论文标题
能源系统规划中的贝叶斯决策支持系统
A Bayesian Decision Support System in Energy Systems Planning
论文作者
论文摘要
高斯工艺（GP）模拟器被广泛用于近似输入空间的复杂计算机模型行为。在耦合计算机模型的问题上，最近在连接的GP模拟器网络的分析理论中取得了进步。在本文中，我们将这些最新的方法论进步与经典状态空间模型结合在一起，以构建贝叶斯决策支持系统。这种方法提供了一个连贯的概率模型，该模型与两个第一矩的不确定性量度产生预测，并可以从单个决策组件中传播不确定性。 该方法用于为英国县议会提供决策支持工具，该工具考虑到低碳技术来改变其基础设施以达到零碳目标。特别是，我们演示了如何从能源模型，供暖需求模型以及天然气和电价时间序列中求助信息，以定量评估对各种政策选择和能源市场变化的运营成本的影响。
Gaussian Process (GP) emulators are widely used to approximate complex computer model behaviour across the input space. Motivated by the problem of coupling computer models, recently progress has been made in the theory of the analysis of networks of connected GP emulators. In this paper, we combine these recent methodological advances with classical state-space models to construct a Bayesian decision support system. This approach gives a coherent probability model that produces predictions with the measure of uncertainty in terms of two first moments and enables the propagation of uncertainty from individual decision components. This methodology is used to produce a decision support tool for a UK county council considering low carbon technologies to transform its infrastructure to reach a net-zero carbon target. In particular, we demonstrate how to couple information from an energy model, a heating demand model, and gas and electricity price time-series to quantitatively assess the impact on operational costs of various policy choices and changes in the energy market.
