Why jEPlus+EA

Parametrics lacks efficiency

For anyone who wants to find the best solution of a problem, the very first approach comes to mind is testing all possible options, i.e. using brute-force search (or exhaustive search). Parametric study and full factorial design are also used to refer to this approach. Although “the best” solution is guaranteed, there is a catch with brute-force search, cost.

The above example has 20 design variables, each has a handful of options. As a result, the total design space is over one trillion! Assuming each simulation takes only 1 minute, to complete a brute-force search on the DMU's 256-core cluster will take 7,600 years. Using a proper optimization algorithm is the only feasible option. This is why jEPlus needs EA for solving real world problems.

Limitations of existing tools

There are a number of optimization tools/packages available for EnergyPlus users. Most notable is GenOpt, which has been around since 1998. GenOpt employs a very flexible mechanism to couple with simulation programs and user-written search algorithms. Since all interactions between the GenOpt core and the simulation program are definable using text files, it can be coupled, by user, with practically any simulation tools that have command-line interface and use text files for input and output.

It is also possible to use generic tools for EnergyPlus optimization, for example, MATLAB's Global Optimization Toolbox; or indeed, write your own optimization algorithms. The downside of such flexibility is the initial learning curve for new users.

Key Features

jEPlus+EA is designed with an aim to remove the barrier to entry into the field of optimization, for existing jEPlus users at least. If you have already got a jEPlus project, you can use jEPlus+EA (almost) out of the box, and see optimization results extremely quickly. There is very little configuration you will have to do. The necessary controls are (or will be) provided on the GUI. For example, you can control the population size, crossover rate and mutation rate on-the-fly, as well as manage your computing resources for the job.

For tools, there is often a trade-off between ease-of-use and versatility. jEPlus+EA sacrifices much in functionality, in order to hide the complex configurations from end users. For example, there is no other choice of algorithms other than the widely used NSGA2; and the algorithmic parameters user can adjust are limited, too. The good news is, for most optimization problems, the algorithm will work effectively.