Smart BIM-Driven Energy Optimization for Net-Zero Buildings

Thesis Abstract

Net-zero buildings require an integrated, data-driven approach to design, operation, and retrofit, leveraging Building Information Modeling (BIM) as a central platform to optimize energy performance across the lifecycle. This study addresses the fragmentation between design intent and real-time energy outcomes by proposing a Smart BIM-Driven Energy Optimization (SBEEO) framework that integrates parametric BIM, energy simulation, real-time building systems data, and machine learning for adaptive control. The aim is to develop and validate a holistic methodology enabling early-stage design decisions and ongoing operational optimization that collectively achieve net-zero targets under variable climate conditions. Specific objectives are to (1) quantify the energy performance gap between conventional BIM-driven design and SBEEO-enabled design for typical office typologies, (2) develop a data fusion pipeline that links Autodesk Revit-based models with energy simulation tools (EnergyPlus) and IoT sensor streams for continuous model calibration, (3) formulate and calibrate predictive models to forecast peak loads and demand response potential using time-series methods and regression analysis, (4) implement and test an adaptive control algorithm within the BIM environment that optimizes envelope, HVAC, and lighting strategies in response to real-time data, and (5) evaluate environmental and economic outcomes, including life-cycle energy use, embodied carbon, payback period, and return on investment. The methodology adopts a mixed-methods research design anchored in activity theory and the Habitability-Resource framework to capture technical and organizational factors influencing SBEEO adoption. The population comprises 40 recently designed or retrofit office buildings in three metropolitan areas with diverse climates. A stratified random sample of 24 projects is selected, with 8 used for in-depth modeling and 16 for cross-site validation. Data collection entails (i) BIM models and associated parametric scripts, (ii) calibrated energy models generated in EnergyPlus linked to the BIM via OpenStudio, (iii) sensor data from smart meters, occupancy sensors, and environmental controls over 12 months, and (iv) stakeholder interviews with 20 facility managers and 12 design consultants to elucidate workflow integration and decision-making. Instruments include a BIM-BEEO calibration checklist, a 36-month energy performance dataset, and a semi-structured interview guide. Validity and reliability are addressed through triangulation, cross-site replication, test-retest procedures for the machine learning components, and inter-rater reliability checks for qualitative coding. Analytical procedures encompass (i) descriptive statistics and energy performance gap analysis to establish baseline disparities, (ii) time-series forecasting (ARIMA, Prophet) and multivariate regression to predict peak demands and assess drivers, (iii) machine learning-based calibration (random forest, gradient boosting) to improve model accuracy using sensor data, (iv) a reinforcement learning-based adaptive control scheme implemented within the BIM environment to optimize envelope, HVAC sequencing, and lighting under constraint satisfaction, and (v) cost-benefit and life-cycle assessment (LCA) to quantify economic viability and environmental impact. A conceptual model integrates BIM data layers, energy simulation outputs, and real-time control signals, guided by the Theory of Planned Behavior for user acceptance and the Technology-Organization-Environment (TOE) framework for deployment readiness. Expected findings indicate substantial reductions in peak demand and annual energy use when SBEEO is applied, with improvements in model calibration accuracy (target RMSE reduction of 25–40% compared to baseline simulations) and demonstrable net-zero feasibility for mid-rise office prototypes under diverse climate scenarios. The study anticipates identifying critical BIM data quality requirements, efficient data fusion strategies, and decision-support workflows that align with facility management practices. The contribution to knowledge includes (i) a validated SBEEO framework that operationalizes BIM-enabled energy optimization across design and operation, (ii) a reproducible data pipeline and calibration methodology linking BIM, simulation, and real-time data, (iii) empirical evidence on the effectiveness of adaptive control within BIM-environment-embedded energy systems, and (iv) practical guidelines for achieving net-zero targets with quantified energy, carbon, and economic outcomes. The main conclusion is that an integrated SBEEO approach can significantly close the energy-performance gap between design intent and real-world operation, enabling reliable achievement of net-zero objectives in commercial office buildings. Recommendations emphasize standardization of BIM data schemas for energy optimization, investment in interoperable sensor networks, stakeholder training for BIM-centered decision-making, and policy support for performance-based retrofits aligned with lifecycle costs.