Predictive Optimization of the Heat Demand in Buildings at the City Level

Abstract

Easily adaptable indoor temperature and heat demand models were applied in the predictive optimization of the heat demand at the city level to improve energy efficiency in heating. Real measured district heating data from 201 large buildings, including apartment buildings, schools and commercial, public, and office buildings, was utilized. Indoor temperature and heat demand of all 201 individual buildings were modelled and the models were applied in the optimization utilizing two different optimization strategies. Results demonstrate that the applied modelling approach enables the utilization of buildings as short-term heat storages in the optimization of the heat demand leading to significant improvements in energy efficiency both at the city level and in individual buildings.

Classification of Measures for Dealing with District Heating Load Variations—A Systematic Review

“…Using heat stored in the buildings connected to DH networks to reduce the DH demand peaks (i.e., using the buildings for so-called 'virtual' storage) is one of the strategies that has attracted increasing attention during the last years, especially in Italy [25,[69][70][71][72][73][74] and in Nordic countries, such as Finland [30,[75][76][77], Denmark [78][79][80][81][82][83][84][85], and Sweden [54,86,87]. These countries are characterized by well-developed DH sectors and a large proportion of buildings with high thermal inertia.…”

“…The choice of controlling strategy for a building should also be based on the service provided by the building [88]. Related to this, several studies were performed to propose controlling strategies for residential apartment buildings [54,77,78,80,82,88,90], residential single-family houses [79,88,91], and offices [30,[75][76][77]89].…”