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PROMT: predicting occupancy presence in multiple resolution with time-shift agnostic classification

机译：PROMT：使用时移不可知分类以多种分辨率预测入住人数

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摘要

Improving methods for predicting occupant presence in commercial buildings is crucial for optimizing energy consumption. Also it is crucial for providing amiable indoor environmental conditions. To enable these improvements, we require a more accurate and flexible framework for predicting occupancy. The PROMT framework proposed in this paper is an accurate and flexible framework for predicting occupancy presence in multiple resolution with time-shift agnostic classification. PROMT assumes that no single prediction algorithm, model, or static model parameter can guarantee high fidelity occupancy prediction for varying occupancy requirements and for every kind of rooms. Given this assumption, the PROMT framework facilitates the deployment of several prediction algorithms and it performs an hyper-parameter optimization procedure on all deployed algorithms to obtain the optimal model for obtaining occupancy prediction in covered room. promt was benchmarked with datasets from two building cases by comparing the F-score of the prediction results obtained from all deployed algorithms. The results document that PROMT outperforms the performance of any single prediction algorithm by a maximum difference in F-score of 2.3% and a minimum difference in F-score of 0.58%. As a case study we demonstrate the use of PROMT for scheduling demand response events in a commercial building.
机译：改善预测商业建筑中居民人数的方法对于优化能耗至关重要。对于提供友善的室内环境条件也至关重要。为了实现这些改进，我们需要一个更准确，更灵活的框架来预测占用率。本文提出的PROMT框架是一种准确而灵活的框架，用于通过时移不可知分类以多种分辨率预测占用情况。 PROMT假设，对于变化的入住要求和每种房间，没有单一的预测算法，模型或静态模型参数可以保证高保真的入住预测。在此假设的前提下，PROMT框架可促进多种预测算法的部署，并且它将对所有部署的算法执行超参数优化过程，以获得用于获得覆盖房间中的占用预测的最佳模型。通过比较从所有已部署算法获得的预测结果的F得分，使用来自两个构建案例的数据集对promt进行基准测试。结果表明，PROMT的F值最大差异为2.3％，F值最小差异为0.58％，优于任何单个预测算法。作为案例研究，我们演示了PROMT在商业建筑中调度需求响应事件的使用。

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