully built smart solar forecasting tools that operate in real time without requiring external monitoring systems (Dobrilovic et al., 2024).

Deploying Machine Learning Models on ESP8266-Based Arduino Boards

To prove that AI-powered solar estimation works on real sensor nodes, researchers ported trained ML models onto ESP8266-based Arduino boards. Using the Micromlgen library, they embedded Random Forest and Decision Tree regressors directly into the sensor node firmware (Dobrilovic et al., 2024).

The ESP8266 platform was chosen because it offers:

• Wi-Fi connectivity, enabling seamless data transmission.
• Low power consumption, making it ideal for solar-powered systems.
• Compatibility with TinyML frameworks, allowing AI deployment in embedded systems.

This successful implementation confirmed that IoT sensors can predict solar panel voltage purely based on UV and light intensity data, unlocking new possibilities for smart solar energy optimization

5. How Accurate Is AI-Driven Solar Estimation?

Comparing Machine Learning Models for Solar Panel Prediction

Researchers tested multiple ML models to determine which technique best estimates solar panel voltage output. Two models stood out—Random Forest and Decision Tree regressors—both showing high accuracy when trained on UV and BH1750 light sensor data (Dobrilovic et al., 2024).

The goal was to predict solar panel voltage generation based only on ambient conditions, rather than requiring direct voltage readings from the panels themselves. This approach allows sensor nodes to estimate solar performance at any location, even if they don’t have a built-in solar panel.

Machine Learning Model	MSE	MAE	R² Score
Random Forest (UV Sensor)	0.10	0.07	0.93
Random Forest (BH1750 Light Sensor)	0.10	0.04	0.93
Decision Tree (UV Sensor)	0.13	0.07	0.90
Decision Tree (BH1750 Light Sensor)	0.13	0.04	0.89

The Random Forest model consistently performed better, showing higher prediction accuracy with lower error margins, making it the preferred choice for ML-powered solar forecasting (Dobrilovic et al., 2024).

Evaluating Accuracy Metrics for Solar Power Estimation

To validate these models, researchers measured Mean Squared Error (MSE), Mean Absolute Error (MAE), and R² Score, which are industry-standard metrics for assessing ML performance.

• Mean Squared Error (MSE): Represents the average squared difference between predicted and actual values. Lower MSE indicates higher accuracy.
• Mean Absolute Error (MAE): Measures the absolute difference between predicted and actual values, providing a clear error analysis.
• R² Score (Coefficient of Determination): Shows how well the model explains variations in solar panel output. The closer to 1.0, the better the model performs.

Model	MSE (Python vs. ESP8266)	MAE (Python vs. ESP8266)	R² (Real vs. Predicted)
RF (UV Sensor)	0.10 vs. 0.10	0.07 vs. 0.07	0.93
DT (UV Sensor)	0.13 vs. 0.13	0.07 vs. 0.07	0.90
RF (BH1750 Sensor)	0.10 vs. 0.10	0.04 vs. 0.04	0.93
DT (BH1750 Sensor)	0.13 vs. 0.13	0.04 vs. 0.04	0.89

The ESP8266 implementation matched Python-based estimates, demonstrating that ML-powered solar prediction works reliably on edge devices. This confirms that IoT networks can use AI-driven solar estimation without requiring cloud-based computation (Dobrilovic et al., 2024).

How ML Predicts Solar Panel Output Without Direct Solar Sensors

One of the biggest breakthroughs in AI-driven solar estimation is predicting voltage output without needing direct panel measurements. Traditional monitoring requires each sensor node to have a dedicated solar panel, which isn’t always feasible.

With ML-based forecasting:

• Sensor nodes analyze environmental data (UV radiation, visible light).
• AI models learn patterns in solar power generation based on past observations.
• Predictions give accurate voltage estimations, even in locations without existing solar infrastructure.

This technique allows existing sensor networks to gauge solar potential before installing panels, helping engineers and businesses optimize solar deployment strategies (Dobrilovic et al., 2024).

6. Benefits of AI-Based Solar Performance Monitoring

1. Improved Efficiency in Tracking Solar Power Fluctuations

Solar panels don’t always operate at maximum efficiency. Factors like weather changes, panel aging, and shifting sun positions cause variations in power output. AI-driven monitoring provides real-time predictions, helping IoT devices adjust energy usage dynamically (Dobrilovic et al., 2024).

Instead of relying on fixed threshold-based alerts, ML models:

• Identify performance patterns over time.
• Predict solar dips and spikes before they happen.
• Optimize energy allocation for IoT devices.

With these improvements, IoT networks avoid unnecessary power shortages, ensuring continuous operation for smart homes, industrial automation, and sensor-based agriculture.

2. Cost-Effective Integration of Solar Monitoring in IoT Networks

ML-based estimation eliminates the need for dedicated voltage sensors, making solar forecasting more scalable and budget-friendly. Traditional monitoring requires extra hardware, increasing costs for large IoT deployments.

By using existing sensors (UV and BH1750 light sensors) and machine learning models, solar power estimation is:

• Cheaper—no need for specialized voltage measurement devices.
• Scalable—AI models can be applied across thousands of sensor nodes.
• Autonomous—IoT devices make energy decisions locally without cloud reliance.

With cost reductions, businesses and researchers can deploy solar-powered IoT solutions more efficiently, expanding their use in smart agriculture, industrial monitoring, and environmental sensing (Dobrilovic et al., 2024).

