LoaDiff: Conditional Generation of Electricity Consumption Time Series for Energy Analytics

2026-09-10

AI Summary

为解决能源分析中真实数据受限问题，本文提出LoaDiff模型，通过条件生成方法合成年度、次小时级的电能消耗时间序列。

Abstract

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.

Problem

• energy transition
• residential electricity consumption
• smart-meter data
• data-protection regulations
• synthetic alternatives

Innovation

• diffusion-based generative model
• conditional generation
• smart-meter load curves
• downstream utility