Simulating Urban Environments for Energy Analysis

Abstract

We present new prototype tools for optimizing building solar energy impacts in urban regions, to enable better real-time control and policy decisions for energy supply and demand response. The concept is demonstrated with a prototype that estimates the amount of direct sunlight available to building surfaces in complex urban landscapes, taking into consideration local weather predictions (via cloud cover simulation). We also calculate partial shadows from visual obstructions, due to their effect on the availability of solar energy and building energy usage. The prototype has the potential to make better day-ahead predictions that can help balance energy supply and demand during peak load hours. This can lead to better strategies for control of heating, air conditioning and alternatives (such as local energy storage in batteries or co-generation) to offset peak energy demand. However, in addition it can be used as a statistical optimization tool for informing local policy decisions related to solar energy incentives and demand response programs. We apply the approach to a prototype calculation on models of a hypothetical city and a section of downtown San Francisco. We briefly discuss optimization opportunities in response to the variability and uncertainty in solar energy for individual buildings in an urban landscape.