Robust space–time modeling of solar photovoltaic deployment

Solar photovoltaic (PV) has established itself as a fairly promising, fast-growing renewable energy source. The main determinants of solar PV deployment are thought to be physical and climatic factors – such as latitude and solar irradiance, not to mention terrain and built environment features – as...

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Autores principales:	Sergio Copiello, Carlo Grillenzoni
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Photovoltaic systems Renewable energy sources Socio-economic determinants Serial and spatial dependence Robust estimation Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/3c5816c7fb504b5a927a97d675d7b09f
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spelling	oai:doaj.org-article:3c5816c7fb504b5a927a97d675d7b09f2021-11-18T04:49:32ZRobust space–time modeling of solar photovoltaic deployment2352-484710.1016/j.egyr.2021.07.087https://doaj.org/article/3c5816c7fb504b5a927a97d675d7b09f2021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352484721005515https://doaj.org/toc/2352-4847Solar photovoltaic (PV) has established itself as a fairly promising, fast-growing renewable energy source. The main determinants of solar PV deployment are thought to be physical and climatic factors – such as latitude and solar irradiance, not to mention terrain and built environment features – as well as socio-economic drivers — such as population density, household size, and education level. Besides, peer effects and neighborhood effects are found to affect the willingness to adopt solar photovoltaic systems strongly. This study aims to set up robust space–time models, which enable us to investigate the drivers of solar PV deployment using fine-grained spatial and temporal data. We use space–time auto-regressive models (STAR) with several exogenous covariates that are expected to explain the installed solar PV capacity. STAR models require the specification of spatial weight matrices (W). As in regular lattice data, we select causal (lower triangular) W matrices so that the consistency of least-squares (LS) estimators is warranted. We show that they can be extended to robust LS estimators, which are necessary because of strong outlier contamination. Models are tested on the Italian municipal data of residential and industrial PV plants installed under the support schemes in force between 2006 and 2011. Empirical results confirm the important role played by the space–time dynamic components. Significant exogenous predictors are found in the domains of the physical features (elevation and land area), demography (population), built environment (residential buildings), and socio-economic aspects (income, employment rate, commuter workers).Sergio CopielloCarlo GrillenzoniElsevierarticlePhotovoltaic systemsRenewable energy sourcesSocio-economic determinantsSerial and spatial dependenceRobust estimationElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENEnergy Reports, Vol 7, Iss , Pp 657-676 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Photovoltaic systems Renewable energy sources Socio-economic determinants Serial and spatial dependence Robust estimation Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Photovoltaic systems Renewable energy sources Socio-economic determinants Serial and spatial dependence Robust estimation Electrical engineering. Electronics. Nuclear engineering TK1-9971 Sergio Copiello Carlo Grillenzoni Robust space–time modeling of solar photovoltaic deployment
description	Solar photovoltaic (PV) has established itself as a fairly promising, fast-growing renewable energy source. The main determinants of solar PV deployment are thought to be physical and climatic factors – such as latitude and solar irradiance, not to mention terrain and built environment features – as well as socio-economic drivers — such as population density, household size, and education level. Besides, peer effects and neighborhood effects are found to affect the willingness to adopt solar photovoltaic systems strongly. This study aims to set up robust space–time models, which enable us to investigate the drivers of solar PV deployment using fine-grained spatial and temporal data. We use space–time auto-regressive models (STAR) with several exogenous covariates that are expected to explain the installed solar PV capacity. STAR models require the specification of spatial weight matrices (W). As in regular lattice data, we select causal (lower triangular) W matrices so that the consistency of least-squares (LS) estimators is warranted. We show that they can be extended to robust LS estimators, which are necessary because of strong outlier contamination. Models are tested on the Italian municipal data of residential and industrial PV plants installed under the support schemes in force between 2006 and 2011. Empirical results confirm the important role played by the space–time dynamic components. Significant exogenous predictors are found in the domains of the physical features (elevation and land area), demography (population), built environment (residential buildings), and socio-economic aspects (income, employment rate, commuter workers).
format	article
author	Sergio Copiello Carlo Grillenzoni
author_facet	Sergio Copiello Carlo Grillenzoni
author_sort	Sergio Copiello
title	Robust space–time modeling of solar photovoltaic deployment
title_short	Robust space–time modeling of solar photovoltaic deployment
title_full	Robust space–time modeling of solar photovoltaic deployment
title_fullStr	Robust space–time modeling of solar photovoltaic deployment
title_full_unstemmed	Robust space–time modeling of solar photovoltaic deployment
title_sort	robust space–time modeling of solar photovoltaic deployment
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/3c5816c7fb504b5a927a97d675d7b09f
work_keys_str_mv	AT sergiocopiello robustspacetimemodelingofsolarphotovoltaicdeployment AT carlogrillenzoni robustspacetimemodelingofsolarphotovoltaicdeployment
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Robust space–time modeling of solar photovoltaic deployment

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