用偏态高斯分布拆解城市用水模式，提升用水行为分析与运营决策精度。
Decomposition of Water Demand Patterns Using Skewed Gaussian Distributions for Behavioral Insights and Operational Planning
- 采用偏态高斯分布分解每日用水曲线，分离基线与各峰值特征。
- 相比对称高斯模型，重建误差降低50%以上，更精准捕捉用水峰值形态。
- 适用于用水异常检测、政策影响评估，适合水务管理与智能调度研究者。
本研究提出一种基于偏态高斯分布（Skewed Gaussian Distributions, SGD）的新方法，用于分解城市用水模式，以获取行为洞察并支持运行规划。小时级用水曲线包含长期基础设施设计与日常运行的关键信息，影响管网压力、水质、能耗与系统可靠性。通过将每日用水曲线分解为基线分量与多个独立峰值分量，该方法用可解释参数刻画每个峰值：峰值幅度、出现时间（均值）、扩散程度（持续时间）和偏度（不对称性），从而重建观测模式并揭示潜在用水动态。这种峰值级别的细粒度分解支持实时需求管理、异常与漏损检测等运维应用，也助力行为变化、季节影响或政策效应的战略分析。与传统对称高斯或纯统计时序模型不同，SGD能显式捕捉如早晨尖峰后缓慢回落的非对称峰值形态，提升合成模式生成保真度，并增强异常消费行为识别能力。在多个真实数据集上验证显示，SGD在重构精度上显著优于对称高斯模型，平均均方根误差降低超50%，同时保持物理可解释性。该框架还可通过设定特定特征生成合成用水情景。所有代码公开于：https://github.com/Relkayam/water-demand-decomposition-sgd
原文摘要 · Abstract (English)
This study presents a novel approach for decomposing urban water demand patterns using Skewed Gaussian Distributions (SGD) to derive behavioral insights and support operational planning. Hourly demand profiles contain critical information for both long-term infrastructure design and daily operations, influencing network pressures, water quality, energy consumption, and overall reliability. By breaking down each daily demand curve into a baseline component and distinct peak components, the proposed SGD method characterizes each peak with interpretable parameters, including peak amplitude, timing (mean), spread (duration), and skewness (asymmetry), thereby reconstructing the observed pattern and uncovering latent usage dynamics. This detailed peak-level decomposition enables both operational applications, e.g. anomaly and leakage detection, real-time demand management, and strategic analyses, e.g. identifying behavioral shifts, seasonal influences, or policy impacts on consumption patterns. Unlike traditional symmetric Gaussian or purely statistical time-series models, SGDs explicitly capture asymmetric peak shapes such as sharp morning surges followed by gradual declines, improving the fidelity of synthetic pattern generation and enhancing the detection of irregular consumption behavior. The method is demonstrated on several real-world datasets, showing that SGD outperforms symmetric Gaussian models in reconstruction accuracy, reducing root-mean-square error by over 50% on average, while maintaining physical interpretability. The SGD framework can also be used to construct synthetic demand scenarios by designing daily peak profiles with chosen characteristics. All implementation code is publicly available at: https://github.com/Relkayam/water-demand-decomposition-sgd
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