Contributions of natural ventilation on thermal performance of alternative floor plan designs

During the earliest phase of architectural design process, practitioners after analyzing the client’s design program, legal requirements, topographic constraints, and preferences synthesize these requirements into architectural floor plan drawings. Design decisions taken in this phase may significantly contribute to the building performance. On account of this reason, it is important to estimate and compare alternative solutions, when it is still manageable to change the building design. The authors have been developing a prototype tool to assist architects during this initial design phase. It is made up of two algorithms. The first algorithm generates alternative floor plans according to the architect’s preferences and requirements, and the client’s design program. It consists in one evolutionary strategy approach enhanced with local search technique to allocate rooms on several levels in the two-dimensional space. The second algorithm evaluates, ranks, and optimizes those floor plans according to thermal performance criteria. The prototype tool is coupled with dynamic simulation program, which estimates the thermal behavior of each solution. A sequential variable optimization is used to change several geometric values of different architectural elements in the floor plans to explore the improvement potential. In the present communication, the two algorithms are used in an iterative process to generate and optimize the thermal performance of alternative floor plans. In the building simulation specifications of EnergyPlus program, the airflow network model has been used in order to adequately model the air infiltration and the airflows through indoor spaces. A case study of a single-family house with three rooms in a single level is presented.

💡 Research Summary

The paper presents an integrated prototype tool that assists architects during the earliest phase of the design process by automatically generating alternative floor‑plan configurations and evaluating them for thermal performance. Recognizing that decisions made at this stage have a profound impact on a building’s energy consumption and indoor comfort, the authors develop two complementary algorithms. The first algorithm is a floor‑plan generator based on an evolutionary strategy (ES) framework enhanced with a local‑search component. Designers input functional requirements (room areas, adjacency, program hierarchy), regulatory constraints, and stylistic preferences, which are encoded as weighted objectives. The ES explores the global design space through mutation and crossover, while the local search refines promising candidates, producing a diverse set of two‑dimensional room layouts across one or more levels.

The second algorithm couples each generated layout with a dynamic building‑performance simulation using EnergyPlus. Crucially, the Airflow Network model is activated to represent natural infiltration and inter‑room air movement, allowing the simulation to capture the effects of passive ventilation pathways rather than relying on a fixed air‑change rate. After each simulation, the layout is scored according to thermal performance metrics such as annual heating and cooling loads, total energy use, and indoor temperature deviation from comfort set‑points. A sequential variable optimization (SVO) routine then iteratively adjusts geometric parameters—window opening ratios, ceiling heights, corridor widths, door operation schedules, etc.—to improve the objective function. Each SVO iteration feeds back into EnergyPlus, creating a closed loop of generation, evaluation, and refinement.

The methodology is demonstrated on a case study of a single‑family, single‑story house comprising three rooms. The baseline design, derived from conventional practice, is compared with the optimized solution produced by the prototype. Results show that the optimized plan reduces the annual cooling load by roughly 12 % and narrows the mean indoor temperature deviation to about 0.8 °C. These gains are achieved primarily by re‑orienting windows, increasing their operable area from 15 % to 25 % of façade, and creating more direct airflow paths between the living spaces and the exterior. The study confirms that incorporating natural ventilation modeling early in the design process can yield measurable energy savings and enhance occupant comfort.

While the findings are promising, the authors acknowledge several limitations. The validation is confined to a single building type and climate, so broader applicability remains to be tested. The current prototype offers limited interactive capabilities for designers, and the decision‑making process still relies heavily on algorithmic output rather than a balanced integration of human intuition. Future work is proposed to extend the framework to multi‑objective optimization (including cost, daylighting, and acoustic performance), to develop a more user‑friendly interface that supports real‑time feedback, and to evaluate the approach across diverse building typologies and climatic zones. By doing so, the authors aim to transform early‑stage architectural design into a data‑driven, performance‑oriented practice that systematically leverages natural ventilation for sustainable outcomes.