A method for predicting city-wide electricity gains from photovoltaic panels based on LiDAR and GIS data combined with hourly Daysim simulations

Abstract

In this paper we present, demonstrate and validate a method for predicting city-wide electricity gains from photovoltaic panels based on detailed 3D urban massing models combined with Daysim-based hourly irradiation simulations, typical meteorological year climactic data and hourly calculated rooftop temperatures. The resulting data can be combined with online mapping technologies and search engines as well as a financial module that provides building owners interested in installing a photovoltaic system on their rooftop with meaningful data regarding spatial placement, system size, installation costs and financial payback. As a proof of concept, a photovoltaic potential map for the City of Cambridge, Massachusetts, USA, consisting of over 17,000 rooftops has been implemented as of September 2012.The new method constitutes the first linking of increasingly available GIS and LiDAR urban datasets with the validated building performance simulation engine Daysim, thus-far used primarily at the scale of individual buildings or small urban neighborhoods. A comparison of the new method with its predecessors reveals significant benefits as it produces hourly point irradiation data, supports better geometric accuracy, considers reflections from near by urban context and uses predicted rooftop temperatures to calculate hourly PV efficiency. A validation study of measured and simulated electricity yields from two rooftop PV installations in Cambridge shows that the new method is able to predict annual electricity gains within 3.6–5.3% of measured production when calibrating for actual weather data and detailed PV panel geometry. This predicted annual error using the new method is shown to be less than the variance which can be expected from climactic variation between years. Furthermore, because the new method generates hourly data, it can be applied to peak load mitigation studies at the urban level. This study also compares predicted monthly energy yields using the new method to those of preceding methods for the two validated test installations and on an annual basis for 10 buildings selected randomly from the Cambridge dataset.