KGS forms public and private R&D relationships
We know how to do research and development. We make technology, fix buildings, and engage dozens of clients in those processes; that's a unique balance of practice, theory and business only large companies typically enjoy. We engage interested clients to test ideas, verify product features, and perform market research on industry needs. KGS also contributes to industry growth and best practices by regularly contributing to technical and trade journals, participating in professional society working groups, and investing in university research programs.
RP1633 - Data and Interfaces for Advanced Building Operations and Maintenance
KGS was awarded the ASHRAE Research Project 1633 on Data and Interfaces for Advanced Building Operations and Maintenance. Analyzing and interpreting building performance data is critical to the proliferation, retrofit and success of higher performance buildings. Despite the growing ease in collecting building data, standards bodies have remained silent on the topic of how to manage building performance through simple and understandable data-driven feedback. As a consequence, little guidance is available to help specifying engineers and designers tap the technical potential of building data and information technology to create and sustain more high performance buildings. Some institutional and commercial research and development has attempted to solve that problem, primarily for bulk energy information, but without much extension to other aspects of building operation or the unique needs of various building types. This research project seeks to fill these voids by conducting fundamental field work and assessment of data and control interfaces in various buildings in order to establish a set of data-driven metrics, interfaces and dashboards that clearly quantify and communicate building performance to a diverse set of building stakeholders.
Model-based predictive control for energy efficient low-lift cooling
KGS works with the Pacific Northwest National Laboratory (PNNL) developing data-driven model-based predictive control algorithms for a novel energy efficient cooling strategy. Low-lift cooling, developed at PNNL and MIT, utilizes predictive control of variable speed chillers operated at low pressure ratios to reduce cooling energy consumption. Predictively controlling low-lift chillers to pre-cool thermal energy storage during the night shifts loads to nighttime and allows more efficient part-load operation. Combining this approach with radiant cooling systems allows for significantly lower pressure difference across the chiller compressor, greatly increasing overall system efficiency.
Automated fault detection and retuning
KGS develops and adapts algorithms that analyze building data to identify faults, inefficiencies, and opportunities for improvement in building system operation, control, maintenance, and design. This development is informed by feedback from Clockworks clients. KGS is currently working under a Cooperative Research and Development Agreement with the Pacific Northwest National Laboratory (PNNL) to further accelerate research and deployment of analytical algorithms for automated retuning and fault detection in buildings.