论文标题
商业建筑中非常短期功率预测的一种非侵入性负载监控方法
A Non-Intrusive Load Monitoring Approach for Very Short Term Power Predictions in Commercial Buildings
论文作者
论文摘要
本文提出了一种新的算法，以从三个阶段完全不受监督为反应性和主动骨料功率测量的设备曲线。提取的设备轮廓用于使用粒子群优化的骨料功率测量进行分解。最后，本文使用分类数据为短期功率预测提供了一种新方法。为此，人造神经网络对每个设备进行了变化的预测，并通过重建有关状态变化和设备概况的功率后转换为功率预测。预测范围为15分钟。为了证明开发的方法，使用了多租户商业建筑的三个相应性和主动的骨料测量。数据的粒度为1 s。在这项工作中，从总功率数据中提取了52个设备配置文件。分解显示了测得的功率非常准确的重建，其百分比的能量误差约为1％。所开发的间接功率预测方法应用于测量的功率数据，优于两个持久性预测和一个人工神经网络，该神经网络专为在功率域中工作的24小时天数预测设计。
This paper presents a new algorithm to extract device profiles fully unsupervised from three phases reactive and active aggregate power measurements. The extracted device profiles are applied for the disaggregation of the aggregate power measurements using particle swarm optimization. Finally, this paper provides a new approach for short term power predictions using the disaggregation data. For this purpose, a state changes forecast for every device is carried out by an artificial neural network and converted into a power prediction afterwards by reconstructing the power regarding the state changes and the device profiles. The forecast horizon is 15 minutes. To demonstrate the developed approaches, three phase reactive and active aggregate power measurements of a multi-tenant commercial building are used. The granularity of data is 1 s. In this work, 52 device profiles are extracted from the aggregate power data. The disaggregation shows a very accurate reconstruction of the measured power with a percentage energy error of approximately 1 %. The developed indirect power prediction method applied to the measured power data outperforms two persistence forecasts and an artificial neural network, which is designed for 24h-day-ahead power predictions working in the power domain.
