基于时序迁移策略的空调负荷需求响应潜力评估

Potential evaluation of air conditioning load demand response based on time-sequential migration strategy

中文关键词: 空调负荷  需求响应潜力  时序迁移策略  循环神经网络  迁移学习

英文关键词: air conditioning load  demand response potential  time-sequential migration strategy  recurrent neural network  transfer learning

中文摘要:

      空调负荷需求响应潜力的精准评估是充分调度其参与需求响应的关键基础性工作。针对当前传统深度学习方法忽略现实场景中空调负荷的时序分布差异导致的预测精度较低的问题，首先将迁移学习的思想拓展至时间维度，类比迁移学习中协变量漂移的概念分析了空调负荷时间序列中存在的时序分布漂移现象，随后基于此提出了时序分布匹配以及时序相似性量化两种时序迁移策略，并将其整合进传统的循环神经网络（RNN）架构构建了自适应RNN空调负荷预测模型，由此提高了实际场景中空调负荷预测的精度。最后基于分别预测响应前后的负荷值的总体思路以及自适应RNN空调负荷预测模型提出了空调负荷需求响应潜力评估方法，并在现实数据集上与传统深度学习方法进行了对比实验分析。结果表明，该方法能在现有基础上显著提升需求响应潜力的预测精度，从而为电网调度中心的需求响应调度决策提供有效的参考。

英文摘要:

      The accurate assessment of the demand response potential of air conditioning loads is a crucial foundational task for effectively scheduling their participation in demand response. To address the issue of low prediction accuracy caused by the traditional deep learning methods’neglect of the time-sequential distribution differences in real-world air conditioning loads, the concept of transfer learning to the time dimension is extended. The phenomenon of time-sequential distribution drift in air conditioning load time series is analyzed by drawing an analogy to covariate shift in transfer learning. Based on this, two time-sequential migration strategies, time-sequential distribution matching and time-sequential similarity quantification, are proposed. These strategies are integrated into the traditional recurrent neural network（RNN）architecture to build an adaptive RNN air conditioning load prediction model, thereby improving the prediction accuracy in real-world scenarios. Finally, an air conditioning load demand response potential evaluation method is proposed based on the overall approach of predicting the load values before and after the response and the adaptive RNN air conditioning load prediction model. Comparative experimental analysis on real datasets shows that this method can significantly improve the prediction accuracy of demand response potential over existing methods, thus providing effective reference for the demand response scheduling decisions of the grid dispatch center.