The increasing importance of coordinated energy management in residential districts has led to a shift from individual end-user optimization to a broader energy community perspective. This transition, however, necessitates efficient data communication and processing tools. In this context, the Internet of Things (IoT) plays a pivotal role by seamlessly connecting energy meters, sensors, data processing units, and controllable energy assets within these districts. This empowers homeowners and utility providers with real-time data and intelligent automation, leading to more efficient energy consumption through predictive analytics. IoT sensors monitor energy usage patterns, weather conditions, and energy market fluctuations, allowing residents to remotely control and optimize their appliances and heating/cooling systems, ultimately reducing energy waste and costs. This paper presents an IoT-powered demand response management simulation study in a building district, validated using data from the Großschönau Municipality in Austria. This community encompasses various building types connected to both electric and local district heating (DH) networks. Data is collected by IoT-enabled sensors and transmitted via the internet for pre-processing and backend services. These services primarily involve an optimization-based coordinated management of energy assets in the community. This study aims to assess, in the simulation phase, the optimal operation scheduling of heat pumps (HP) with energy storage units that connect each building in the energy community to the DH network. The simulation outcomes demonstrate a notable improvement in the community's energy self-efficiency, resulting in lowered energy expenses facilitated by real-time monitoring of energy market data. This approach also leads to a reduction in estimated total CO2 emissions related to HP's operation.