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Abstract ▼ AI-Helper

The demand for local district-heating systems is closely related to the temperature and socioeconomic variables. The climate change and unpredictable climate variations, technological progress, and preference changes generate wide and prolonged prediction errors from the forecasting model. Based o...

The demand for local district-heating systems is closely related to the temperature and socioeconomic variables. The climate change and unpredictable climate variations, technological progress, and preference changes generate wide and prolonged prediction errors from the forecasting model. Based on the stylized facts of the district-heat data, this paper proposes a real-time forecasting model of district-heat demand. The proposed model is predicated on the state-dependent relationship between the temperature and the district heat demand. The socioeconomic seasonal variables and dynamic properties are incorporated to explain the district heat demand in two metropolitan areas for the period 2007~2008. A sizable amount of improvement in prediction accuracy is observed.

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