A direct and relatively simple method to cut down computational runtime in forecast software by up to 86.

Software runtime poses significant challenges across multiple industries. In particular forecast applications such HVAC, cooling, and heating are time consuming due to their intricate input requirements and the need for repeated simulations to arrive at viable solutions. To address this, we present an a method implemented in the form of a Python software suit. This suite streamlines iterations and minimizes the mathematical operations necessary to generate cumulative output profiles for software solutions related to the aforementioned applications. Our method which deals directly and efficiently modifies achieves an impressive reduction in simulation runtime, up to 86 %. Here we demonstrate:•Software runtime in the simulation/CAD industry is an ongoing challenge applying simple problem-specific (in our case study it's HVAC energy consumption) algorithm show promising results.•We used an average "representative day" of a short reference year on a reduced input file, when the deviation was considerable, the PATE method was triggered to adjust the error between the real software generated results and the results generated from the natural full simulation.•The method was applied to several buildings on different locations in several continents.