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.
The rise of extensive monitoring systems and the availability of low-cost sensors as well as affordable computing power has led to the development of various big data and simulation model applications in the building sector. Nevertheless, many of these promising approaches face a common hindrance for the widespread application. In case of the big data applications, training data is often limited. Much the same, simulation models often lack required input data and require extensive work for calibration. Standard practices are often preferred to innovative approaches because construction and commissioning businesses are highly cost-sensitive. Therefore, we identified the need for a holistic approach for the combined use of machine learning and simulation techniques. In this paper, we present a toolchain to generate models and data needed for the application of innovative building automation and control tools. Using the data available during the construction process, machine-learning algorithms are employed to determine the type and location of data points in devices. From the relations of data points, the system architecture is derived and simulation models are generated automatically. Using these models, the data needed for the training of big data machine-learning algorithms can be generated. We describe the toolchain, already existing components and discuss the possible future implementation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.