The energy load data in the micro-energy network are a time series with sequential and nonlinear characteristics. This paper proposes a model based on the encode-decode architecture and ConvLSTM for multi-scale prediction of multi-energy loads in the micro-energy network. We apply ConvLSTM, LSTM, attention mechanism and multi-task learning concepts to construct a model specifically for processing the energy load forecasting of the micro-energy network. In this paper, ConvLSTM is used to encode the input time series. The attention mechanism is used to assign different weights to the features, which are subsequently decoded by the decoder LSTM layer. Finally, the fully connected layer interprets the output. This model is applied to forecast the multi-energy load data of the micro-energy network in a certain area of Northwest China. The test results prove that our model is convergent, and the evaluation index value of the model is better than that of the multi-task FC-LSTM and the single-task FC-LSTM. In particular, the application of the attention mechanism makes the model converge faster and with higher precision.

中文翻译：

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基于ConvLSTM和编码器-解码器的多任务预测模型

微能源网络中的能源负荷数据是具有时序和非线性特征的时间序列。本文提出了一种基于编码-解码架构和ConvLSTM的模型，用于微能量网络中多能量负荷的多尺度预测。我们应用ConvLSTM，LSTM，注意力机制和多任务学习概念来构建专门用于处理微能源网络的能源负荷预测的模型。在本文中，ConvLSTM用于编码输入时间序列。注意机制用于为特征分配不同的权重，这些权重随后由解码器LSTM层解码。最后，完全连接的层将解释输出。该模型用于预测西北某地区微能源网络的多能源负荷数据。测试结果证明我们的模型是收敛的，并且该模型的评估指标值优于多任务FC-LSTM和单任务FC-LSTM。特别地，注意力机制的应用使得模型收敛更快并且具有更高的精度。