A holistic framework for optimizing building decision metrics via advanced parametric modeling: The ECO‑Matrix Protocol

The rapid tightening of building-energy codes and carbon-mitigation targets now obliges design teams to compare dozens of envelope, system and control permutations while simultaneously controlling capital cost. Yet conventional parametric studies seldom test more than a handful of scenarios because every additional simulation imposes time penalties, and most optimisation tools still chase a single metric—typically annual energy use.

This paper introduces the ECO-Matrix Protocol, a cloud-hosted workflow that marries targeted Latin-hypercube sampling, batch simulation, surrogate-model interpolation and an interactive, many-objective dashboard. The protocol ranks thousands of design options against a suite of Key Performance Indicators (KPIs) that matter to both owners and regulators: capital-cost delta, operating savings, greenhouse-gas (GHG) abatement, simple payback and return on investment (ROI).

A K-4 school in Ontario, Canada, provides the validating case study. Ten recurrent variable families covering envelope, daylighting and HVAC were each discretised into three levels, yielding 51,840 theoretical combinations. Only 72 high-value IESVE simulation setup, powered with variable iteration through parametric batch were needed to train the interpolation engine, expanding the result set to 5,184 envelope–lighting variants; 20 explicit HVAC archetypes completed the matrix. The browser-based dashboard instantly filtered solutions exceeding the National Energy Code of Canada baseline and visualised trade-offs among energy, cost and carbon.

Statistical cross-validation against 20 random full simulations confirmed interpolation errors below one percent for energy-use intensity predictions.

By showing that holistic, many-objective optimisation fits within a typical schematic-design window, the ECO-Matrix Protocol offers a transferable roadmap for low-carbon design in diverse climates. Future work will automate HVAC surrogate training and further integrate Life Cycle Costing (LCA) and embodied-carbon analysis.

Research Gap and Objectives:

Past studies by Attia et al. (2012) and Ascione et al. (2017) highlight the limited uptake of multi-objective optimization in practice. Few approaches move beyond single‑metric energy savings, and sampling strategies are often ad‑hoc, leaving decision‑makers unsure about representativeness. This paper addresses three questions:

1. How can a design team explore tens of thousands of envelope‑system combinations with <100 simulations?
2. How can multiple KPIs—including capital cost—be brought into one visual space?
3. Does such a workflow improve design decisions in a live project?