Individual temperature control on demand response in a district heated office building in Finland

Abstract

The Internet of Things can be an effective way to manage the demand side and perform demand response in thermal grids. The concept provides new models for predicting demand and gathering data in a smart and inexpensive fashion. This research investigates the deployment of room-specific demand response in a districtheated office building in Southern Finland. It is fulfilled by controlling set-point temperatures on thermostatic radiator valves. By developing a predictive algorithm on a cloud platform and gathering feedback from the environment and the users, the paper presents findings about load shifting on room level and about local discomfort. The results indicate that heating power can be increased and decreased at the thermostat, even by 104% and 47% respectively. On average this accounts for 40 W/m 2 . However, the larger the set-point temperature variation is, the larger local discomfort grows. Indoor air temperature readings do not give enough evidence of thermal comfort. Hence, demand response with room-level accuracy should be deployed in areas of the building in which flexibility is least noticed.

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].…”

“…The strategy can be applied (1) at building level by remotely controlling the heat flux provided to the customers from individual substations [93], or (2) at room level by remotely controlling individual digital thermostatic radiator valves [30,75,78,80,89,94], or (3) by remotely controlling an entire area or building clusters (i.e., the buildings clustered into different groups based on similar characteristics) [39,72,86,95]. When applying the strategy at room level, the modulation should not be similar in all rooms.…”

“…When applying the strategy at room level, the modulation should not be similar in all rooms. The set-point temperature for each room should be individually adjusted depending on the room's function and on the occupants' heat perception [31,75]. While the clustering approach allows a larger peak reduction [72], applying the strategy at room level enables the utilization of the building's ability to contribute with demand flexibility to its full potential [75].…”

Fourth-Generation District Heating and Motivation Tariffs

“…Licklederer et al [56] as well as Gross et al [57] investigate the options of integrating buildings as prosumers into the heating grids and Gorrono-Albizu and Godoy address the issue of fairness in the transition [58]. Several other previous papers add to these findings regarding energy consumption modeling [59][60][61][62] and energy renewability improvements [63][64][65][66][67][68][69][70] for buildings. The newly developed methodologies for determining the heat demand create the possibility of better matching the production with the consumption, thereby creating a more energy-efficient system.…”