Optimizing solar power plant efficiency through advanced analytical framework and comparative analysis

Abstract

As the world embraces the transition towards renewable energy, the optimization of solar power plants becomes paramount. In this research, we present a comprehensive framework that leverages advanced analytical methodologies to address critical operational challenges and elevate the efficiency of solar power generation. Our framework encompasses data preprocessing, time series analysis, anomaly detection, and equipment performance assessment, synergistically combining their strengths to offer a holistic solution. The heart of our proposed approach lies in the precision and efficacy of anomaly detection. We introduce two powerful techniques—LSTM Autoencoder and Isolation Forest—to identify anomalies and equipment underperformance. Through meticulous evaluation, we showcase their comparative performance, revealing the nuanced strengths of each. Visualizations depict the model’s proficiency in pinpointing anomalies, with LSTM Autoencoder emerging as a standout performer, adept at capturing even subtle deviations from expected patterns. Our research extends beyond detection to equip stakeholders with real-time insights. The visualization of daily yield trends uncovers potential data anomalies, enabling timely intervention and rectification. Additionally, we address equipment failures by harnessing random forest modeling to establish a robust relationship between irradiance, temperature, and DC power. This approach provides a powerful tool for real-time condition monitoring and fault detection, enabling proactive maintenance and enhancing operational resilience.