An Energy Prediction Approach for a Nonintrusive Load Monitoring in Home Appliances

Abstract

Home energy monitoring by appliance-level information can provide consumers awareness on energy saving. The system can be implemented through a smart meter which requires an efficient data analysis algorithm for providing an accurate energy consumption profile, the purpose for proper home energy management. This article proposes a set of data analysis procedures for extracting appliances power state from its power consumption data. The approach is based on multitarget classification, a new data learning framework for nonintrusive load monitoring. The procedures include: 1) partitioning the appliance power data into an effective number of power states using K-means clustering, and 2) determining the optimal number of power states using the Area Under the ROC Curve index. The design objective is to obtain the optimal predictive performance for identification of the appliance power state which could result in a proper power and energy prediction. Applying the multitarget classification algorithm of RAndom k-labELsets by disjoint subsets with the decision tree, the identification of appliance power state achieved F-score and accuracy values greater than 89% for high-power loads such as A/C and water heater. The normalized error values of power prediction outperformed the use of Factorial Hidden Markov Model and binary state modeling system.