基于EMD-TCN的多能供热系统太阳辐照度预测模型

SOLAR IRRADIANCE FORECASTING MODEL BASED ON EMD-TCN FOR MULTI-ENERGY HEATING SYSTEMS

针对太阳辐照度的非平稳性和非线性影响多能供热系统运行效率和可靠性问题,该文提出一种基于经验模态分解(EMD)和时间卷积网络(TCN)的太阳辐照度混合预测模型EMD-TCN,更精准地从气象数据中提取太阳辐照度非线性和非平稳的隐含特征,获得更佳的预测精度。该研究利用逐时气象数据对所提出的EMD-TCN模型进行不同时间尺度的太阳辐照度预测实验,并与4种主流深度学习预测算法进行对比分析,结果表明该太阳辐照度预测模型具有更高的预测精度和泛化能力。

Aiming at the problem that the non-stationary and non-linear of solar irradiance affects the operation efficiency and reliability of multi-energy heating system,this paper proposes a hybrid forecasting model of solar irradiance based on empirical mode decomposition(EMD)and temporal convolutional network(TCN)named EMD-TCN,which can extract the hidden features of nonlinear and non-stationary of solar irradiance from meteorological data more accurately,and obtain better prediction accuracy.The proposed EMD-TCN model is used to predict solar irradiance at different time scales using hourly meteorological data,and compared with four mainstream deep learning prediction algorithms.The results show that the proposed solar irradiance prediction model has higher prediction accuracy and generalization ability.