Development and implementation of statistical models for estimating diversified adoption of energy transition technologies

R. Bernards, J. Morren, J.G. Slootweg

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Scopus)
114 Downloads (Pure)

Abstract

For efficient network investments, insight in the expected spatial spread of new load and generation units is of prime importance. This paper presents and applies a method to determine key factors for adoption of photovoltaics, electric vehicles and heat pumps. Using a logistic regression analysis the impact of geographical and socio-economic factors on adoption probabilities of these new energy technologies is quantified. Income level, average age and household composition are shown to be important factors. Additionally for photovoltaics, peer effects were also shown to significantly influence the likelihood of adoption. The implementation of the developed models and the achievable improvement in prediction accuracy is demonstrated by application to a scenario study based on historical data. The models can be incorporated in future energy scenarios to provide a probabilistic spatial forecast of future penetration levels of the mentioned technologies and identify key areas of interest.

Original languageEnglish
Pages (from-to)1540-1554
JournalIEEE Transactions on Sustainable Energy
Volume9
Issue number4
DOIs
Publication statusPublished - 1 Oct 2018

Fingerprint

Electric vehicles
Regression analysis
Logistics
Pumps
Economics
Chemical analysis
Statistical Models
Hot Temperature

Keywords

  • Adaptation models
  • Buildings
  • Forecast uncertainty
  • Investment
  • Planning
  • Power system planning
  • Predictive models
  • Probabilistic logic
  • Resistance heating
  • Statistical learning
  • Technology adoption

Cite this

@article{29d8f34794e74508a1d15f436127b880,
title = "Development and implementation of statistical models for estimating diversified adoption of energy transition technologies",
abstract = "For efficient network investments, insight in the expected spatial spread of new load and generation units is of prime importance. This paper presents and applies a method to determine key factors for adoption of photovoltaics, electric vehicles and heat pumps. Using a logistic regression analysis the impact of geographical and socio-economic factors on adoption probabilities of these new energy technologies is quantified. Income level, average age and household composition are shown to be important factors. Additionally for photovoltaics, peer effects were also shown to significantly influence the likelihood of adoption. The implementation of the developed models and the achievable improvement in prediction accuracy is demonstrated by application to a scenario study based on historical data. The models can be incorporated in future energy scenarios to provide a probabilistic spatial forecast of future penetration levels of the mentioned technologies and identify key areas of interest.",
keywords = "Adaptation models, Buildings, Forecast uncertainty, Investment, Planning, Power system planning, Predictive models, Probabilistic logic, Resistance heating, Statistical learning, Technology adoption",
author = "R. Bernards and J. Morren and J.G. Slootweg",
year = "2018",
month = "10",
day = "1",
doi = "10.1109/TSTE.2018.2794579",
language = "English",
volume = "9",
pages = "1540--1554",
journal = "IEEE Transactions on Sustainable Energy",
issn = "1949-3029",
publisher = "Institute of Electrical and Electronics Engineers",
number = "4",

}

Development and implementation of statistical models for estimating diversified adoption of energy transition technologies. / Bernards, R.; Morren, J.; Slootweg, J.G.

In: IEEE Transactions on Sustainable Energy, Vol. 9, No. 4, 01.10.2018, p. 1540-1554.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Development and implementation of statistical models for estimating diversified adoption of energy transition technologies

AU - Bernards, R.

AU - Morren, J.

AU - Slootweg, J.G.

PY - 2018/10/1

Y1 - 2018/10/1

N2 - For efficient network investments, insight in the expected spatial spread of new load and generation units is of prime importance. This paper presents and applies a method to determine key factors for adoption of photovoltaics, electric vehicles and heat pumps. Using a logistic regression analysis the impact of geographical and socio-economic factors on adoption probabilities of these new energy technologies is quantified. Income level, average age and household composition are shown to be important factors. Additionally for photovoltaics, peer effects were also shown to significantly influence the likelihood of adoption. The implementation of the developed models and the achievable improvement in prediction accuracy is demonstrated by application to a scenario study based on historical data. The models can be incorporated in future energy scenarios to provide a probabilistic spatial forecast of future penetration levels of the mentioned technologies and identify key areas of interest.

AB - For efficient network investments, insight in the expected spatial spread of new load and generation units is of prime importance. This paper presents and applies a method to determine key factors for adoption of photovoltaics, electric vehicles and heat pumps. Using a logistic regression analysis the impact of geographical and socio-economic factors on adoption probabilities of these new energy technologies is quantified. Income level, average age and household composition are shown to be important factors. Additionally for photovoltaics, peer effects were also shown to significantly influence the likelihood of adoption. The implementation of the developed models and the achievable improvement in prediction accuracy is demonstrated by application to a scenario study based on historical data. The models can be incorporated in future energy scenarios to provide a probabilistic spatial forecast of future penetration levels of the mentioned technologies and identify key areas of interest.

KW - Adaptation models

KW - Buildings

KW - Forecast uncertainty

KW - Investment

KW - Planning

KW - Power system planning

KW - Predictive models

KW - Probabilistic logic

KW - Resistance heating

KW - Statistical learning

KW - Technology adoption

UR - http://www.scopus.com/inward/record.url?scp=85041640829&partnerID=8YFLogxK

U2 - 10.1109/TSTE.2018.2794579

DO - 10.1109/TSTE.2018.2794579

M3 - Article

AN - SCOPUS:85041640829

VL - 9

SP - 1540

EP - 1554

JO - IEEE Transactions on Sustainable Energy

JF - IEEE Transactions on Sustainable Energy

SN - 1949-3029

IS - 4

ER -