1. Introduction

In solar home energy storage systems, photovoltaic (PV) panels are the core components for converting solar energy into electricity. However, over time, PV panels experience light - induced degradation, commonly known as light decay. Light decay leads to a gradual decrease in the power output of PV panels, reducing the overall efficiency and energy generation capacity of the solar home energy storage system. This phenomenon not only affects the economic benefits of homeowners relying on solar energy but also poses challenges to the stable operation of the energy storage system. To address these issues, research on light decay compensation algorithms has become crucial. Such algorithms aim to optimize the performance of PV panels by compensating for the power loss caused by light decay, ensuring the long - term reliability and efficiency of solar home energy storage systems.

2. Mechanisms of Light Decay in PV Panels

2.1 Photovoltaic Material Degradation

The degradation of photovoltaic materials is one of the primary causes of light decay. In silicon - based PV panels, which are the most commonly used in solar home energy storage systems, various physical and chemical processes occur under sunlight exposure. For instance, the presence of impurities and defects in the silicon crystal lattice can interact with photons, leading to the generation of electron - hole pairs. Over time, these interactions can cause the formation of new defects, such as dislocations and vacancies, in the lattice structure. These defects act as recombination centers for electrons and holes, reducing the number of charge carriers that can be effectively collected and converted into electricity.

In thin - film PV panels, which use materials like cadmium telluride (CdTe) or copper indium gallium selenide (CIGS), light - induced degradation mechanisms are different but equally significant. In CdTe panels, for example, the formation of oxygen - related defects at the surface of the CdTe layer can reduce the efficiency of the photovoltaic conversion process. These defects can alter the energy band structure of the material, affecting the separation and collection of charge carriers.

2.2 Environmental Factors

Environmental factors also play a crucial role in accelerating light decay. High temperatures can increase the rate of chemical reactions within the PV panel materials, leading to faster degradation. For every 1°C increase in temperature above the standard test conditions (usually 25°C), the power output of a silicon - based PV panel can decrease by approximately 0.3 - 0.5%. Additionally, exposure to moisture, dust, and pollutants in the air can cause surface degradation of the PV panels. Moisture can penetrate the panel encapsulation, leading to corrosion of the electrical contacts and degradation of the encapsulation materials. Dust and pollutants can accumulate on the surface of the panels, reducing the amount of sunlight that reaches the photovoltaic materials and also causing mechanical abrasion over time.

2.3 Aging of Encapsulation and Packaging Materials

The encapsulation and packaging materials of PV panels, such as ethylene - vinyl acetate (EVA) films used for encapsulation and the backsheet materials, also age over time. EVA films can yellow and lose their transparency due to photo - oxidation, reducing the amount of sunlight that can pass through to the photovoltaic cells. The backsheet materials, which protect the PV cells from the external environment, can crack, peel, or degrade under long - term exposure to sunlight, moisture, and temperature fluctuations. These issues not only compromise the mechanical integrity of the PV panels but also contribute to light decay by affecting the optical and electrical performance of the cells.

3. Existing Light Decay Compensation Algorithms

3.1 Temperature - Based Compensation Algorithms

One category of existing light decay compensation algorithms focuses on temperature - based compensation. These algorithms take into account the negative impact of temperature on the power output of PV panels. They typically use a temperature coefficient, which is a measure of how the power output of a PV panel changes with temperature. For example, a common approach is to measure the temperature of the PV panel using a temperature sensor installed on or near the panel. Based on the measured temperature and the known temperature coefficient, the algorithm adjusts the expected power output of the panel. If the temperature is higher than the standard test temperature, the algorithm reduces the predicted power output to account for the temperature - induced power loss. However, these algorithms often assume a linear relationship between temperature and power loss, which may not accurately represent the complex behavior of PV panels under real - world conditions.

3.2 Time - Series Prediction Algorithms

Time - series prediction algorithms are another type of approach used for light decay compensation. These algorithms analyze historical power output data of PV panels over time. By using statistical methods such as autoregressive integrated moving average (ARIMA) models or exponential smoothing techniques, they attempt to predict the future power output of the panels, taking into account the trend of light decay. For instance, an ARIMA model can be trained on several years of daily power output data to identify patterns and trends in the data. Based on these patterns, the model can predict the power output for future days or weeks, allowing for proactive compensation measures to be taken. However, these algorithms may struggle to adapt quickly to sudden changes in environmental conditions or unforeseen factors that can affect the light decay rate.

