S4ConvD通过自适应调节提升能耗预测效率，适合智能建筑实时部署。
S4ConvD: Adaptive Scaling and Frequency Adjustment for Energy-Efficient Sensor Networks in Smart Buildings
- 基于深度状态空间模型改进，结合自适应缩放与频率调整。
- 在ASHRAE数据集上超越现有基准，GPU运行时间显著缩短。
- 专为资源受限环境设计，利于可再生能源接入与开源研究。
由于传感器数据间的依赖关系和环境条件的多变性，智能建筑中的能耗预测极具挑战。本文提出S4ConvD，一种新型卷积型深度状态空间模型（Deep-SSMs），旨在减少对复杂预处理步骤的依赖。该模型专为资源受限环境优化运行时性能，通过自适应缩放与频率调整机制，有效捕捉建筑能耗的复杂时序特征。在ASHRAE Great Energy Predictor III数据集上的实验表明，S4ConvD优于当前主流基准。此外，借助块分块（Block Tiling）优化技术，其GPU运行时间大幅降低。因此，S4ConvD具备实际部署于实时能耗建模场景的潜力。完整代码与数据集已开源至GitHub，支持开放协作与后续研究。本方法还推动了资源高效模型执行，有助于提升能源预测精度并促进可再生能源融入智能电网系统。
原文摘要 · Abstract (English)
Predicting energy consumption in smart buildings is challenging due to dependencies in sensor data and the variability of environmental conditions. We introduce S4ConvD, a novel convolutional variant of Deep State Space Models (Deep-SSMs), that minimizes reliance on extensive preprocessing steps. S4ConvD is designed to optimize runtime in resource-constrained environments. By implementing adaptive scaling and frequency adjustments, this model shows to capture complex temporal patterns in building energy dynamics. Experiments on the ASHRAE Great Energy Predictor III dataset reveal that S4ConvD outperforms current benchmarks. Additionally, S4ConvD benefits from significant improvements in GPU runtime through the use of Block Tiling optimization techniques. Thus, S4ConvD has the potential for practical deployment in real-time energy modeling. Furthermore, the complete codebase and dataset are accessible on GitHub, fostering open-source contributions and facilitating further research. Our method also promotes resource-efficient model execution, enhancing both energy forecasting and the potential integration of renewable energy sources into smart grid systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。