| 任禹丞,王玉珏,贾丰全,胡涵天.公共建筑空调负荷的自适应滑窗LSTM预测方法[J].电力需求侧管理,2024,26(5):43-48 |
| 公共建筑空调负荷的自适应滑窗LSTM预测方法 |
| Adaptive sliding window LSTM approach of air conditioning load in public buildings |
| 投稿时间：2024-03-09 修订日期：2024-04-11 |
| DOI：10. 3969 / j. issn. 1009-1831. 2024. 05. 007 |
| 中文关键词: 公共建筑 空调负荷预测 LSTM 神经网络 滑动窗口 |
| 英文关键词: public building air conditioning load prediction LSTM neural network sliding window |
| 基金项目:国 家 电 网 有 限 公 司 科 技 项 目（5400- 202318574A-3-2-ZN） |
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| 摘要点击次数: 3 |
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| 中文摘要: |
| 针对空调负荷受天气条件和日历信息等多维因素影响,难以充分挖掘空调负荷数据的时间序列特征,导致预测精度低的问题,提出一种基于自适应滑窗的长短期记忆递归神经网络（LSTM）的公共建筑空调负荷预测模型。首先分析了公共建筑空调负荷的影响因素；同时针对传统的时间序列预测模型在处理非平稳数据时效果不佳的问题,创新性地引入了自适应滑动窗口机制,能够动态调整滑窗大小,更好地捕捉温度和历史空调负荷数据的变化特征,提高了数据预处理的有效性；进一步根据空调负荷变化的复杂性和长短期依赖性原因,设计多层LSTM网络架构,实现公共建筑空调负荷准确预测。以某区域负荷数据为例,验证了所提模型在选择合适的滑动窗口数量时,拟合能力更高,预测效果更好。 |
| 英文摘要: |
| To address the problem of low prediction accuracy due to the influence of multidimensional factors such as weather factors and calendar information on air conditioning load and the difficulty in sufficiently extracting the time series characteristics of load data, an air conditioning load prediction model for public buildings using long short-term memory（LSTM）recurrent neural networks based on an adaptive sliding window is proposed. The model first analyzes the influencing factors of air conditioning load in public buildings. Considering that traditional time series prediction models often perform poorly when dealing with non-stationary data, an adaptive sliding window mechanism is innovatively introduced. This mechanism can dynamically adjust the window size to better capture the variations in temperature and historical air conditioning load data, thereby improving the effectiveness of data preprocessing. Furthermore, given the complexity and long-term and short-term dependencies of air conditioning load variations, a multi-layer LSTM network architecture is designed to achieve accurate prediction of air conditioning load in public buildings. Taking the load data of a specific region as an example, proposed model achieves higher fitting ability and better prediction results when an appropriate sliding window size is selected. |
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