3.3 Machine Learning - Based Algorithms

In recent years, machine learning - based algorithms have gained popularity in light decay compensation research. Neural network models, such as feed - forward neural networks and recurrent neural networks, have been used to model the complex relationship between various factors (such as temperature, irradiance, humidity, and panel age) and the power output of PV panels. These models can learn from large amounts of data collected from PV panels in different environments and operating conditions. For example, a neural network can be trained on data from hundreds of PV panels installed in different regions, with each data point including information about the panel's characteristics, environmental conditions at the time of measurement, and the corresponding power output. Once trained, the neural network can predict the power output of a PV panel more accurately than traditional algorithms, taking into account the non - linear relationships between the input factors and the output. However, machine learning algorithms often require a large amount of data for training, and there is a risk of overfitting if the model is too complex or the training data is not representative.

4. Design of a Novel Light Decay Compensation Algorithm

4.1 Algorithm Framework

The proposed novel light decay compensation algorithm combines the advantages of multiple existing approaches. It is based on a hybrid model that integrates physical - based models with machine learning techniques. The algorithm framework consists of three main components: a data collection module, a feature extraction and preprocessing module, and a compensation prediction module.

The data collection module gathers real - time data from various sensors installed on the PV panels and in the surrounding environment. These sensors measure parameters such as irradiance, temperature, humidity, wind speed, and dust concentration. Additionally, historical data on the PV panel's power output, age, and maintenance records are also collected and stored in a database.

The feature extraction and preprocessing module processes the collected data to extract relevant features that are likely to have an impact on light decay and power output. For example, it calculates the daily average irradiance, the maximum and minimum temperatures during a day, and the rate of change of these parameters over time. The preprocessing step also includes data normalization and outlier removal to ensure the quality of the data used in the algorithm.

The compensation prediction module is the core of the algorithm. It uses a combination of a physical - based model, which describes the fundamental physical processes of light decay in PV panels, and a machine learning model, specifically a long short - term memory (LSTM) neural network. The physical - based model provides a basic understanding of how the PV panel's power output should change under normal conditions based on the known physical properties of the materials. The LSTM neural network, on the other hand, is trained on the preprocessed data to capture the complex non - linear relationships between the input features and the power output, as well as to account for the effects of unforeseen factors and random variations.

4.2 Algorithm Implementation

To implement the algorithm, first, the data collection module continuously monitors the sensors and updates the database with new data. The feature extraction and preprocessing module then processes the newly collected data in real - time. The physical - based model calculates the expected power output of the PV panel based on the current environmental conditions and the panel's characteristics, assuming no light decay. At the same time, the LSTM neural network uses the preprocessed data as input to predict the actual power output of the panel, taking into account the effects of light decay and other influencing factors.

The difference between the expected power output from the physical - based model and the predicted power output from the LSTM neural network represents the amount of power loss due to light decay. The algorithm then generates a compensation signal, which can be used to adjust the operation of the solar home energy storage system. For example, if the power loss is significant, the algorithm can adjust the charging and discharging strategy of the energy storage batteries to optimize the overall energy utilization of the system.

4.3 Parameter Tuning and Optimization

To ensure the accuracy and effectiveness of the algorithm, parameter tuning and optimization are essential. For the LSTM neural network, parameters such as the number of layers, the number of neurons in each layer, and the learning rate need to be optimized. This is typically done using techniques such as cross - validation and grid search. In cross - validation, the training data is divided into multiple subsets, and the model is trained and evaluated on different combinations of these subsets to find the optimal parameter values that minimize the prediction error. Grid search involves systematically searching through a predefined set of parameter values to find the combination that results in the best performance of the model.

For the physical - based model, the parameters related to the physical properties of the PV panel materials, such as the temperature coefficient and the degradation rate constants, also need to be calibrated based on experimental data. This calibration process ensures that the physical - based model accurately represents the real - world behavior of the PV panels.

