Abstract

Accurate prediction of electricity sale has a positive effect on power companies in rationally arranging power supply plans, scientifically optimizing power resource allocation, improving power management efficiency, saving energy and reducing consumption. Predicting future electricity sale based on historical electricity sale data can essentially be summarized as a time series forecasting problem. This paper proposes a fast and memory-efficient method, which adopts the expressive power of deep neural networks and the time characteristics of sequence-to-sequence structure (parallel convolution and recurrent neural network) for long range forecasting in electricity sale. Through a large number of experiments and evaluation of real-world datasets, the effectiveness of the proposed method is proved and verified in terms of prediction accuracy, time consuming and training speed.

中文翻译：

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一种用于电力销售长期预测的复合深度学习模型。

摘要

准确的售电量预测对电力企业合理安排供电计划、科学优化电力资源配置、提高电力管理效率、节能降耗具有积极作用。基于历史售电数据预测未来售电本质上可以概括为一个时间序列预测问题。本文提出了一种快速且内存高效的方法，该方法采用深度神经网络的表达能力和序列到序列结构（并行卷积和循环神经网络）的时间特性，用于售电中的长期预测。通过对真实世界数据集的大量实验和评估，在预测精度方面证明和验证了所提出方法的有效性，