Scalable Hybrid Classification-Regression Solution for High-Frequency Nonintrusive Load Monitoring

TL;DR

This paper introduces a scalable hybrid classification-regression machine learning approach for high-frequency nonintrusive load monitoring in residential buildings, enabling accurate, fast, and cost-effective net-load prediction without event detection.

Contribution

The paper presents a novel hybrid method combining classification and regression for high-frequency NILM, improving accuracy, speed, and scalability over existing approaches.

Findings

High accuracy in load state and power level prediction.

Operates at 60-Hz cycle prediction with response time within 160ms.

Effective for grid-interactive control at fast timescales.

Abstract

Residential buildings with the ability to monitor and control their net-load (sum of load and generation) can provide valuable flexibility to power grid operators. We present a novel multiclass nonintrusive load monitoring (NILM) approach that enables effective net-load monitoring capabilities at high-frequency with minimal additional equipment and cost. The proposed machine learning based solution provides accurate multiclass state predictions while operating at a faster timescale (able to provide a prediction for each 60-Hz ac cycle used in US power grid) without relying on event-detection techniques. We also introduce an innovative hybrid classification-regression method that allows for the prediction of not only load on/off states via classification but also individual load operating power levels via regression. A test bed with eight residential appliances is used for validating the NILM approach. Results show that the overall method has high accuracy and, good scaling and generalization properties. Furthermore, the method is shown to have sufficient response time (within 160ms, corresponding to 10 ac cycles) to support building grid-interactive control at fast timescales relevant to the provision of grid frequency support services.