applied sciences  
Article  
Energym: A Building Model Library for Controller Benchmarking  
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controllers that can run without major modifications on different types of buildings and do not necessitate fine-tuning by hand, as often practiced in research studies. Tools that allow to easily run simulations on different, realistic building models, provide a way to alleviate these issues. We will restrict ourselves to discussing frameworks that are able to handle the two most popular modeling languages for buildings, namely EnergyPlus [16] and Modelica [17]. Tools that are capable of including, or including these types of models are Modelicagym [18], an RL testbed for power consumption optimization [19], BOPTEST [20], and CityLearn [21]. The former two provide interfaces to the API of the popular RL benchmarking library Gym [22] for including Modelica (for the first package) or EnergyPlus models (for the second package). Their focus does not lie on providing a broad range of pre-compiled models or fixed evaluation metrics, which limits their practicality for benchmarking. BOPTEST, on the other hand, follows an approach similar to Energym, while concentrating purely on Modelica models. Of their 10 selected reference cases, two are currently available, together with four additional prototypes. The models are launched using Docker and the communication is done through a RESTful API, which differs from the Energym approach of providing pre-compiled models or a Docker container and the Gym API. Recently, the developers of BOPTEST also started to incorporate the Gym API into their framework. CityLearn tackles the problem of demand response, the coordinated control of multiple buildings to match the power demand to the provided power by the distribution grid. Aiming also at providing a benchmark for RL controllers, their focus does not lie on the assessment of single building control performance. **Contribution:** We present Energym (available at [[https://github.com/bsl546/energym](https://github.com/bsl546/energym)]([https://github.com/bsl546/energym](https://github.com/bsl546/energym)) (accessed on 14 April 2021)), a Python-based library, made for standardized comparison and evaluation of controller performances, based on predefined evaluation scenarios, and inspired by the RL benchmarking library Gym. The included building models to date are based on EnergyPlus and Modelica, and are interfaced using the Functional Mock-up Interface (FMI) standard [23] through compiling them as Functional Mock-up Units (FMUs). We provide an overview of these models regarding their characteristics, location and technical equipment. Key performance indicators (KPIs) that relate to thermal comfort, energy consumption, and emissions are defined and we lay out the standardized evaluation scenarios for each model. Four of the 11 models already present are entirely based on real buildings (envelope and equipment were calibrated with recorded measurement data), whereas for the other models, only parts of the equipment were calibrated.