Daylight harvesting: a multivariate regression linear model for predicting the impact on lighting, cooling and heating

On a worldwide scale lighting accounts for 20% to 50% of buildings’ energy use and 19% of the global electricity consumption, and therefore represents a key opportunity for energy efficiency efforts in different countries due to its relevant impact and often short payback periods of investments. Among the various strategies developed to foster efficient lighting, daylight harvesting (i.e. the deployment of controls to reduce electric lighting based on available daylight in interior spaces) in combination with dynamic daylighting devices (i.e. windows and skylights able to modify their Visible Light Transmittance and Solar Heat Gain Coefficient) has shown dramatic potential for energy savings, peak electricity demand reduction and occupant visual comfort improvement. This paper is focused on daylight harvesting implementations utilizing fenestration systems that incorporate dynamic components, such as electrochromic glazing and operable louvers, assessing their impact on building energy performance and occupant visual comfort through advanced modeling techniques based on the EnergyPlus simulation engine. EnergyPlus is used in combination with the Building Controls Virtual Test Bed (BCVTB), which supports simulation of multiple fenestration and electric lighting control strategies, based on occupancy/vacancy and daylight availability. Results show dramatic savings potential on electric lighting (35-41%) and cooling (16-29%) loads, but also potential for significant increase in heating loads, especially in heating-dominated climates. Since case-by-case simulation is often not affordable for real buildings, parametric simulations are performed varying the values of key design and context parameters in JEPlus and the results are used to develop a linear multivariate regression model for predicting the impact of daylight harvesting strategies on electric lighting, cooling and heating loads as functions of a limited set of input parameters. This approach proves to be very useful for order-of-magnitude estimation of building energy requirements during the early, schematic phases of building design, as well as high-level analyses for investment and policy making goals. The approach is very suitable for the development of a quick and easy-to-use tool for such purposes