Analysing the relationship between district heating demand and weather conditions through conditional mixture copula

Abstract

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.