In this study, an integrated forecasting model was developed by combining the ensemble empirical mode decomposition (EEMD) model and gated recurrent unit (GRU) neural network to accurately predict the rooftop solar power output at a specific power unit located in Tay Ninh province, Vietnam. The EEMD method was employed to decompose the solar power signals into multiple frequencies, allowing for a more comprehensive analysis. Subsequently, the GRU network, known for its ability to capture long-term dependencies, was utilized to forecast future values for each decomposition series. By merging the forecasted values obtained from the decomposition series, the final prediction for the solar power output was generated. To evaluate the efficacy of our proposed approach, a comparative analysis was undertaken against other forecasting models, including a single artificial neural network, long short-term memory network, and GRU, all of which solely considered the solar power series as input features. The experimental results provided compelling evidence of the superior performance of the EEMD-GRU model, especially when incorporating weather variables into the forecasting process, achieving the best results in all three forecasting scenarios (1-step, 2-step, and 3-step). For both forecasting targets, Inverter 155 and 156, the n-RMSE indices were 1.35%, 3.5%, and 4.8%, respectively, significantly lower than the compared single models. This integration of weather variables enhances the model's accuracy and reliability in predicting rooftop solar power output, establishing it as a valuable tool for efficient energy management in the region.

中文翻译：

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使用 EEMD-GRU 组合数据分解和深度学习模型对屋顶太阳能发电进行短期多步预测

在本研究中，通过结合集合经验模态分解（EEMD）模型和门控循环单元（GRU）神经网络开发了一个综合预测模型，以准确预测位于越南西宁省特定发电厂的屋顶太阳能发电量。采用EEMD方法将太阳能信号分解为多个频率，以便进行更全面的分析。随后，以捕获长期依赖性的能力而闻名的 GRU 网络被用来预测每个分解序列的未来值。通过合并从分解序列获得的预测值，生成太阳能输出的最终预测。为了评估我们提出的方法的有效性，我们与其他预测模型进行了比较分析，包括单个人工神经网络、长期短期记忆网络和 GRU，所有这些模型都只将太阳能系列作为输入特征。实验结果为 EEMD-GRU 模型的优越性能提供了令人信服的证据，特别是在将天气变量纳入预报过程时，在所有三种预报场景（1 步、2 步和 3 步）中均取得了最佳结果。对于逆变器 155 和 156 两个预测目标，n-RMSE 指数分别为 1.35%、3.5% 和 4.8%，显着低于比较的单一模型。这种天气变量的整合提高了模型预测屋顶太阳能输出的准确性和可靠性，使其成为该地区高效能源管理的宝贵工具。