What does this research mean for the field?

A self-tuning reinforcement learning-driven Isolation Forest framework improves anomaly detection in solar power plants, achieving accuracy, recall, and F1 scores of 0.94, 0.99, and 0.97, respectively. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The aim is to enhance anomaly detection in solar power systems using a self-tuning reinforcement learning approach.

March 2, 2026Open Access

A Self-Tuning Reinforcement Learning–Driven Isolation Forest Framework for Scalable and Efficient Anomaly Detection in Solar Power Plants

Key Points

The aim is to enhance anomaly detection in solar power systems using a self-tuning reinforcement learning approach.
Preprocessed data for anomaly detection.
Trained a reinforcement learning agent using Proximal Policy Optimization.
Evaluated the RL-tuned Isolation Forest model's performance.
Examined detected anomalies to assess effectiveness.
Achieved accuracy of 0.94, recall of 0.99, and F1 score of 0.97.
Outperformed traditional anomaly detection methods like neural networks.
Demonstrated significant improvement in identifying anomalies accurately.
Supported the adaptability of the model to evolving data patterns.

Abstract

Anomaly detection plays a vital role in ensuring the stability and efficiency of the solar power production, where the accurate monitoring of the abnormality patterns can prevent failures and improve the performance. The proposed work introduced a new approach to anomaly detection through the integration of reinforcement learning (RL) and the Isolation Forest algorithm to improve the performance of anomaly detection in solar energy generation systems. This study aims to develop an adaptive and scalable solution to maximize the contamination rate of Isolation Forest model dynamically with Proximal Policy Optimization (PPO) based on a RL agent. The RL agent modulates the contamination rate of the Isolation Forest model through adaptive learning, enabling the system to adjust and evolving data patterns without manual intervention. It includes four main steps: (1) preprocessing the data (2) training the RL agent to maximize the contamination rate to best detect anomalies, (3) evaluating the performance of the RL-tuned Isolation Forest model (4) examining the detected anomalies. This technique is novel in that the adaptation of a key parameter (contamination rate) of the Isolation Forest algorithm (usually set manually or by heuristic), is performed via RL to make the system more adaptable and adaptive. The proposed model outperforms other mainstream anomaly detection models, e.g., neural networks and mainstream machine learning methodologies, achieving accuracy, recall, and F1 score of 0.94, 0.99, and 0.97, respectively. The results support the ability of the model to identify anomalies significantly below a balanced performance on several measures. The results of the current research point to the possibilities of applying reinforcement learning to the optimization of anomaly detection algorithms with an available scalable approach that can adapt to evolve and react according to the emergent and advanced trends in the operation of solar energy production and even in the overall renewable energy control systems.

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