In the pursuit of optimizing photovoltaic (PV) systems, we conducted a comprehensive evaluation of seven machine learning algorithms. Our study aimed to predict maximum power output efficiently and accurately. The algorithms examined included Linear Regression (LR), Ridge Regression (RR), Lasso Regression (Lasso R), Bayesian Ridge Regression (BR), Decision Tree Regression (DTR), Gradient Boosting Regression (GBR), and Artificial Neural Networks (ANN).
Algorithm Performance
Figure 9 summarizes the algorithms’ performance during training, revealing their high effectiveness in predicting maximum current (Im), voltage (Vm), and power (Pm). With R2 values exceeding 0.97, DTR stands out with an impressive R2 of 0.99999 for current, voltage, and power predictions. Although training times were consistent across algorithms, ANN recorded the longest time at 1.059 seconds.
Accuracy Metrics
Fig. 14b highlights all algorithms’ strong performance based on Mean Absolute Error (MAE), with low values predicted for Im, Vm, and Pm. DTR continues to excel with MAE values of 0.00001 for Im and Vm and 0.0063 for Pm. Similarly, Fig. 14c demonstrates that Root Mean Square Error (RMSE) values were minimal for all models, with DTR achieving the highest prediction accuracy.
Challenges and Strategic Approaches
The study highlights that while DTR, BRR, GBR, and ANN increase the model’s explanatory power, their interpretability remains a concern. Machine learning models often lack transparency, necessitating correlation and significance analysis to improve understanding.
Particular challenges associated with DTR and Gradient Boosting models include overfitting and computational costs. Strategic measures such as pruning, cross-validation, and hyperparameter optimization considerably aided in overcoming these limitations. Moreover, efficient data collection and rigorous maintenance procedures were implemented to ensure optimal model performance.
Data Analysis and Statistical Testing
We focused on solar cells and utilized collected data samples to predict maximum power output. Fig. 16 shows the relationship between different datasets, informed by correlation analyses utilizing Pearson methods.
Conducting an Analysis of Variance (ANOVA) test offered insights into RMSE values across models. With a p-value of 0.9097, we determined there were no statistically significant differences, reinforcing our algorithm’s robustness despite observed variances.
Historical Data and Normalization
Our dataset was divided into an 80:20 ratio for training and testing. Before modeling, input and output parameters were normalized within a -1 to 1 range to ensure accuracy. This preprocessing step facilitated the creation of reliable data-driven models.
Observational insights suggested standard and skewed distributions across different parameters like irradiance and temperature. ANOVA results underscored DTR’s ability to achieve minimal RMSE values in Im, Vm, and Pm predictions, supporting its high precision and strength.
Predictive Performance and Visualization
Our predictive performance metrics were corroborated by observing key figures, notably Fig. 15 showcasing DTR’s excellent performance with near-zero NRMSE values. Complemented by the GBR model’s robust performance, a compelling quantitative basis for comparisons was established.
Efforts to visualize and analyze residuum through histogram distributions revealed distributions and standard deviations of parameters involved in modeling, underpinning reliable model performance.
These visual representations, along with data analytics, supported the comprehensive understanding of our study’s findings.
Machine Learning Impact
The successful deployment and integration of our research findings could lead to implementing intelligent expert systems. By leveraging DTR’s predictive power for maximum power point (MPP) tracking, PV systems’ efficiency can be greatly enhanced, saving both time and resources.
Our DTR model demonstrated outstanding potential with superior predictive performance resulting in an R2 closer to 1 compared to its counterparts when evaluated with testing datasets. This underscores the DTR model’s efficacy in advancing ML applications in PV systems.
Conclusion
This comprehensive study on evaluating machine learning models for predicting PV system power output underscores the value of DTR in achieving high accuracy and minimizing errors. Despite facing intricacies relevant to computation and interpretability, strategic measures ensured generalizability and model robustness.
Emphasizing DTR’s efficiency provides a promising outlook for future advancements within this domain. Our findings illustrate the tangible benefits of these methodologies, promising a cost-effective and accurate pathway to enhancing PV system output under various environmental conditions.