5. Experimental Setup and Results

5.1 Experimental Setup

To validate the performance of the proposed light decay compensation algorithm, an experimental setup was established. The setup included a set of PV panels installed in a real - world environment, equipped with various sensors for measuring irradiance, temperature, humidity, and other relevant parameters. The PV panels were connected to a solar home energy storage system, which included an inverter, energy storage batteries, and a data acquisition system.

The data acquisition system collected data from the sensors at regular intervals (e.g., every 15 minutes) and stored the data in a database. The proposed algorithm was implemented on a computer connected to the data acquisition system, and it continuously processed the collected data and generated compensation signals. For comparison, several existing light decay compensation algorithms, including the temperature - based algorithm, the ARIMA - based time - series prediction algorithm, and a simple feed - forward neural network - based algorithm, were also implemented and tested under the same experimental conditions.

5.2 Results and Analysis

The experimental results showed that the proposed hybrid light decay compensation algorithm outperformed the existing algorithms in terms of prediction accuracy. The root mean square error (RMSE) between the predicted power output and the actual measured power output was significantly lower for the proposed algorithm compared to the other algorithms. This indicates that the proposed algorithm was able to more accurately predict the power output of the PV panels, taking into account the complex effects of light decay and environmental factors.

In terms of the impact on the overall performance of the solar home energy storage system, the proposed algorithm also demonstrated better results. By using the compensation signals generated by the algorithm, the energy storage system was able to optimize its charging and discharging operations more effectively, resulting in a higher overall energy utilization rate. For example, during periods of significant light decay, the algorithm adjusted the charging strategy of the batteries to ensure that the available energy was stored and used in the most efficient way, reducing the reliance on grid - supplied electricity and increasing the self - sufficiency of the solar home energy storage system.

6. Challenges and Future Research Directions

6.1 Challenges

Despite the promising results of the proposed algorithm, there are still several challenges that need to be addressed. One of the main challenges is the high computational complexity of the algorithm, especially when using a large - scale LSTM neural network. This can lead to long processing times and high energy consumption, which may not be suitable for real - time applications in some solar home energy storage systems with limited computational resources.

Another challenge is the lack of comprehensive and accurate data for algorithm training. While the experimental setup collected data from a set of PV panels, the data may not be representative of all possible operating conditions and PV panel types. To improve the generalization ability of the algorithm, more extensive data collection from different regions, climates, and PV panel models is required.

6.2 Future Research Directions

Future research in light decay compensation algorithms for solar home energy storage systems could focus on several directions. One area of research could be the development of more efficient and lightweight machine learning models that can achieve high prediction accuracy with lower computational complexity. For example, exploring the use of sparse neural networks or model compression techniques to reduce the size and complexity of the LSTM neural network without sacrificing much of its performance.

Another direction could be the integration of more advanced sensor technologies and data fusion methods. By using new types of sensors, such as hyperspectral sensors, which can provide more detailed information about the PV panel materials and their degradation status, and by fusing data from multiple sensors, the algorithm can gain a more comprehensive understanding of the factors affecting light decay and improve its prediction accuracy.

In addition, research could also focus on the development of real - time monitoring and control systems that can automatically adjust the operation of the solar home energy storage system based on the compensation signals generated by the algorithm. This would further enhance the efficiency and reliability of the system and enable it to adapt more quickly to changes in light decay and environmental conditions.

7. Conclusion

Research on light decay compensation algorithms for solar home energy storage systems is of great significance for improving the efficiency and reliability of these systems. This article has explored the mechanisms of light decay in PV panels, analyzed existing compensation algorithms, and proposed a novel hybrid algorithm that combines physical - based models with machine learning techniques. The experimental results have shown that the proposed algorithm can more accurately predict the power output of PV panels and improve the overall performance of the solar home energy storage system. However, there are still challenges to be overcome, and future research should focus on improving the algorithm's efficiency, data collection, and integration with real - time monitoring and control systems. With continuous research and development, light decay compensation algorithms will play an increasingly important role in promoting the widespread adoption and sustainable development of solar home energy storage systems.