Clayton Miller

Urban and building multiscale co-simulation: case study implementations on two university campuses

The co-simulation of both urban and building-level models leverages the advantages of both platforms. It better accounts for the localized effects of surrounding buildings, geography and climate conditions while maintaining high-fidelity building systems representation. This paper describes the co-simulation process of the building and urban-scale models of two university campuses in Switzerland using EnergyPlus and CitySim. In the first case study, on-site measured performance data is compared to the co-simulation results. The second case study examines the results of the two engines. The results show that coupling of EnergyPlus with CitySim resulted in a −15.5% and −7.5% impact on cooling consumption and a +6.5% and +4.8% impact on heating use as compared to solo simulations.The co-simulation process was able to better model realistic conditions for heating, but not cooling in one case study. It was able to substantially reduce the discrepancies in prediction between the engines in the other study.

A Cyber-Physical Middleware Platform for Buildings in Smart Cities

In the realm of smart cities, tools for intelligent, real-time building energy management at the urban-scale are still evolving. Today, most commercial buildings have a dedicated Building Management System (BMS) to manage operations on-site efficiently; but there is no unified approach involving multiple stakeholders to manage them cooperatively in a district or urban scale. Thus, we formulate the following research question: ‘What is a feasible structure and what are the relevant components of a middleware that is easy to integrate within existing BMS/EMS and scalable for application in smart cities?’ To address this knowledge gap, this article proposes a new integrated and non-intrusive approach to cyber-physical middleware design using open-source tools. The implementation of proposed middleware in a case study building-scale project in Singapore is presented. Several features are analyzed to understand the relevance and scalability of proposed middleware as a technology enabler for smart building management in cities. The experiences and lessons learned from this project can be extrapolated to the urban-scale as more buildings are equipped with such systems. For this purpose, middleware code is also made available as open-source.

Predicting success of energy savings interventions and industry type using smart meter and retrofit data from thousands of non-residential buildings

This paper discusses the creation of targeting and segmentation information about non-residential buildings that are equipped with advanced metering infrastructure (AMI) meters, or smart meters. Statistics, model, and pattern-based temporal features are extracted from over 36,000 smart meters. They are then merged with a database of past energy efficiency interventions such as lighting, HVAC, and controls retrof its from 1,600 buildings. The buildings are divided into Good, Average, and Poor performing classes according to consumption from before and after the retrofits. Classification models are developed that improve the ability to predict retrofit success and standard industry class by 18.3% and 27.6% respectively over baselines. This study serves as an example of better leveraging smart meter data from non-residential buildings for utility targeted incentive programs. The methodology outlined is preliminary and further models and temporal features are to be tested.