A Non-Intrusive Load Monitoring Approach for Very Short Term Power Predictions in Commercial Buildings
This work addresses energy management and efficiency for commercial building operators, but it is incremental as it builds on existing non-intrusive load monitoring techniques with specific improvements.
The paper tackles the problem of predicting short-term power usage in commercial buildings by developing an unsupervised algorithm to extract device profiles from aggregate power measurements and using them for disaggregation and forecasting, achieving a 1% energy error in reconstruction and outperforming baseline methods for a 15-minute horizon.
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.