In:
HMD Praxis der Wirtschaftsinformatik, Springer Fachmedien Wiesbaden GmbH, Vol. 60, No. 4 ( 2023-08), p. 850-871
Kurzfassung:
Approximately 18% of CO2 emissions in Germany are caused by the heating, cooling and hot water supply of buildings, with more than 75% of households using fossil fuels such as natural gas and oil. The SECAI (Sustainable heating through Edge-Cloud-based Artificial Intelligence Systems) approach presented in this paper aims to reduce heating control in multi-residential buildings, and thus CO2 consumption, through the use of information technology. The SECAI approach considers the entire ecosystem consisting of sensors, individual room controls, central heating, tenants and landlords. This involves an AI-based analysis of the heating requirements of private apartments, based on which optimized and coordinated heating plans can be created for building complexes. Edge cloud technologies, sensor technology and federated learning enable these plans to react ad hoc and in compliance with data protection regulations to changes in usage behavior. The information is also used for AI-based control of the central heating systems within the building, where heating and hot water are generated for all apartments. For this purpose, SECAI considers four layers. These range from sensors and actuators (nano), to the apartment (micro), to the building (meso), to building complexes and same-type buildings (macro), and are highly interdependent. A complex ecosystem is being created around the SECAI solution in which tenants, the housing industry, heating manufacturers and providers of IoT solutions interact with products and services.
Materialart:
Online-Ressource
ISSN:
1436-3011
,
2198-2775
DOI:
10.1365/s40702-023-00988-8
Sprache:
Deutsch
Verlag:
Springer Fachmedien Wiesbaden GmbH
Publikationsdatum:
2023
ZDB Id:
1015731-1
ZDB Id:
2109643-0
SSG:
3,2
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