• Energy Research

Abstract A variety of novel, recyclable and reusable, construction materials has already been studied within literature during the past years, aiming at improving the overall energy efficiency ranking of the building envelope. However, several studies show that a delicate control of indoor climating elements can lead to a significant performance improvement by exploiting the building’s savings potential via smart adaptive HVAC regulation to exogenous uncertain disturbances (e.g. weather, occupancy). Building Optimization and Control (BOC) systems can be categorized into two different groups: centralized (requiring high data transmission rates at a central node from every corner of the overall system) and decentralized 1 (assuming an intercommunication among neighboring constituent systems). Moreover, both approaches can be further divided into two subcategories, respectively: model-assisted (usually introducing modeling oversimplifications) and model-free (typically presenting poor stability and very slow convergence rates). This paper presents the application of a novel, decentralized, agent-based , model-free BOC methodology (abbreviated as L4GPCAO) to a modern non-residential building (E.ON. Energy Research Center’s main building), equipped with controllable HVAC systems and renewable energy sources by utilizing the existing Building Management System (BES). The building testbed is located inside the RWTH Aachen University campus in Aachen, Germany. A combined rule criterion composed of the non-renewable energy consumption (NREC) and the thermal comfort index – aligned to international comfort standards – was adopted in all cases presented herein. Besides the limited availability of the specified building testbed, real-life experiments demonstrated operational effectiveness of the proposed approach in BOC applications with complex, emerging dynamics arising from the building’s occupancy and thermal characteristics. L4GPCAO outperformed the control strategy that was designed by the planers and system provider, in a conventional manner, requiring no more than five test days.

Abstract The increasing integration of renewable forms of energy into building energy systems raises the need for the use of more complex energy conversion and distribution systems. These are often equipped with complex automation systems, which require methods for structured development of control algorithms. In practice, textual descriptions of control algorithms represent a considerable source of failures. The digitization of the planning process is a core element in facilitating control development and supporting the engineering in the process automation. With regard to the digitization of the planning process, we have introduced the MODI-method in previous work. This method allows structured control development in early stages of the planning process and can be extended by performing simulations, i.e. an algorithm implemented as a Petri-Net. In this paper, we investigate the integration of this method into the simulation-based planning and an approach to model an algorithm based on Petri-Net. The case study shows the functionality of this method and the modeling approach. In future work, the MODI-method will be compared with other control strategies and applied in complex systems for further development.

The objective of this research is to evaluate the performance of a system of systems optimization algorithm, namely, L4G-PCAO, in building energy systems. Since the test bed of this research is an office building with more than two hundred occupiers, the heating and cooling demands of the building must always be fully satisfied. Consequently, changes in the currently-installed control system cannot be made forthrightly. Therefore, fresh ideas like implementation of new control strategies or optimization algorithms should be firstly put to the test via dynamic simulation, which makes engineers capable of examining new control and optimization strategies. The performance should then be analyzed and evaluated before implementing in the use case. This paper presents a strategy for simulation-based implementation of L4G-PCAO in a building energy system and also evaluates its performance. The results show that it is not only possible to conserve energy by applying this newly-developed optimization algorithm to existing control systems, but also it can shift the usage of energy sources in a more environment-friendly direction.