The primary goal of this research is to devise a system that produces controllers for complex fenestration systems that perform nearly as well as Model Predictive Control but at a level of cost and implementation complexity that rivals simple heuristic controls. To this end, a cloud-based automated controller production system has been set up for a motorized external Venetian blind device, with a simple web interface that can be used by non-experts. The computation cost per controller is in the range of a few dollars, and the control logic is simple enough to be implemented on small and cheap distributed controllers. The web interface allows the user to specify some details of their particular building and window configuration, including orientation, latitude, interior geometries, and lighting and HVAC system parameters. Upon submittal, a cloud-based system configures the necessary files and commands, and then runs thousands of optimizations with them. Once the calculations are finished, the system produces a lookup table and interpolation-based controller scripts that can be used on a simple and cheap distributed controller. This paper describes the underlying models and optimization processes. It also describes the resulting control logics for two cases tested at Lawrence Berkeley National Laboratory’s Advanced Windows Testbed Facility: illuminance maximization subject to glare constraints; and lighting + HVAC energy minimization. The performance of the model-based controllers produced by the automated web-based system are compared to a heuristic ‘block beam’ controller in physical experiments at the Testbed. The experimental results are supplemented by simulation experiments with the same configuration as the Testbed. The results show the illuminance maximizing controller significantly outperforms the heuristic controller in terms of glare avoidance, and also outperforms it in terms of hours of daylight autonomy. The energy minimizing controller also outperforms the heuristic controller. This paper also discusses how the web-based system may be extended to consider other configurations, such as electrochromic windows and thermally massive HVAC systems. Potential roles for this type of system within the building design and construction industry are discussed.

The High Performance Building Façade Solutions–Phase II project was initiated through the California Energy Commission’s Public Interest Energy Research (PIER) program in July 2010 to support industry’s development and deployment of both incremental and breakthrough façade technologies in partnership with the U.S. Department of Energy (DOE). The objective of this three-year project was to develop, or support the development and deployment of, promising near-term and emerging zero net energy building façade technologies for solar control and daylighting, addressing two of the largest end uses in California commercial buildings: cooling and lighting. In partnership with industry (such as manufacturers), three classes of technologies were investigated: daylighting systems, angular-selective shading systems, and dynamic façade systems. Commercially available and emerging prototype technologies were developed and evaluated using laboratory tests. Simulations, full-scale outdoor tests in the Advanced Window Testbed, and demonstration projects quantified energy and peak electric demand reductions and occupant satisfaction, acceptance, and comfort associated with the resultant indoor environment. Several new technologies were developed using virtual prototyping tools. Integrated control systems were developed using model predictive controls. Simulation tools were developed to model operable complex fenestration systems such as shades and microprismatic films. A schematic design tool called COMFEN was developed to facilitate evaluation of these advanced technologies in the early design phase. All three classes of technologies resulted in significant reductions in perimeter zone energy use and peak electric demand, providing viable options that can support California’s long-term goal of achieving zero net energy use in the next decade.