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.

翻译：能源需求预测对电网运营商、工业能源消费者和服务提供商至关重要。能源需求受多种因素影响，包括天气条件（如温度、湿度、风速、太阳辐射）和日历信息（如一天中的小时和一年中的月份），这些因素进一步影响日常工作和生活安排。这些因素之间存在因果相互依赖关系，使得问题比简单的基于相关性的学习技术所能充分处理的更为复杂。我们提出了一种结构因果模型，用于解释这些变量之间的因果关系。通过全面分析验证了我们的因果假设，并揭示了与先前研究一致的重要见解。例如，我们的因果模型表明，能源需求对温度波动的响应具有季节依赖性敏感性。此外，我们发现冬季能源需求的方差较低，这是由于温度变化与日常活动模式之间的解耦效应所致。随后，我们构建了一个贝叶斯模型，利用学习到的因果见解作为先验知识。该模型在未见数据上进行训练和测试，在测试集上以3.84%的平均绝对百分比误差（MAPE）取得了最先进的性能。该模型还表现出强大的鲁棒性，在两年数据上的交叉验证平均MAPE为3.88%。