Open AccessProbabilistic analysis of solar photovoltaic self‐consumption using Bayesian network models
Author(s) -
Leicester Philip A.,
Goodier Chris I.,
Rowley Paul N.
Publication year - 2016
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/iet-rpg.2015.0360
Subject(s) - photovoltaic system , consumption (sociology) , environmental science , probabilistic logic , quartile , population , range (aeronautics) , statistics , sample (material) , probabilistic analysis of algorithms , computer science , econometrics , engineering , mathematics , electrical engineering , confidence interval , social science , chemistry , demography , chromatography , aerospace engineering , sociology
To assess the systemic value and impacts of multiple photovoltaic (PV) systems in urban areas, detailed analysis of on‐site electricity consumption and of solar PV yield at relatively high temporal resolution is required, together with an understanding of the impacts of stochastic variations in consumption and PV generation. In this study, measured and simulated time‐series data for consumption and PV generation at 5 and 1 min resolution for a large number of domestic PV systems are analysed, and a statistical evaluation of self‐consumption (SC) carried out. The results show a significant variability of annual PV SC across the sample population, with typical median annual SC of 31% and inter‐quartile range of 22–44%. About 10% of the dwellings exceed an SC of 60% with 10% achieving 14% or less. The results have been used to construct a Bayesian network model capable of probabilistically analysing SC given consumption and PV generation. This model provides a basis for rapid detailed analysis of the techno‐economic characteristics and socio‐economic impacts of PV in a range of built environment contexts, from single building to district scales.