消除能源模型网络中的不确定性
Propagating uncertainty in a network of energy models
摘要：
计算机模型广泛用于能源系统运行、规划和政策的决策支持。通常采用模型系统，其中模型输入本身来自其他计算机模型，每个模型由不同的专家团队开发。高斯过程仿真器可用于近似复杂的计算密集型模型的行为，并用于生成预测以及预测模型输出的不确定性度量。本文提出了一种计算效率高的框架内传播的不确定性与高维输出用于能源规划的模型网络。我们提出了一个案例研究，从英国县议会考虑低碳技术改造其基础设施，以达到净零碳目标。本案例研究所考虑的系统模型很简单，但是该框架可以应用于更复杂模型的更大网络。
英文摘要：
Computer models are widely used in decision support for energy systems operation, planning and policy. A system of models is often employed, where model inputs themselves arise from other computer models, with each model being developed by different teams of experts. Gaussian Process emulators can be used to approximate the behaviour of complex, computationally intensive models and used to generate predictions together with a measure of uncertainty about the predicted model output. This paper presents a computationally efficient framework for propagating uncertainty within a network of models with high-dimensional outputs used for energy planning. We present a case study from a UK county council considering low carbon technologies to transform its infrastructure to reach a net-zero carbon target. The system model considered for this case study is simple, however the framework can be applied to larger networks of more complex models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
