| 任禹丞,王雨薇,郑 杨,杨子跃,刘京易.基于AOBP的空调能耗特征标签模型与预测方法[J].电力需求侧管理,2025,27(6):78-84 |
| 基于AOBP的空调能耗特征标签模型与预测方法 |
| Feature labeling model and prediction method for air conditioning energy consumption based on AOBP |
| 投稿时间：2025-06-05 修订日期：2025-09-08 |
| DOI：10. 3969 / j. issn. 1009-1831. 2025. 06. 012 |
| 中文关键词: 空调能耗预测 空调使用行为概率 数据驱动 改进长短时记忆网络 |
| 英文关键词: air-conditioning energy consumption prediction air-conditioning occupant behavior probability data driven improving long short-term memory network |
| 基金项目:国网江苏省电力有限公司科技项目（J2023176） |
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| 中文摘要: |
| 在当前全球能源和环境危机不断加剧，同时空调能耗在整个建筑能源消耗中占据很大比例的背景下，精确预测空调能耗对于制定有效的节能政策、优化能源利用、减轻能源压力以及降低碳排放至关重要。提出一种基于空调使用行为概率模型的数据驱动空调能耗预测方法。首先，分析了空调能耗与空调使用者行为、环境参数、时间、建筑等因素之间的关系，构建了建筑空调能耗分析特征标签体系，涵盖了空调使用行为、环境、时间和建筑特征等多个维度。接着，引入了空调使用行为概率模型作为影响因素，以反映建筑环境、空调使用者和能源系统之间的实时交互。该模型考虑了策略、时间、事件和外部刺激的影响，较全面地估计了空调使用情况。最后，利用粒子群优化算法对长短时记忆网络进行优化，并在不同建筑物和空调类型下进行能耗预测。仿真实验结果表明，提出的数据驱动空调能耗预测方法在提高预测性能方面取得了显著进展，但计算时间也相应增加。 |
| 英文摘要: |
| Against the backdrop of the escalating global energy and environmental crisis, accurate prediction of air conditioning energy consumption is crucial for formulating effective energy-saving policies, optimizing energy utilization, reducing energy pressure, and reducing carbon emissions, as air conditioning energy consumption accounts for a large proportion of the entire building energy consumption. A data-driven air conditioning energy consumption prediction method is proposed based on a probability model of air-conditioning occupant behavior. Firstly, based on the analysis of the relationship between air conditioning energy consumption and factors such as air-conditioning occupant behavior, environmental parameters, time, and building, a feature label system for building air conditioning energy efficiency analysis is further constructed, covering multiple dimensions such as air conditioning occupant behavior, environment, time, and building characteristics. Secondly, the air-conditioning occupant behavior probability（AOBP）model is introduced as a factor to reflect the realtime interaction between the building environment, air conditioning occupants, and energy systems. This model considers the effects of strategy, time, events, and external stimuli, thus providing a more comprehensive estimation of air conditioning usage. Finally, particle swarm optimization algorithm is utilized to optimize the long short term memory network（LSTM）and to predict energy consumption across various building types and air conditioning systems. The simulation experiment results show that the proposed data-driven air conditioning energy consumption prediction method has made significant progress in improving prediction performance, but the calculation time has also correspondingly increased. |
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