基于深度学习的光伏发电功率预测方法研究

Research on Photovoltaic Generation Power Prediction Method Based on Deep Learning

近几年新能源技术不断发展,光伏发电因具有绿色清洁、持续长久等优点得到了广泛应用,但同时其输出功率存在间歇性、随机性和突变性等特点,会对电网的稳定性带来负面影响,因此准确的功率预测对电网的稳定运行至关重要。随着人工智能的兴起,将深度学习网络技术与功率预测相结合,可得到高精度的预测结果。为此提出一种基于长短期记忆网络的深度学习方法,建立分时长短期记忆网络模型,从而实现了光伏发电功率的预测。该预测方法的推广应用为电网的稳定运行提供了可靠保证,有效提高了功率预测精度,具有很好的应用前景和现实的应用价值。

In recent years,new energy technology has been continuously developed.Photovoltaic power generation has been widely used because of its advantages of green,clean and long-lasting.However,its output power is intermittent,random and abrupt,which will negatively affect the stability of the power grid,so accurate power prediction is crucial for the stable operation of the power grid.With the rise of artificial intelligence,combining deep learning network technology with power prediction can obtain high-precision prediction results.Therefore,a deep learning method based on long short-term memory network is proposed,and a time-sharing long short-term memory network model is established,so as to realize the prediction of photovoltaic power generation.The promotion and application of this prediction method provides a reliable guarantee for the stable operation of the power grid,effectively improves the accuracy of power prediction,and has a good application prospect and practical application value.