Efficient energy production and distribution systems are urgently needed to reduce world climate change. Since modern district heating systems are sustainable energy distribution services that exploit renewable sources and avoid energy waste, in-depth knowledge of thermal energy demand, which is mainly affected by weather conditions, is essential to enhance heat production schedules. We hence propose a mixture copula-based approach to investigate the complex relationship between meteorological variables, such as outdoor temperature and solar radiation, and thermal energy demand in the district heating system of the Italian city Bozen-Bolzano. We analyse data collected from 2014 to 2017, and estimate copulas after removing serial dependence in each time series using autoregressive integrated moving average models. Due to complex relationships, a mixture of an unstructured Student-t and a flipped Clayton copula is deemed the best model, as it allows differentiating the magnitude of dependence in each tail and exhibiting both heavy-tailed and asymmetric dependence. We derive the conditional copula-based probability function of thermal energy demand given meteorological variables, and provide useful insight on the production management phase of local energy utilities.

迫切需要有效的能源生产和分配系统，以减少世界气候变化。由于现代区域供热系统是利用可再生资源并避免能源浪费的可持续能源分配服务，因此对主要受天气条件影响的热能需求的深入了解对于增强供热计划至关重要。因此，我们提出了一种基于混合copula的方法来研究气象变量（例如室外温度和太阳辐射）与意大利城市Bozen-Bolzano的区域供热系统中的热能需求之间的复杂关系。我们分析了2014年至2017年收集的数据，并使用自回归综合移动平均模型在每个时间序列中去除了序列依赖性后估计了copulas。由于关系复杂，t和翻转的Clayton copula被认为是最好的模型，因为它可以区分每条尾巴的依存程度，并表现出重尾和不对称依存关系。我们在给定气象变量的基础上，推导了基于条件copula的热能需求概率函数，并为当地能源公司的生产管理阶段提供了有用的见识。