基于数据驱动的中央空调系统多状态互动能力研究

Research on multi state interaction capability of central air conditioning system based on data driven approach

中文关键词: 数据驱动  需求响应  BiGRU神经网络  热力学模型  互动能力

英文关键词: data-driven  demand response  BiGRU neural network  thermodynamic model  ability to interact

中文摘要:

      空调负荷是一类响应速度快和可调容量大的柔性资源，通过对空调负荷多个需求响应时间状态下互动潜力进行特性分析与建模，可以在较小影响用户舒适度的情况下快速响应电网侧的调度，降低高峰时段的电力需求，缓解电力供需矛盾。为了更好揭示影响空调参与需求响应下的多种因素，首先基于空调的热力学模型，将空调负荷分解为静态负荷和动态负荷，数据驱动概念区别于经典的模型驱动，是利用采集或仿真获取的海量数据，从中挖掘并探索深层的特征关系并建立数据驱动算法下的问题架构与求解思路，并分别通过约束回归法和基于数据驱动的时间卷积-双向门控循环单元-注意力机制（temporal convolution?al network-bidirectional gated recurrent unit-attention，TCN-BiGRU-Attention）神经网络进行估算，然后对空调参与需求响应时不同时间状态下可调互动能力进行分析。仿真结果表明，设置温度等因素与空调静态负荷存在显著的相关性，且不同需求响应时间尺度状态下，空调负荷与电网侧互动能力存在巨大差异，该方法可以有效降低系统调用总成本和大幅提升空调负荷参与削峰填谷效率，并基于实际用户的数据验证了该方法的有效性和准确性。

英文摘要:

      Air conditioning load is a kind of flexible resources with fast response speed and large adjustable capacity. Through the analysis and modeling of the interaction potential of air conditioning load under multiple demand response time states, it can quickly respond to the grid side dispatch with little impact on user comfort, reduce the power demand in peak hours, and alleviate the contradiction between power supply and demand. In order to better reveal the various factors that affect air-conditioning participation in demand response, firstly,based on the thermodynamic model of air-conditioning, air-conditioning load is decomposed into static load and dynamic load. The concept of data-driven is different from the classical model-driven, which uses massive data acquired by collection or simulation. The deep feature relationships are mined and explored, and the problem architecture and solution ideas under the data-driven algorithm are established.Then, the constrained regression method and the data-driven temporal convolutional network-bidirectional gated recurrent unit-attention （TCN-BiGRU-Attention）neural network are used to estimate the adjustable interaction ability under different time states when the air conditioner participates in the demand response. The simulation results show that there is a significant correlation between the setting temperature and other factors and the static load of air conditioning, and there is a huge difference in the interaction ability between air conditioning load and the grid side under different demand response time scales. This method can effectively reduce the total cost of system calls and greatly improve the efficiency of air conditioning load participating in peak load shaving. The validity and accuracy of the method are verified based on the data of real users.