# Intelligent Diagnosis of Photovoltaic Systems: An Engineering Comparison Study of Random Forest and SVM Models

> This article deeply analyzes a machine learning engineering practice for photovoltaic systems, comparing the performance of Random Forest and Support Vector Machine (SVM) in operating condition classification and power prediction tasks. Using a physics-based synthetic dataset and avoiding information leakage, the study verifies the advantages of Random Forest in nonlinear relationship modeling and class imbalance handling, providing a practical technical solution for intelligent monitoring of photovoltaic systems.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-05-18T20:15:24.000Z
- 最近活动: 2026-05-18T20:17:45.866Z
- 热度: 151.0
- 关键词: 光伏系统, 机器学习, 随机森林, SVM, 工况分类, 功率预测, 合成数据, 智能运维
- 页面链接: https://www.zingnex.cn/en/forum/thread/svm
- Canonical: https://www.zingnex.cn/forum/thread/svm
- Markdown 来源: floors_fallback

---

## [Main Floor] Introduction to Intelligent Diagnosis of Photovoltaic Systems: An Engineering Comparison Study of Random Forest and SVM Models

This article addresses the needs of intelligent operation and maintenance of photovoltaic systems, comparing the engineering performance of Random Forest and SVM models in operating condition classification and power prediction tasks. Using a physics-based synthetic dataset, the study verifies the advantages of Random Forest in nonlinear relationship modeling and class imbalance handling, providing a practical technical solution for intelligent monitoring of photovoltaic systems.

## Project Background and Core Challenges

Modern photovoltaic power plants face operation and maintenance challenges: anomalies such as photovoltaic panel occlusion, dust accumulation, and component failures affect power generation efficiency; accurate power prediction is important for grid dispatching. However, there are difficulties in machine learning applications: real operating condition data is scarce/confidential, the nonlinear relationship between environmental and electrical parameters is complex, and the distribution of fault samples is unbalanced.

## Data Construction: Physics-Based Synthetic Strategy

To solve the problem of real data, the project adopts a physics-based synthetic data strategy. The dataset includes environmental variables (irradiance, temperature, etc.), electrical variables (voltage, current, etc.), and target variables (operating condition categories). Synthetic data allows precise control of distribution, introduces physics-compliant noise and fault patterns, avoids privacy issues, and can eliminate information leakage.

## Task Definition and Model Selection Considerations

The project is divided into two tasks: 1. Operating condition classification (multi-class, class imbalance), comparing Random Forest classifier and SVC; 2. Power prediction (regression), comparing Random Forest regressor and SVR. Selection considerations: Random Forest (ensemble learning) reduces overfitting and is insensitive to feature scaling; SVM handles nonlinearity via kernel tricks and has good generalization when sample size is appropriate.

## Experimental Results: Performance Comparison Between Random Forest and SVM

Classification task: Random Forest achieved an accuracy of 73.9% and a macro-average F1 score of 0.735, outperforming SVM (which was slightly inferior in handling class imbalance); Regression task: Random Forest had an RMSE of 207.25 watts and an R² of 0.765 (explaining 76.5% of power variation), and was superior in feature interaction handling. SVM performance was greatly affected by kernel functions and parameter tuning.

## Engineering Practice Insights and Recommendations

Engineering insights: 1. Synthetic data is effective in scenarios where real data is limited, but it needs to reflect the statistical characteristics and physical constraints of real systems; 2. Traditional machine learning methods (such as Random Forest) are still competitive for structured data and small-scale samples, with fast training and strong interpretability; 3. Preventing information leakage, using independent test sets, and multi-dimensional evaluation are necessary conditions for reliable model operation.

## Future Outlook: Deepening Directions for Photovoltaic Intelligent Diagnosis

Future exploration directions: Introduce time series modeling to improve prediction accuracy; Try ensemble algorithms such as gradient boosting trees/XGBoost; Explore anomaly detection to identify unknown fault patterns. With the growth of photovoltaic installed capacity, machine learning has broad application prospects in the renewable energy field.