Application of the software as a service model to the control of complex building systems

Panel: 6. Innovations in buildings and appliances

This is a peer-reviewed paper.

Authors:
Michael Stadler, Center for Energy and Innovative Technologies (CET), Austria
Jonathan Donadee, Lawrence Berkeley National Laboratory, USA
Chris Marnay, Lawrence Berkeley National Laboratory, USA
Gonçalo Mendes, Instituto Superior Técnico – MIT Portugal Program, Portugal, Portugal
Jan von Appen, Technische Universität Darmstadt, Germany, Germany
Oliver Mégel, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, Switzerland
Prajesh Bhattacharya, Lawrence Berkeley National Laboratory, USA
Nicholas DeForest, Lawrence Berkeley National Laboratory, USA
Judy Lai, Lawrence Berkeley National Laboratory, USA

Abstract

In an effort to create broad access to its optimization software, Lawrence Berkeley National Laboratory (LBNL), in collaboration with the University of California at Davis (UC Davis) and OSISoft, has recently developed a Software as a Service (SaaS) Model for reducing energy costs, cutting peak power demand, and reducing carbon emissions for multipurpose buildings. UC Davis currently collects and stores energy usage data from buildings on its campus. Researchers at LBNL sought to demonstrate that a SaaS application architecture could be built on top of this data system to optimize the scheduling of electricity and heat delivery in the building. The SaaS interface, known as WebOpt, consists of two major parts: a) the investment & planning and b) the operations module, which builds on the investment & planning module. The operational scheduling and load shifting optimization models within the operations module use data from load prediction and electrical grid emissions models to create an optimal operating schedule for the next week, reducing peak electricity consumption while maintaining quality of energy services. LBNL’s application also provides facility managers with suggested energy infrastructure investments for achieving their energy cost and emission goals based on historical data collected with OSISoft’s system. This paper describes these models as well as the SaaS architecture employed by LBNL researchers to provide asset scheduling services to UC Davis. The peak demand, emissions, and cost implications of the asset operation schedule and investments suggested by this optimization model are analysed.