Development of new building HVAC control algorithms has grown due to needs for energy efficiency and operational flexibility. However, case studies demonstrating new algorithms are largely individualized, making algorithm performance difficult to compare directly. In addition, the effort and expertise required to implement case studies in real or simulated buildings limits rapid prototyping potential. Therefore, this paper presents the Building Optimization Testing Framework (BOPTEST) and associated software for simulation-based benchmarking of building HVAC control algorithms. A containerized run-time environment (RTE) enables rapid, repeatable deployment of common building emulators representing different system types. Emulators use Modelica to represent realistic physical dynamics, embed baseline control, and enable overwriting supervisory and local-loop control signals. Finally, a common set of key performance indicators are calculated within the RTE and reported to the user. This paper details the design and implementation of software and demonstrates its usage to benchmark a Model Predictive Control strategy.

Building and district energy systems become increasingly complex, requiring accurate simulation and optimization of systems that combine building envelope, heating ventilation and air conditioning, electrical distribution grids and advanced controllers. Hence, it becomes more challenging for existing simulation tools to provide integrated solutions for these multi-physics problems. Moreover, common building simulation frameworks tightly integrate model equations and their solvers in the program code, which affects model transparency and hampers tool extensions. This is contrasted by equation-based tools such as Modelica, for which different solvers can be used. In this context, the Integrated District Energy Assessment by Simulation (IDEAS) library is developed. After a recent development shift towards more detailed, multi-zone models, this paper presents a comprehensive, well-documented, overview of the buildings part of IDEAS. This includes new computational aspects of the library, improved usability aspects, an updated intercomparison with BESTEST and a verification based on IEA EBC Annex 58.

Predictive controllers can greatly improve the performance of energy systems in buildings. An important challenge of these controllers is the need of a building model accurate and simple enough for optimization. Grey-box modelling stands as a popular approach, but the identification of reliable grey-box models is hampered by the complexity of the parameter estimation process, specifically for multi-zone models. Hence, single-zone models are commonly used, limiting the performance and applicability of the predictive controller. This paper investigates the feasibility of the identification of multi-zone grey-box building models and the benefits of using these models in predictive control. For this purpose, the parameter estimation process is split by individual zones to obtain an educated initial guess. A virtual test case from the BOPTEST framework is contemplated to assess the simulation and control performance. The results show the relevance of modelling thermal interactions between zones in the multi-zone building.