Unsupervised energy disaggregation using conditional random fields

The task of energy disaggregation is to break up total energy usage into usage by its component electrical appliances. This can provide home owners with useful feedback about how they use electrical energy, and motivate them to save significant amounts of energy. Our ultimate goal is to provide ways to modify human energy consumption behavior in order to conserve and optimize the use of energy. This paper focuses on stochastic modeling and energy disaggregation based on conditional random fields (CRFs) using real-world energy consumption data. The proposed disaggregation method uses a clustering method and histogram analysis to detect the ON/OFF states of selected types of energy-using devices in the home. Data labeling is not required, because the label sequences are obtained by applying a clustering method, and decoding using all of the data. Long spans of data from 21 households were used in a binary classification experiment, in which an 86.1% average classification accuracy was achieved. The proposed method was also evaluated using hidden Markov models (HMMs), but significantly higher accuracy was obtained when CRFs were applied.

[1]  S. Drenker,et al.  Nonintrusive monitoring of electric loads , 1999 .

[2]  A.J. Conejo,et al.  Day-ahead electricity price forecasting using the wavelet transform and ARIMA models , 2005, IEEE Transactions on Power Systems.

[3]  Chen-Ching Liu,et al.  Day-Ahead Electricity Price Forecasting in a Grid Environment , 2007, IEEE Transactions on Power Systems.

[4]  Tommi S. Jaakkola,et al.  Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation , 2012, AISTATS.

[5]  Manish Marwah,et al.  Unsupervised Disaggregation of Low Frequency Power Measurements , 2011, SDM.

[6]  Luis Pérez-Lombard,et al.  A review on buildings energy consumption information , 2008 .

[7]  Radu Zmeureanu,et al.  Using a pattern recognition approach to disaggregate the total electricity consumption in a house into the major end-uses , 1999 .

[8]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[9]  A.M. Gonzalez,et al.  Modeling and forecasting electricity prices with input/output hidden Markov models , 2005, IEEE Transactions on Power Systems.

[10]  M. Shahidehpour,et al.  A Hybrid Model for Day-Ahead Price Forecasting , 2010, IEEE Transactions on Power Systems.

[11]  Sundeep Pattem.  Unsupervised Disaggregation for Non-intrusive Load Monitoring , 2012, 2012 11th International Conference on Machine Learning and Applications.

[12]  Kazuya Takeda,et al.  Stochastic modeling and disaggregation of energy-consumption behavior , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[13]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[14]  L. Tesfatsion,et al.  Short-Term Congestion Forecasting in Wholesale Power Markets , 2011, IEEE Transactions on Power Systems.

[15]  Andrew Y. Ng,et al.  Energy Disaggregation via Discriminative Sparse Coding , 2010, NIPS.

[16]  J. Contreras,et al.  ARIMA Models to Predict Next-Day Electricity Prices , 2002, IEEE Power Engineering Review.

[17]  Shonali Krishnaswamy,et al.  Learning to be energy-wise: Discriminative methods for load disaggregation , 2012, 2012 Third International Conference on Future Systems: Where Energy, Computing and Communication Meet (e-Energy).

[18]  D. N. Geary.  Mixture Models: Inference and Applications to Clustering , 1989 .

[19]  J. Ramos,et al.  Electricity Market Price Forecasting Based on Weighted Nearest Neighbors Techniques , 2007, IEEE Transactions on Power Systems.