Wang Senyao, Ma Jin
School of Electrical and Computer Engineering, The University of Sydney, Sydney, NSW, 2008, Australia.
Sci Rep. 2023 Sep 13;13(1):15113. doi: 10.1038/s41598-023-42153-7.
Despite being a clean and renewable energy source, photovoltaic (PV) power generation faces severe challenges in operation due to its strong intermittency and volatility compared to the traditional fossil fuel power generation. Accurate predictions are therefore crucial for PV's grid connections and the system security. The existing methods often rely heavily on weather forecasts, the accuracy of which is hard to be guaranteed. This paper proposes a novel GBDT-BiLSTM day-ahead PV forecasting model, which leverages the Teacher Forcing mechanism to combine the strong time-series processing capabilities of BiLSTM with an enhanced GBDT model. Given the uncertainty and volatility inherent in solar energy and weather conditions, the gradient boosting method is employed to update the weak learner, while a decision tree is incorporated to update the strong learner. Additionally, to explore the correlation between photovoltaic power output and historical time-series data, the adaptive gradient descent-based Adam algorithm is utilized to train the bidirectional LSTM model, enhancing the accuracy and stability of mid- to long-term time-series predictions. A prediction experiment, conducting with the real data from a PV power station in Sichuan Province, China, was compared with other methods to verify the model's effectiveness and robustness.
尽管光伏发电是一种清洁的可再生能源，但与传统化石燃料发电相比，由于其强烈的间歇性和波动性，光伏发电在运行中面临严峻挑战。因此，准确的预测对于光伏并网和系统安全至关重要。现有方法通常严重依赖天气预报，而天气预报的准确性难以保证。本文提出了一种新颖的GBDT-BiLSTM提前一天光伏预测模型，该模型利用教师强制机制将BiLSTM强大的时间序列处理能力与增强的GBDT模型相结合。鉴于太阳能和天气条件固有的不确定性和波动性，采用梯度提升方法更新弱学习器，同时引入决策树更新强学习器。此外，为了探索光伏发电输出与历史时间序列数据之间的相关性，利用基于自适应梯度下降的Adam算法训练双向LSTM模型，提高中长期时间序列预测的准确性和稳定性。利用中国四川省某光伏电站的实际数据进行了预测实验，并与其他方法进行了比较，以验证该模型的有效性和鲁棒性。
