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A Hybrid Data-Driven Simulation Framework For Integrated Energy-Air Quality (iE-AQ) Modeling at Multiple Urban Scales
Title
A Hybrid Data-Driven Simulation Framework For Integrated Energy-Air Quality (iE-AQ) Modeling at Multiple Urban Scales
Creator
Ashayeri, Mehdi
Date
2020
Description
To date, limited work has been done to collectively incorporate two key urban challenges: climate change and air pollution for the design of...
Show moreTo date, limited work has been done to collectively incorporate two key urban challenges: climate change and air pollution for the design of sustainable and healthy built environments. Main limitations to doing so include the existence of large spatiotemporal gaps in local outdoor air pollution data and a lack of a formal theoretical framework to effectively integrate localized urban air pollution data into sustainable built environment design strategies such as natural ventilation in buildings. This work hypothesizes that emerging advanced computational modeling approaches, including artificial intelligence (AI) and machine learning (ML) techniques, along with big open data set initiatives, can be used to fill some of those gaps. This can be achieved if urban air quality explanatory factors are properly identified and effectively connected to the current building performance simulation workflows.Therefore, the primary objective of this dissertation is to develop a hybrid AI-based data-driven simulation framework for integrated Energy-Air Quality (iE-AQ) modeling to quantify the combined energy reduction profiles and health risks implications of sustainable built environment design. This framework (1) incorporates dynamic human-centered factors, including mobility and building occupancy among others into the model, (2) interlinks land use regression (LUR), inverse distance weighting (IDW), and building energy simulation (BES) approaches via the R computational platform for developing the model, and (3) develops a web-based platform and interactive tool for visualizing and communicating the results. A series of novel machine learning approaches are tested within the workflow to improve efficiency and accuracy of the simulation model. A multi-scale model of urban air quality (using PM2.5 concentrations as the end point) and weather localization model with high spatiotemporal resolution was developed for Chicago, IL using low-cost sensor data. The integrated energy and air quality model was tested for the prototype office building at multiple urban scales in Chicago through applying air pollution-adjusted natural ventilation suitable hours.Results showed that the proposed ML approaches improved model accuracy above traditional simulation and statistical modeling approaches and that incorporating dynamic building-related factors such as human activity patterns can further improve urban air quality prediction models. The results of integrated energy and air quality (iE-AQ) analysis highlight that the energy saving potentials for natural ventilation considering local ambient air pollution and micro-climate data vary from 5.2% to 17% within Chicago. The proposed framework and tool have the potential to aid architects, engineers, planners and urban health policymakers in designing sustainable cities and empowering analytical solutions for reducing human health risk.
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