A Non-Intrusive Load Monitoring Approach for Very Short Term Power Predictions in Commercial Buildings

TL;DR

This paper introduces a novel unsupervised algorithm for device profile extraction and short-term power prediction in commercial buildings, achieving high accuracy and outperforming existing methods for 15-minute forecasts.

Contribution

It presents a new unsupervised device profile extraction method combined with particle swarm optimization and a neural network for short-term power prediction in commercial buildings.

Findings

Device profiles extracted with 1% energy error

Disaggregation accurately reconstructs power data

Proposed prediction outperforms benchmarks

Abstract

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.