Operation data-driven energy efficiency optimization algorithms for distributed photovoltaic generation systems

This article focuses on the energy efficiency optimization problem in the operation of distributed photovoltaic power generation systems and proposes an intelligent optimization algorithm based on operation data-driven approach. With the large-scale integration of distributed photovoltaics into the distribution network, their operational efficiency is easily affected by multiple factors such as equipment aging, environmental disturbances, and control strategies, and there is an urgent need for efficient and adaptive optimization methods. Therefore, this article constructs a deep learning (DL) model that integrates long short-term memory networks (LSTM) and reinforcement learning (RL). This model collects key operating parameters such as grid load and power distribution, effectively extracts dynamic features from time series data using LSTM, and combines RL intelligence to explore optimal scheduling strategies. On this basis, a complete learning framework including state space, action space, and reward function was designed to achieve real-time perception of system operating status and energy efficiency optimization decision-making. The results indicate that the proposed method can significantly improve the overall operational energy efficiency of distributed photovoltaic power generation systems, demonstrating good adaptability and robustness in complex and changing practical operating environments.