LoaDiff 模型用于生成电力消耗时间序列
LoaDiff: Conditional Generation of Electricity Consumption Time Series for Energy Analytics
朋友，DeepSeek 新出的 LoaDiff 模型很实用，能根据天气和家电情况生成模拟的用电数据，比直接用真实数据更安全，适合做电力分析。
这篇论文提出 LoaDiff，一个基于扩散模型的生成模型，用于生成符合实际的年长、亚小时级的智能电表负荷曲线。该模型支持根据静态家庭属性（如电器拥有情况）和动态上下文变量（如日历信息和户外温度）进行条件生成。实验表明，LoaDiff 生成的负荷曲线既真实又多样，在生成质量和训练数据记忆风险之间取得了良好平衡，并保留了下游能源应用有用的信息。
LoaDiff: Conditional Generation of Electricity Consumption Time Series for Energy Analytics
The energy transition is reshaping residential electricity consumption through the increasing adoption of distributed generation, electrified appliances, and demand-response programs. Understanding these evolving behaviors requires access to granular smart-meter data for applications such as load forecasting, appliance detection, and demand-side flexibility analysis. However, such data are subject to strict access restrictions and data-protection regulations. Thus, realistic synthetic alternatives are necessary. In this paper, we introduce LoaDiff, a diffusion-based generative model for year-long, sub-hourly smart-meter load curves. LoaDiff supports flexible conditioning on static household attributes, such as appliance ownership, and dynamic contextual variables, including calendar information and outdoor temperature. We evaluate the model against multiple generative baselines on three residential electricity-consumption datasets. Our experiments assess four complementary dimensions: fidelity and diversity, training-record memorization risk, downstream utility for load forecasting and appliance detection, and conditional controllability under alternative temperature conditions. The results show that LoaDiff generates realistic and diverse load profiles, achieves a favorable trade-off between generation quality and limited evidence of memorization, preserves information useful for downstream energy applications, and responds coherently to changes in conditioning variables.