用因果模型提升电力需求预测精度，揭示温变与用电的季节性关系。
Causal Inference in Energy Demand Prediction
- 构建结构因果模型，解析气象与日程因素间的因果关系。
- 在测试集上实现3.84%的MAPE，跨两年验证平均3.88%。
- 适合电网调度、能源服务等需高鲁棒预测的场景。
电力需求预测对电网运营、工业用户及服务商至关重要。需求受多种因素影响，如温度、湿度、风速、太阳辐射等气象条件，以及每日和每月时间信息，这些因素相互关联并影响日常作息。传统相关性方法难以捕捉其复杂因果结构。本文提出一个结构因果模型，分析变量间因果关系，并验证了已有研究的结论：电力需求对温度波动的响应具有季节依赖性；冬季因温变与活动模式解耦，需求波动更小。基于此因果先验，构建贝叶斯模型，在未见数据上训练测试，达3.84%的测试集MAPE；跨两年数据交叉验证，平均MAPE为3.88%，表现优于现有方法。
原文摘要 · Abstract (English)
Energy demand prediction is critical for grid operators, industrial energy consumers, and service providers. Energy demand is influenced by multiple factors, including weather conditions (e.g. temperature, humidity, wind speed, solar radiation), and calendar information (e.g. hour of day and month of year), which further affect daily work and life schedules. These factors are causally interdependent, making the problem more complex than simple correlation-based learning techniques satisfactorily allow for. We propose a structural causal model that explains the causal relationship between these variables. A full analysis is performed to validate our causal beliefs, also revealing important insights consistent with prior studies. For example, our causal model reveals that energy demand responds to temperature fluctuations with season-dependent sensitivity. Additionally, we find that energy demand exhibits lower variance in winter due to the decoupling effect between temperature changes and daily activity patterns. We then build a Bayesian model, which takes advantage of the causal insights we learned as prior knowledge. The model is trained and tested on unseen data and yields state-of-the-art performance in the form of a 3.84 percent MAPE on the test set. The model also demonstrates strong robustness, as the cross-validation across two years of data yields an average MAPE of 3.88 percent.
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