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Abstract: Building industry is closely related to current energy and environmental issues. Several green building codes and rating systems addressing the problems have been developed. Leadership in Energy and Environmental Design (LEED) rating system is recognized as one of the effective and widely adopted commercial building standards. LEED buildings were investigated in several green city and green building studies but only used as instances in static matrices. These studies were not able to answer the question why a particular city favors LEED. However, in this paper, three commonly used machine learning algorithms – Linear Regression, Locally Weighted Regression and Support Vector Regression (SVR) – are compared and SVR is used to investigate, discover and evaluate the variables that could influence LEED building markets in U.S. East Coast cities. Machine learning models are first created and optimized with the features of city geography, demography, economy, higher education and policy. Then SVR model identifies the key factors by dynamic self-training and model-tuning using the dataset. Via optimization, the correlation coefficient between the model''s prediction and actual value is 0.79. The result suggests that population and policy can be important factors for developing LEED buildings. It is also interesting that higher education institutions, especially accredited architecture schools could also be driving forces for LEED commercial building markets in East Coast cities. [Copyright &y& Elsevier]

Focuses on a concept from the Total Building Performance (TBP) team that constitutes an integrated and holistic framework for conceptualizing, specifying, designing, analyzing and commissioning a building project. Background on a Develop and Build procurement contract for the National Library Board building awarded to the team; Details of principal performance mandates in the TBP model; Implementation of the concept in selective projects significant in Singapore.

• Reinforcement learning-based method to using a whole building energy model for HVAC optimal control. • Implementation and deployment of the method in an existing novel heating system (Mullion system) of an office building. • Building energy modeling for the Mullion system and sub-hourly multi-objective model calibration using the Bayesian method and genetic algorithm optimization. • 16.7% heating demand reduction in the real-life deployment with more than 95% probability compared to the old rule-based control. • Discussions on the practicability of the control method. Whole building energy model (BEM) is a physics-based modeling method for building energy simulation. It has been widely used in the building industry for code compliance, building design optimization, retrofit analysis, and other uses. Recent research also indicates its strong potential for the control of heating, ventilation and air-conditioning (HVAC) systems. However, its high-order nature and slow computational speed limit its practical application in real-time HVAC optimal control. Therefore, this study proposes a practical control framework (named BEM-DRL) that is based on deep reinforcement learning. The framework is implemented and assessed in a novel radiant heating system in an existing office building as a case study. The complete implementation process is presented in this study, including: building energy modeling for the novel heating system, multi-objective BEM calibration using the Bayesian method and the Genetic Algorithm, deep reinforcement learning training and simulation results evaluation, and control deployment. By analyzing the real-life control deployment data, it is found that BEM-DRL achieves 16.7% heating demand reduction with more than 95% probability compared to the old rule-based control. However, the framework still faces the practical challenges including building energy modeling of novel HVAC systems and multi-objective model calibration. Systematic study is also needed for the design of deep reinforcement learning training to provide a guideline for practitioners. [ABSTRACT FROM AUTHOR]