Evaluating regional climate-electricity demand nexus: A composite Bayesian predictive framework
Evaluating regional climate-electricity demand nexus: A composite Bayesian predictive framework
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DOI：
10.1016/j.apenergy.2018.10.119
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发表时间：
2019-02-01
期刊：
影响因子：
11.2
通讯作者：
Nateghi, Roshanak
中科院分区：
文献类型：
--
作者：
Mukherjee, Sayanti;Vineeth, C. R.;Nateghi, Roshanak
气候变化显著影响终端用电需求曲线的形状。尽管终端电力需求对气候的敏感性是公认的，但预测中长期未来需求仍然是一项重大挑战，主要是因为建模过程中涉及到许多不确定性。在这篇文章中，我们利用最先进的贝叶斯方法来开发严格验证的气候需求关系的区域预测模型，该模型以需求强度水平为条件。预测模型是为美国前八个能源密集型州的住宅和商业部门开发的。这项工作的一个关键贡献是说明了负荷对气候的敏感性的不对称性。更具体地说，我们的结果表明，与中等强度的最终用途需求相比，高强度最终用途需求对气候变化的敏感性更高。此外，我们的结果有助于确定平均露点温度作为住宅和商业电力需求的气候敏感部分的关键预测因子，而不考虑需求强度水平。风速被确定为高强度(即第3个四分位数)最终用户需求的第二个最重要的预测因素，而电价被发现是中等强度(即，
Climatic variations significantly influence the shape of end-use electricity demand curves. Although the climate sensitivity of end-use electricity demand is well-established, projecting medium- and long-term future demand remains a significant challenge mostly due to a multitude of uncertainties involved in the modeling process. In this paper, we leveraged a state-of-the-art Bayesian approach to develop rigorously validated regional prediction models of the climate demand nexus conditioned on the intensity level of demand. The prediction models were developed for the residential and commercial sectors for the top eight energy-intensive states in the U.S. A key contribution of this work was to illustrate the asymmetry in the sensitivity of load to climate. More specifically, our results demonstrated a greater sensitivity of the high-intensity end-use demand to climate variability as compared to the moderate-intensity end-use demand. In addition, our results helped identify mean dew point temperature as the key predictor of the climate-sensitive portion of both residential and commercial electricity demands, irrespective of the demand intensity levels. Wind speed was identified as the second most important predictor of the high-intensity (i.e., >= 3rd quartile) end-use demand, while electricity price was found to be the key predictor of the moderate-intensity (i.e.,