Probabilistic evaluation of UK domestic solar photovoltaic systems: An integrated geographical information system PV estimation tool
LeicesterPhilip
DoylendNicholas
RowleyPaul
2016
It is shown how key predictor parameters for the spatial estimation of PV yield, self
-consumption and thereby economic and social indicators can be extracted from a GIS system and introduced into a Bayesian Network model. This model endogenises the uncertainties and incorporates spatial variability inherent in these parameters. Empirical monthly and annual yield measurements obtained from over 600 PV installations have been obtained and
compared with estimated yields obtained by two key solar tools used for performance estimation in the UK â€“ these are PVGIS and the UK Governmentâ€™s Standard Assessment Procedure (SAP) for domestic buildings. Mean bias estimates and
root mean square error estimations were obtained for each tool and the results used to construct an uncertainty distribution in PV yield prediction given key input parameters such as system rating, orientation and tilt. This uncertainty was used to furnish a probabilistic graphical model with a prior distribution for PV yield estimation. This was integrated into a Geographical Information (GIS) system furnished with roof and building stock parameters including roof attributes obtained from lidar data. Elements held in a vector layer of the GIS system can be selected and the resultant distributions of input parameters automatically fed to the model to yield a posterior distribution of
the PV yield. The model is able to propagate the yield uncertainty to other probabilistic models, including ones which predict the internal rate of return and self
-consumption. The latter is in turn predicted by empirical marginal
distributions of domestic electricity consumption. Thus with a given posterior distributions of PV yield, new posterior
distributions for the internal rate of return, self-consumption and carbon
emission savings are automatically
calculated. By integration with GIS this novel approach allows the spatial analysis of the uncertainty pertaining to representative risk factors for PV adoption in the UK, and facilitate the estimation by installers, investors, and local authorities in a manner which endogenises uncertainty.