Machine Learning Prediction of Photovoltaic Hydrogen Production Capacity Using Long Short-Term Memory Model

作者：Qian He, Mingbin Zhao, Shujie Li, Xuefang Li, Zuoxun Wang · 发表于：Energies · 年份：2025 · DOI：10.3390/en18030543 · 被引用次数：10 · 研究领域：Solar Radiation and Photovoltaics、Power Systems and Renewable Energy、Hybrid Renewable Energy Systems

The yield of photovoltaic hydrogen production systems is influenced by a number of factors, including weather conditions, the cleanliness of photovoltaic modules, and operational efficiency. Temporal variations in weather conditions have been shown to significantly impact the output of photovoltaic systems, thereby influencing hydrogen production. To address the inaccuracies in hydrogen production capacity predictions due to weather-related temporal variations in different regions, this study develops a method for predicting photovoltaic hydrogen production capacity using the long short-term memory (LSTM) neural network model. The proposed method integrates meteorological parameters, including temperature, wind speed, precipitation, and humidity into a neural network model to estimate the daily solar radiation intensity. This approach is then integrated with a photovoltaic hydrogen production prediction model to estimate the region’s hydrogen production capacity. To validate the accuracy and feasibility of this method, meteorological data from Lanzhou, China, from 2013 to 2022 were used to train the model and test its performance. The results show that the predicted hydrogen production agrees well with the actual values, with a low mean absolute percentage error (MAPE) and a high coefficient of determination (R2). The predicted hydrogen production in winter has a MAPE of 0.55% and an R2 of 0.985, while the predicted hydrogen production in summer has a slightly higher MAPE of ...