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Decarbonising and digitalising the energy sector requires scalable and interoperable Demand Flexibility (DF) applications. Semantic models are promising technologies for achieving these goals, but existing studies focused on DF applications exhibit limitations. These include dependence on bespoke ontologies, lack of computational methods to generate semantic models, ineffective temporal data management and absence of platforms that use these models to easily develop, configure and deploy controls in real buildings. This paper introduces a semantics-driven framework to enable DF control applications in real buildings. The framework supports the generation of semantic models that adhere to Brick and SAREF while using metadata frommore » Building Information Models (BIM) and Building Automation Systems (BAS). The work also introduces a web platform that leverages these models and an actor and microservices architecture to streamline the development, configuration and deployment of DF controls. The paper demonstrates the framework through a case study, illustrating its ability to integrate diverse data sources, execute DF actuation in a real building, and promote modularity for easy reuse, extension, and customisation of applications. The paper also discusses the alignment between Brick and SAREF, the value of leveraging BIM data sources, and the framework's benefits over existing approaches, demonstrating a 75% reduction in effort for developing, configuring, and deploying building controls.« less

In the transition from fossil fuel to electrified heating, a concerning trend is emerging in certain regions of the US. Owners of buildings with gas-based systems leave them in place after adding heat pumps (HPs). Existing control solutions for these hybrid (dual fuel) systems are rudimentary and fall short of realizing the full carbon reduction potential of these systems. Model predictive control (MPC) is often regarded as the benchmark for achieving optimal control in integrated systems. However, in the case of small-medium commercial buildings (SMCBs), the control and communication infrastructure required to facilitate the implementation of such advanced controls ismore » often lacking. This paper presents a field implementation of easy-to-deploy MPC for a dual fuel heating system consisting of HPs and a gas-fired furnace (GF) for SMCBs. The control system is deployed on an open-source middleware platform and utilizes low-cost sensor devices to be used for real SMCBs without major retrofits. Here, we demonstrated this MPC in a real office building with 5 HPs and 1 GF for 2 months. The test results showed that MPC reduced 27% of cost while completely eliminating GF usage by shifting 23% of the thermal load from occupied-peak time to non-occupied-non-peak times.« less

Control applications that facilitate Demand Flexibility (DF) are difficult to deploy at scale in existing buildings. The heterogeneity of systems and non-standard naming conventions for metadata describing data points in building automation systems often lead to ad-hoc and building-specific applications. In recent years, several researchers investigated semantic models to describe the meaning of building data. They suggest that these models can enhance the deployment of building applications, enabling data exchanges among heterogeneous sources and their portability across different buildings. However, the studies in question fail to explore these capabilities in the context of controls. This paper proposes a novel semantics-drivenmore » framework for developing and deploying portable DF control applications. The design of the framework leverages an iterative design science research methodology, evolving from evidence gathered through simulation and field demonstrations. The framework aims to decouple control applications from specific buildings and control platforms, enabling these control applications to be configured semi-automatically. This allows application developers and researchers to streamline the onboarding of new applications that could otherwise be time-consuming and resource-intensive. The framework has been validated for its capability to facilitate the deployment of control applications sharing the same codebase across diverse virtual and real buildings. The demonstration successfully tested two controls for load shifting and shedding in four virtual buildings using the Building Optimization Testing Framework (BOPTEST) and in one real building using the control platform VOLTTRON. Insights into the current limitations, benefits, and challenges of generalizable controls and semantic models are derived from the deployment efforts and outcomes to guide future research in this field.« less

Hundreds of studies have investigated Model Predictive Control (MPC) for the optimal operation of building energy systems in the past two decades. However, MPC field tests are still uncommon, especially for small- and medium-sized commercial buildings and for buildings integrated with onsite renewables. This paper describes the implementation and the long-term performance evaluation of an MPC controller in a small commercial building equipped with behind-the-meter photovoltaics and electrochemical batteries. MPC controls space conditioning, commercial refrigeration, and the battery system. We tested two types of demand flexibility applications in the field: electricity bill minimization under time-of-use tariffs and responses to gridmore » flexibility events. Results show that the proposed controller achieves 12% of annual electricity cost savings and 34% peak demand reduction against the baseline, while respecting thermal comfort and food safety. The field tests also demonstrate the ability of the MPC controller to provide a multitude of grid services including real-time pricing, demand limiting, load shedding, load shifting, and load tracking, using the same optimization framework.« less