- Co-simulation between EnergyPlus and MATLAB®Co-simulation is a simulation strategy which allows different simulation programs to run simultaneously and exchange data during the simulation. Each software package expects data from the other, which allows the programs to complement missing features in the other or to optimize the building operation with information obtained during the simulation. EnergyPlus uses the Building Controls Virtual Test Bed (BCVTB) as the software environment that enables the user to couple different elements of EnergyPlus for co-simulation with other software (e.g., MATLAB®, Modelica, Radiance, TRNSYS, ESP-r, and so on). It is based on the Ptolemy II software environment and has the ability to couple the simulation program with the actual hardware. This communication protocol is open and can be used by other software to perform co-simulation with EnergyPlus; this is the case of MLE+ . The latter is executed in the standard environment of MATLAB®, which facilitates the finding and fixing of errors (debugging). This is more complicated to do with the direct implementation of BCVTB in MATLAB®, which requires knowledge of the Ptolemy II programming language. All of the examples that use co-simulation between EnergyPlus and MATLAB® use one of these (i.e., BCVTB or MLE+).For the cases that use BCVTB as the software environment to perform the co-simulation between EnergyPlus and MATLAB®, the optimizations carried out are focused on Model Predictive Control (MPC) optimizations [13,14,15,16,17,18]; on building energy consumption improvements [19,20]; on thermal comfort using the Human and Building Interaction Toolkit ; on considering the condensation risk of a thermally activated building (TAB) in the cooling operation ; on how to integrate the ability to simulate double-skin facades into EnergyPlus ; on thermal bridges using MATLAB® ; and so on.For the cases that use MLE+, the optimizations carried out are similar: Model Predictive Control (MPC) optimizations [25,26,27,28]; building energy performance improvements using phase change materials ; analyzing daylight conditions with Radiance ; or making decisions based on the thermal comfort levels . It has even been used to create a black-box model with the information provided by EnergyPlus during co-simulation .
- Management of EnergyPlus simulations using MATLAB®On the other hand, other studies on optimizing or analyzing the energy performance of a building did not require the use of co-simulation, but focused on managing the simulations and their results. In these cases, it is necessary to develop a personal script that manages EnergyPlus through MATLAB®, as there exists no dedicated application that does this in a simple way. The research topics that use EnergyPlus in this way are broader: for example, optimizing building energy retrofits using genetic algorithms [33,34,35,36,37,38] or a specific methodology, such as the “Simulation-based Large-scale uncertainty/sensitivity Analysis of Building Energy performance” (SLABE) method ; improving the building energy performance when designing the envelope ; performing thermostatic optimization ; or using Agent-Based Modeling (ABM) to capture the dynamic energy of occupants . In addition, more specific topics have been studied using this strategy, such as: optimizing the control of blind systems ; using urban energy models to analyze the urban heat island effect ; developing an EnergyPlus meta-model ; performing MPC and thermal comfort optimization ; developing a sensitivity analysis using the Simlab software [47,48]; optimizing the renewable energy mix of a building ; finding an optimal architectural design using BIM at the design stage ; or performing automated random parametric simulations .To make things even more complicated, researchers sometimes prefer to develop a personal script to manage other software, which, in turn, manages EnergyPlus, for example using GenOpt to analyze the energy saving potential of adaptive glazing technologies  or the optimal control of a switchable glazing façade ; using jEPlus  to perform sensitivity analyses, uncertainty quantification, or multi-objective optimizations [55,56,57,58,59]; or, with Rhinoceros and Grasshopper, using Ladybug and Honeybee to evaluate energy saving strategies in commercial buildings with integrated data collection .
2. Description of the API
- launcher.mThis is the main file, where almost all of the options must be defined (see Listing 1). First, the paths of the files, including the path of the EplusLauncher API, are defined [API Loading]. Next, it is necessary to configure the API [Launcher configuration]. In this part, it is possible to select the number of cores to perform the simulation (Launcher.MaxCores), where “0” is the maximum. This function is useful as, in some studies, many simulations need to be performed, such as parameterization studies or in the analysis of models that use optimization algorithms, where a group of simulations must be performed before making a decision about the model (for example, a genetic algorithm and its population simulation). For these cases, having the ability to perform a group of simulations at the same time (i.e., parallelization of the simulations) can make these studies feasible. In addition, when the number of cores is high, it is possible to perform several optimizations of different problems at the same time by defining specific cores for each optimization.Each of these simulations will be stored inside a folder called (OutputJob#), where # is the number defined by (Launcher.OutputFolderNumber). In the cases where different optimizations are carried out at the same time, it is good practice to identify each of them. Initially, this folder name may seem too simple; however, it satisfies the Windows® file and path length limitations (due to the maximum character number limitation). The disadvantage is that the file does not give us information about the type of simulation that has been carried out. This Windows® limitation seemed more important to us than the information that could be given in the folder name. However, this information is collected in the log file, which is created by the command (Launcher.JobsLogFile) and can be accessed to obtain the details of each of the simulations performed by EplusLauncher. As shown below, the folder name (OutputJob#) is defined in the [EnergyPlus configuration] section of launcher.m, whereas the number (#) is defined in the [Launcher configuration] section. The reason for this is to avoid cases where it is necessary to perform different simulations with different configurations (e.g., several jobs with different weather or input files), in which the results will be overwritten because they are assigned the same folder name. Thus, each folder is identified by a unique name and number, preventing errors.Listing 1. launcher.m file.Next, [EnergyPlus listeners] are defined. These listeners report each EnergyPlus message to MATLAB®, as well as when a simulation ends or all of the jobs end. They make calls to the other MATLAB® files (EnergyPlusMessage.m, SimulationFinished.m, and JobsFinished.m) and allow for adding in new code (scripts) for other functionalities that have not yet been developed in the API (e.g., reporting the simulation time or the ongoing results, modify the simulation file during the optimization process using the results, change the optimization strategy if a local minimum has been reached, etc.).Then, it is necessary to perform the [EnergyPlus configuration]. In this part, all the files needed to run EnergyPlus are defined: the installation folder of EnergyPlus (config.EnergyPlusFolder); the building energy model file ()(config.InputFile); the weather file ()(config.WeatherFile); the RVI file (), which is the responsible for reorganizing the EnergyPlus results in the output files ()(config.RviFile); and the name of the output folder (config.OutputFolder). This name will be followed by the number defined in the (Launcher.JobsLogFile) stage.Finally, editing of the Building Energy Model (BEM) file and definition of the simulation list is done. EplusLauncher can edit the simulation file () before each simulation. As files are text-based files, EplusLauncher searches for tags (i.e., identifiers) in the file and replaces them with other variables. These variables can be text (strings) or values (numbers). These identifiers are defined in the variable (Tags) and their values are variables created by the user. The example given below is the parameterization of a building envelope, where the roughness and density of one of its layers and the insulation thickness are parameterized. In this case, the job list is defined by several “for” loops, in order to select all the variation possibilities of the three parameters. This list must be defined for each analyzed problem, and will be different depending on the type of problem. For example, in the case of optimization using a genetic algorithm, this list of jobs will be the population of each generation, where the different identifiers will be first added to the list of jobs (job.AddTags(Tags)) and, then, the values of these identifiers (job.AddValues([…])).The file (launcher.m) ends with the execution command (Launcher.Run()), which, in the example, executes 27 simulations () giving all possibilities in the parameterization example (i.e., a brute force technique).
- EnergyPlusMessage.mThis file displays all EnergyPlus messages in the MATLAB® command window. It is very helpful, as it reports all possible errors during the simulation process, identifying which simulation has caused the error (see Listing 2).Listing 2. EnergyPlusMessage.m file.
- SimulationFinished.mThis file summarizes the entire simulation process of each simulation, reporting “correct simulation: True” if the simulation has run without any errors (see Listing 3).Listing 3. SimulationFinished.m file.
- JobsFinished.mFinally, this file is executed when all simulations are finished (see Listing 4). In an optimization process, this file is used to add additional operations to extract valuable information from the results obtained, which can be used to edit the EnergyPlus files and repeat the process.In general, simulations are carried out on a large scale in all optimization processes; thus, it is necessary to manage the simulation files. EnergyPlus generates an enormous amount of information throughout the simulation process. Many of these files are not used in the study being carried out and only take up space. For this reason, the API has an option to delete files, which is executed at the end of the simulation process. In the example, this function is useful, as the only files that are used after the parameterization are the results files. This removal process is accessed by defining the filenames with their extensions (cleaner.SetFiles()) or by selecting files with the same extension (cleaner.SetFilesExtensions()). Once the files have been defined, the cleaning process is executed in all the folders created during the simulation process using the command (cleaner.CleanFromLogFile()).Listing 4. JobsFinished.m file.
3. Example of Use
- It allows the editing of EnergyPlus input files () before performing simulations. These modifications are made through a process of searching and replacing tags, which provides great versatility to the editing process. This, together with other EnergyPlus utilities (such as Parametric:Logic, which allows changes to the file according to certain values) gives this feature great potential for the optimization of building energy models.
- It allows (in a simple way) for selection of the quantity of cores to be used in each optimization process. This not only reduces the total time needed to perform an optimization process but, in cases where a high number of cores are available (i.e., a cluster), several optimizations can be performed at the same time.
- The API is structured in such a way that it is easy to introduce improvements (through scripts) in each of the phases of the simulation/optimization process. It allows for combination of the analysis capabilities of all MATLAB® toolboxes with the large amount of results that EnergyPlus provides, by allowing both to work under the same platform.
Conflicts of Interest
|API||Application Programming Interface|
|BCVTB||Building Controls Virtual Test Bed|
|BEM||Building Energy Model|
|BPO||Building Performance Optimization|
|ECM||Energy Conservation Measures|
|FMI||Functional Mockup Unit|
|GHG||Green House Gases|
|HVAC||Heating Ventilation Air Conditioning|
|IPCC||Intergovernmental Panel on Climate Change|
|MPC||Model Predictive Control|
|SABINA||SmArt BI-directional multi eNergy gAteway|
|SLABE||Simulation-based Large-scale uncertainty/sensitivity Analysis of Building Energy performance|
|TAB||Thermally Activated Building|
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