期刊
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
卷 9, 期 4, 页码 1540-1554出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSTE.2018.2794579
关键词
Forecast uncertainty; power system planning; statistical learning; technology adoption
资金
- Netherlands Enterprise Agency program TKI Switch2SmartGrids
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据