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首页> 外文会议>International Symposium on Automation and Robotics in Construction and Mining >A Data-driven Framework to Estimate Saving Potential of Buildings in Demand Response Events
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A Data-driven Framework to Estimate Saving Potential of Buildings in Demand Response Events

机译：数据驱动框架，以估算需求响应事件中建筑物的节约潜力

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In the U.S., the increasing electricity demand gives pressure on the power grids because of its limited capacity to serve demand. Instead of building new power plants to meet the increasing demand, Demand Response (DR) programs incentivize end-consumers to reduce certain electricity demand during certain periods (e.g., peak demand and emergency times). In the current practice, saving potential of buildings, i.e., the amount of electricity that end-consumers can save during an event, is usually determined using the technical specifications of equipment installed, which is unrealistic and leads to over or underestimation of the expected saving potential. In this study, the authors developed a data-driven framework to quantify the electricity saving potential in buildings. The framework was applied to nineteen campus buildings. Several prediction algorithms were used to fit models to the integrated datasets of these buildings, and models were evaluated using four criteria to avoid over-fitting and under-fitting. The best performance of the models resulting in 0.86 of R~2, which represents high capability to quantify the electricity saving potential. The contribution of this study is the proposed data-driven framework, which provides facility operators with reliable tools to accurately quantify saving potential of buildings. The conducted case study using the framework on 19 test buildings showed that facility operators could avoid unnecessary penalties by eliminating them to sign up for unrealistic targets, and help them to gain the most value out of the DR programs by knowing the true potential of their buildings.
机译：在美国，由于其需求需求的能力有限，因此电力需求增加给电网压力。而不是建立新的发电厂以满足日益增长的需求，需求响应（DR）计划激励最终消费者在某些时期（例如，高峰需求和急诊时间）中降低某些电力需求。在目前的实践中，节省建筑物的潜力，即最终消费者在事件期间可以节省的电力量，通常使用安装的设备的技术规范确定，这是不现实的，并导致超过或低估预期节省潜在的。在这项研究中，作者开发了一种数据驱动的框架，以量化建筑物中的节电潜力。该框架应用于十九校区建筑物。使用几种预测算法将模型适合于这些建筑物的集成数据集，并且使用四个标准评估模型，以避免过度拟合和底层。模型的最佳性能导致0.86的R〜2，这代表了量化节电潜力的高能力。本研究的贡献是所提出的数据驱动框架，为设施运营商提供可靠的工具，以准确地量化建筑物的节省潜力。使用19个测试建筑物的框架进行的案例研究表明设施运营商可以通过消除不切实际的目标来避免不必要的惩罚，并通过了解其建筑物的真正潜力，帮助他们获得最大的价值。 。

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