Energy disaggregation research at MLSUST 2013 workshop

The 2013 workshop on Machine Learning for Sustainability was recently held at at the NIPS conference in Lake Tahoe, NV, USA. The workshop was organised by Edwin Bonilla, NICTA and ANU, Tom Dietterich, Oregon State University, Theodoros Damoulas, NYU CUSP and NYU-Poly and Andreas Krause, ETH Zurich. The workshop invited papers which propose and apply machine learning algorithms to solve sustainability problems such as climate change, energy management and biodiversity monitoring. The workshop featured two poster sessions, in which the authors of the accepted papers were invited to present their work. Both poster sessions featured a paper on energy disaggregation, which I have briefly summarised below.

Interleaved Factorial Non-Homogeneous Hidden Markov Models for Energy Disaggregation. Mingjun Zhong, Nigel Goddard, Charles Sutton.

This paper proposes a method for disaggregating 2 minute energy consumption data into individual appliances. The approach is based upon an extension of the factorial hidden Markov model (FHMM), in which the appliance transition probabilities are dependent upon the time of day (non-homogeneous), and also the appliances are constrained such that only one appliance can change state per time slice (interleaved). The authors evaluate their approach on 100 homes from the Household Electricity Study, in which 20-30 days of sub-metered data from each household is used for training, while 5-10 days of data is held out for testing. The results show that both the interleaved and non-homogeneous extensions individually provide better performance than the basic FHMM, while a combination of the two provides the best performance. Finally, the authors identify a key finding in that the disaggregation accuracy varies greatly across different households, and raise this as an open problem for the NIALM community.

Using Step Variant Convolutional Neural Networks for Energy Disaggregation. Bingsheng Wang, Haili Dong, Chang-Tien Lu.

This paper proposes a method for disaggregating 15 minute interval aggregate energy data into individual appliances. The approach is based on Step Variant Convolutional Neural Networks (SVCNN), which use the aggregate energy consumption in the intervals t-2, t-1, t, t+1, t+2 to predict the energy consumption of each individual appliance in interval t. The authors evaluate their approach via cross validation using REDD, in which 3 houses are used to train the model while 2 other houses are used to test the performance. The results show that the SVCNN model achieves greater accuracy than both discriminative sparse coding models and factorial hidden Markov models. However, the results still show a relatively high whole home normalised disaggregation error of approximately 0.8, confirming the difficulty of the disaggregation of 15 minute energy data.

Further details on both the REDD and HES data sets are available in my post summarising the publicly available NIALM data sets.