3. AI Helps Sensor Nodes Adapt to Changing Environmental Conditions

Unlike traditional solar systems that react only after a voltage drop occurs, AI-enhanced estimation allows sensor nodes to predict changes in solar power output before they happen.

This helps IoT networks:

• Adapt power usage dynamically based on sunlight conditions.
• Prevent energy shortages in low-light environments.
• Maximize efficiency without needing manual intervention.

AI-powered solar forecasting ensures longer device lifespans, fewer maintenance issues, and more reliable energy allocation, making it a game-changer for IoT deployments (Dobrilovic et al., 2024).

7. Challenges and Future Innovations in AI-Powered Solar Estimation

What is Holding Machine Learning Back from Perfect Solar Predictions?

While machine learning has revolutionized solar forecasting, it is not yet perfectly accurate. Some factors limit AI’s ability to provide flawless predictions.

1. Changing Weather Conditions – Solar panel efficiency fluctuates with seasonal changes, unexpected cloud cover, and dust accumulation, making long-term predictions harder to maintain (Dobrilovic et al., 2024).
2. Limited Data – AI models need consistent, high-quality data to refine predictions. Gaps in sensor logging can lead to errors in forecasting (Dobrilovic et al., 2024).
3. Processing Power Limits – IoT devices operate on low-energy microcontrollers like ESP8266, which are great for edge computing but cannot handle heavy AI computations like cloud-based systems (Dobrilovic et al., 2024).

Machine learning models work well for short-term solar predictions, but improving long-term forecasting will require smarter AI models, better hardware, and richer datasets.

How AI-Powered Optimization is Changing Grid-Free Sensor Networks

Most IoT sensor networks are deployed in remote areas, far away from traditional power grids. This makes them reliant on batteries or solar panels for power. AI-driven solar forecasting helps sensor nodes become self-sufficient by:

• Predicting when solar panels will generate peak voltage, ensuring better energy distribution.
• Adjusting power consumption dynamically, reducing wasted electricity.
• Ensuring IoT devices operate autonomously, without needing human intervention (Dobrilovic et al., 2024).

AI-powered optimization makes solar-powered sensors more reliable, especially in agriculture, environmental monitoring, and smart city applications.

Future Innovations in TinyML and AI-Powered Solar Forecasting

TinyML in Solar Optimization

TinyML is a breakthrough in machine learning for low-power IoT devices. It allows AI models to run directly on microcontrollers like ESP8266 and ESP32 without needing cloud servers.

Benefits of TinyML in solar power estimation:

• Processes data locally, reducing lag in solar predictions.
• Uses ultra-low power, making it ideal for grid-free IoT systems.
• Improves real-time solar optimization, adjusting energy usage instantly (Dobrilovic et al., 2024).

TinyML is going to change how we track solar power, making sensor networks smarter and more efficient.

AI Meets Solar Energy: Smarter Forecasting is Coming

Future AI models will go beyond simple voltage predictions. Advanced machine learning techniques—like deep learning and AI-driven sensor fusion—will improve long-term forecasting and seasonal energy predictions (Dobrilovic et al., 2024).

Here’s what is next for AI-driven solar optimization:

• Seasonal adaptation, ensuring AI models work year-round.
• Multi-sensor fusion, combining temperature, UV radiation, and air quality data for better accuracy.
• Reinforcement learning, allowing solar-powered IoT systems to self-optimize based on historical data trends (Dobrilovic et al., 2024).

With these advances, solar forecasting will become more intelligent, powering next-generation smart cities and off-grid sensor networks.

8. Conclusion Why AI is the Future of Solar Energy Optimization

How Machine Learning is Changing Solar Panel Performance Tracking

Machine learning has transformed how we monitor solar panels. Instead of relying on traditional sensors, AI models predict voltage output using environmental data, making solar forecasting cheaper, more scalable, and more reliable (Dobrilovic et al., 2024).

Key benefits of AI-powered solar monitoring:

• Cuts costs by eliminating expensive voltage monitoring hardware.
• Boosts accuracy in predicting solar panel performance.
• Optimizes energy usage across IoT sensor networks.

How AI is Helping Smart Cities, Farms, and Factories

AI-powered solar forecasting is already impacting multiple industries, including:

• Smart Cities – AI-enhanced solar streetlights and urban monitoring systems (Dobrilovic et al., 2024).
• Smart Agriculture – AI-powered solar sensors for irrigation and crop health monitoring (Dobrilovic et al., 2024).
• Industrial IoT – Solar-powered AI optimization for factories and logistics operations (Dobrilovic et al., 2024).

Why Businesses Should Invest in AI-Powered Solar Management

For companies, researchers, and engineers, now is the time to adopt AI-driven solar power solutions. Here’s why:

• AI improves solar efficiency, maximizing energy output.
• TinyML reduces the cost of solar-powered IoT networks.
• AI-based forecasting helps businesses optimize renewable energy usage.

With machine learning and edge computing, the future of solar-powered IoT networks is smarter, more efficient, and more sustainable than ever before.

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Reference: Dobrilovic, D.; Pekez, J.; Ognjenovic, V.; Desnica, E. Analysis of Using Machine Learning Techniques for Estimating Solar Panel Performance in Edge Sensor Devices. Appl. Sci. 2024, 14, 1296. https://doi.org/10.3390/app14031296

CC BY 4.0 License: This article is distributed under the terms of the Creative Commons Attribution (CC BY) license, allowing unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited. License link: https://creativecommons.org/licenses/by/4.0/