Abstract
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.
Abstract (translated)
能源需求预测对电网运营商、工业能源用户和服务提供商来说至关重要。能源需求受到多种因素的影响，包括天气条件（如温度、湿度、风速和太阳辐射）以及日历信息（如一天中的小时数和一年中的月份），这些因素进一步影响日常工作和生活安排。由于这些因素之间存在因果关系的相互依赖性，因此问题比简单的基于相关性的学习技术所能处理的更为复杂。我们提出了一种结构因果模型，用以解释这些变量之间的因果关系。进行了全面分析来验证我们的因果信念，并揭示了一些与之前研究一致的重要见解。例如，我们的因果模型显示能源需求对温度波动的反应具有季节依赖性敏感度。此外，我们发现由于温度变化和日常活动模式之间脱钩效应的影响，在冬季能源需求表现出较低的变化幅度。然后我们建立了一个贝叶斯模型，该模型利用了我们学到的因果见解作为先验知识。在未见过的数据上对该模型进行训练和测试后，其性能达到了最先进的水平，即在测试集上的3.84%平均绝对百分比误差（MAPE）。此外，该模型还表现出较强的鲁棒性，在两年数据之间的交叉验证中获得了平均3.88%的MAPE。
URL
https://arxiv.org/abs/2512.11653
