Title: Leveraging Algorithms in Energy-Efficient Design Platforms for Sustainable Construction

Abstract:

Computer-aided energy assessment has become a standard practice in green building design, serving as a critical mechanism for enhancing efficiency and achieving comprehensive optimization. This study introduces a novel platform that integrates Building Information Modeling (BIM), operational sensor data, and sophisticated simulation workflows through the use of robust algorithms. The system is built upon a multi-layer service architecture that facilitates dynamic energy simulation and evolutionary multi-objective optimization. These components are tightly coupled via a high-performance core developed in C++ and supported by adaptive agent models.

To validate the platform, a mid-rise office building was selected for a detailed case study. Data collection focused on five representative zones, targeting specific metrics regarding occupancy patterns and building envelope characteristics. Following data preprocessing, it was determined that missing sensor records constituted only 3.2% of the annual dataset. To ensure data integrity, all variables were standardized through interpolation at 15-minute intervals.

The optimization process involved 40 iterative rounds, resulting in a significant reduction in annual energy consumption per square meter. Specifically, energy usage decreased by 29.3%, falling from an initial 315 kWh/m² to a final 223 kWh/m². Despite this efficiency gain, the lifecycle cost increase for occupants remained minimal at just 3.7%. Furthermore, the number of discomfort hours was successfully curtailed to fewer than 70 hours annually.

An examination of the Pareto optimal solutions revealed distinct correlations between energy performance and specific design parameters. The envelope U-values ranged between 1.05 and 1.57 W/m²K, while nighttime ventilation rates varied from 2.1 to 3.6 h⁻¹. These findings demonstrate that the proposed integrated algorithmic framework is technically feasible, highly performant, and scalable for green building applications. Ultimately, this platform serves as a dependable decision-support instrument for sustainability professionals and design engineers, facilitating the precise, data-driven realization of energy-efficient structures.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC