Estimating climate — Demand Nexus to support longterm adequacy planning in the energy sector
Estimating climate — Demand Nexus to support longterm adequacy planning in the energy sector
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估算气候——Demand Nexus 支持能源行业的长期充足性规划
DOI：
10.1109/pesgm.2017.8274648
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
R. Nateghi
中科院分区：
文献类型：
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作者：
S. Mukhopadhyay;R. Nateghi
由于未来政策、技术、土地利用和气候变化的高度不确定性，预测未来能源需求极其复杂。现有的能源经济工具（例如NEMS、MARKAL）可以在考虑不确定的技术、价格等因素的情况下预测未来的需求。但是，这些工具无法纳入与气候变化导致的需求变化相关的不确定性。在本文中，我们提出了一种贝叶斯非参数方法来捕获能源需求 - 气候关系。我们提出的模型的概率估计可以与现有模型集成以预测未来需求。贝叶斯框架还可以允许利益相关者根据专家知识对相关气候变量应用适当的先验概率。为了说明我们提出的方法的适用性，我们使用印第安纳州的住宅和商业能源部门作为案例研究。我们的结果表明最大持续风速、平均露点温度和降雪量是住宅和商业能源需求的最重要预测因素。
Projecting future energy demand is extremely complex, due to the highly uncertain future policy, technology, land-use and climate change. Existing energy-economy tools (e.g., NEMS, MARKAL) can project future demand accounting for factors such as uncertain technology, price, etc. However, these tools are incapable of incorporating uncertainties associated with shifts in demand due to climate change. In this paper, we propose a Bayesian non-parametric method to capture the energydemand — climate nexus. The probabilistic estimates from our proposed models can be integrated with existing models to project future demand. The Bayesian framework can also allow stakeholders to apply appropriate prior probabilities on relevant climate variables based on expert knowledge. To illustrate applicability of our proposed method, we used residential and commercial energy sectors in the state of Indiana as a case study. Our results indicate maximum-sustained-wind-speed, mean-dew-point-temperature and snowfall are the most important predictors of the residential and commercial energy-demands.