Middleware platforms are key technology in any Internet of Things (IoT) system, considering their role in managing the intermediary communications between devices and applications. In the energy sector, it has been shown that IoT devices enable the integration of all network assets to one large distributed system. This comes with significant benefits, such as improving energy efficiency, boosting the generation of renewable energy, reducing maintenance costs and increasing comfort. Various existing IoT middlware solutions encounter several problems that limit their performance, such as vendor locks. Hence, this paper presents a literature review and an expert survey on IoT middleware platforms in energy systems, in order to provide a set of tools and functionalities to be supported by any future efficient, flexible and interoperable IoT middleware considering the market needs. The analysis of the results shows that experts currently use the IoT middleware mainly to deploy services such as visualization, monitoring and benchmarking of energy consumption, and energy optimization is considered as a future application to target. Likewise, non-functional requirements, such as security and privacy, play vital roles in the IoT platforms’ performances.

In order to reduce energy consumption and CO₂ emissions in the building sector, more and more renewable energy sources are integrated into energy systems. Especially geothermal fields combined with heat pumps are able to supply buildings with heat and cold at low carbon emissions. However, using geothermal fields as heat and cold source influences the ground temperature. Consequently, the ground temperature can change dramatically over a building’s lifetime, leading to less efficient operation of the energy system. Therefore, a sustainable operation is required to ensure the long-term efficiency of geothermal fields. In this paper, we develop an optimization model to derive operating strategies for an efficient long-term operation of a building energy system coupled to a geothermal field. The investigated energy system is the main building of the E.ON Energy Research Center in Aachen, Germany, which includes a heat pump, two boilers, a combined heat, and power unit, a glycol cooler, and a geothermal field with 41 probes. For each component, we develop energy-based sub-models, which are connected to form the overall system. The geothermal field is modeled by using a g-functions approach as well as a simplified resistance-capacitance approach. To achieve short computing times and realize an optimization horizon of several years, the optimization problem is formulated as mixed-integer linear programming (MILP). The developed model is optimized regarding two different objectives: the minimization of energy costs and the minimization of long-term temperature changes in the ground. Conclusions for an efficient and sustainable operation of the field, especially for the cooling supply, can be derived from the optimization results. It is shown that a state of equilibrium should be aimed to achieve an energy-efficient operation, in which the temperature of the field is close to the initial ground temperature.

This work presents an agent-based control concept that we integrate into a cloud framework for controlling building energy systems. The agents are arranged in a hierarchical structure, where a coordinator agent sends optimized set point values to sub-agents. Each sub-agent controls a subsystem in order to reach the given set points and provides the coordinator agent with a cost function for the overall set point optimization. The multi-agent system and the building energy system exchange data via the cloud framework FIWARE. The components of the building energy system (e.g. boiler, air-handling unit) are connected to the cloud framework and send measurements (e.g. temperature values or volume flow) via a scalable IoT-Interface to the corresponding data object in the platform. The sub-agents of the agent control receive the measurements and send the calculated control action back to the corresponding components of the energy system. In a simulation study, we use the framework to control an air-handling unit. The aim of the agent control is to reach a given supply air temperature while reducing the total consumed energy. The implemented cloud-based control is capable of efficiently reaching the given temperature set points while communicating via the IoT interface.

Faulty programming of control functions in Building Automation and Control Systems (BACS) might result in inefficient building operations. To reduce programming errors, an automated implementation process of control functions might be a promising solution. Recently, Building Information Modeling (BIM) contributes to digitizing building construction projects but is rarely used in the planning and implementation of control functions in BACS. The control description in BIM also remains unclear. Regarding these problems, a control documentation method for BIM and an automated control implementation approach can simplify control implementation in BACS and hence improve the building operation. In the previous work, we developed the MODI method for a structured planning process of mode-based control algorithms for building energy systems. This method showed the potential to support digitized control planning and implementation in BACS. Based on this, in this paper, we introduce a documentation method to report mode-based control algorithms in the industrial foundation class (IFC), enabling data sharing among BIM, and a software-assisted approach to automatically generate PLC codes for implementing these algorithms. The case study demonstrates the documentation of a desired mode-based control strategy for an energy supply network in IFC and the implementation of this strategy in a PLC program. In the simulation phase, we test the implemented control strategy to verify the functionalities of the PLC program. The results prove that mode-based control strategies can be fully automatically implemented in a PLC program based on IFC data.

The growing share of renewable energy sources in building energy systems leads to more complex energy conversion and distribution systems. The current process of developing appropriate control functions for energy systems is insufficient and consequently error-prone. Regarding this problem, a new method is expected to systematically develop appropriate control functions for buildings and reduce design errors in this process. This paper introduces the MODI method, aiming at a structured development process of mode-based control algorithms to reduce errors in the early design stages of buildings. A complete framework and a standardized application process of the MODI method will be established to systematically design mode-based control algorithms described through signal-interpreted Petri nets. Furthermore, we performed a simulation-assisted evaluation approach to test and improve the performance of the control algorithms generated by MODI. In a case study, we applied MODI to develop a mode-based control strategy for an energy system containing heating and cooling supply networks. The desired control strategy was tested and tuned in a simulation phase. Compared to a reference control, the mode-based control algorithm shows an improvement in system efficiency by 4% in winter and 8% during the transitional season phase.

As a disease, COVID-19 has shaken the world in 2020/21. Countermeasures have been taken by governments to protect against infection, which have also affected the water and energy consumption of buildings. In particular, buildings managed by public authorities (e.g. kindergartens, schools and administrative offices) have been closed and reopened with different concepts. In this paper, the impact of countermeasures on the electrical and thermal energy and water consumption of municipal buildings is investigated using monitoring data of 100 buildings from 2018 to 2021 of a city in the state of North Rhine-Westphalia (NRW) in Germany. Where available, data of CO₂ sensors from building automation systems are included in the evaluation. The CO₂ concentration is in a range of concern (1500-2000 ppm) for 2% of presence periods. A value in the unacceptable range (>2000 ppm) is not found in the CO₂ data. Since only ventilation-conditioned spaces are equipped with CO₂ sensors, no conclusions can be drawn about non-ventilated buildings. We investigate to which extent the measures taken have changed the energy and water consumption. On total average, the heat demand increased lightly by 5 % and water and electricity demand decreased by 15% and 12% respectively.