Using EPPY scripts to enable automated building electrification modeling with flexibility and scalability

Building electrification has emerged as a necessary strategy for reducing greenhouse gas emissions in the building sector. Many research projects require extensive modeling of building electrification to properly quantify potential greenhouse gas emission reductions, cost savings, and changes in peak load patterns. These electrification measures may impact end-uses such as space heating, water heating, and cooking, among others. This modeling process can be repetitive, redundant and error prone. One challenge that stands out, particularly for large buildings with complex layouts, is the process of replacing large systems that serve multiple thermal zones. Manual completion of this task can include repetitively inserting an object into a space, renaming it, along with all associated nodes (representing, for example, air flow), and reconnecting these nodes to the main branch. With room for error in each of these steps, the manual approach can be time consuming, hence resulting in slow progress on large-scale modeling projects. To improve the efficiency of this common modeling process, we developed a code-based tool using EPPY, a Python based scripting language for EnergyPlus, to streamline and automate the electrification modeling process. The input files include the original mixed fuel model and a sample all-electric model that includes the replacement system. Users first identify the zones that will be affected by the retrofit. For these zones, the script automatically replaces the fossil fuel system with the new all-electric system based on the name of conditioned zones, creating a new model of preliminary electrification. This streamlined process can save substantial time and effort, simplifying the process of running large scale models of complex commercial and multifamily buildings under different conditions.

We present two use cases that apply this streamlined framework. In one study, we consider the potential energy efficiency improvements that result from the implementation of high efficiency electrification in different building types across multiple climate zones. The automation code significantly reduced modeling time and effort. In another, we apply this approach to several commercial and multifamily building prototypes to understand optimal pathways of compliance with building performance standards which require reductions in greenhouse gas emissions.

This paper will discuss the logical approach and algorithm behind this streamlined process and present potential benefits to researchers and stakeholders who would benefit from large scale building electrification modeling.