Climate sensitivity of end-use electricity consumption in the built environment: An application to the state of Florida, United States

Climate sensitivity of end-use electricity consumption in the built environment: An application to the state of Florida, United States
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DOI:
10.1016/j.energy.2017.04.034
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发表时间:
2017-06-01
期刊:
影响因子:
9
通讯作者:
Nateghi, Roshanak
Nateghi, Roshanak
中科院分区:
工程技术1区
文献类型:
--
作者:
Mukhopadhyay, Sayanti;Nateghi, Roshanak

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气候与空间调节所需能源之间的联系已被广泛研究,但仍存在不确定性。预计的气候变化影响可能会增加极端天气和气候发生的频率,这进一步加剧了不确定性。与之前的方法不同,我们利用非线性统计学习方法来开发住宅和商业用电量的预测模型,以了解天气、气候和电力消耗之间的关系。我们的分析是在佛罗里达州实施的,佛罗里达州是能源密集度最高的州之一,地理和人口都容易受到气候变化的影响。我们的研究结果表明,贝叶斯加性回归树(BART)方法最能捕捉数据的复杂结构。我们得出结论,平均露点温度比最广泛使用的度-天变量更适合预测气候敏感负荷。此外,风速和降水是电力消费气候敏感部分的关键预测因子。与商业部门相比,居民用电量具有更强的时空异质性,且受消费者行为的影响更大。(C) 2017 Elsevier Ltd.版权所有。
The link between climate and energy needed for space conditioning has been widely studied, but is still beset with uncertainties. The uncertainties are further compounded by the projected climate change impacts, which will likely increase the frequency of weather and climate extremes. Unlike previous approaches, we leveraged non-linear statistical learning methods to develop predictive models for residential and commercial electricity usage to understand the relationship between weather, climate and electric power consumption. Our analyses were implemented for the state of Florida, one of the most energy intensive states with geographic and demographic vulnerability to climatic change. Our results indicate that the method of Bayesian additive regression trees (BART) best captures the complex structure of the data. We conclude that mean dew point temperature is a more suitable predictor of climate sensitive load than the most widely used degree-days variables. Moreover, wind-speed and precipitation are key predictors of the climate sensitive portion of electricity consumption. We also find that the residential electricity consumption has more spatio-temporal heterogeneity and is more influenced by consumer behavior than the commercial sector. (C) 2017 Elsevier Ltd. All rights reserved.