77 research outputs found
Interleaved Factorial Non-Homogeneous Hidden Markov Models for Energy Disaggregation
To reduce energy demand in households it is useful to know which electrical
appliances are in use at what times. Monitoring individual appliances is costly
and intrusive, whereas data on overall household electricity use is more easily
obtained. In this paper, we consider the energy disaggregation problem where a
household's electricity consumption is disaggregated into the component
appliances. The factorial hidden Markov model (FHMM) is a natural model to fit
this data. We enhance this generic model by introducing two constraints on the
state sequence of the FHMM. The first is to use a non-homogeneous Markov chain,
modelling how appliance usage varies over the day, and the other is to enforce
that at most one chain changes state at each time step. This yields a new model
which we call the interleaved factorial non-homogeneous hidden Markov model
(IFNHMM). We evaluated the ability of this model to perform disaggregation in
an ultra-low frequency setting, over a data set of 251 English households. In
this new setting, the IFNHMM outperforms the FHMM in terms of recovering the
energy used by the component appliances, due to that stronger constraints have
been imposed on the states of the hidden Markov chains. Interestingly, we find
that the variability in model performance across households is significant,
underscoring the importance of using larger scale data in the disaggregation
problem.Comment: 5 pages, 1 figure, conference, The NIPS workshop on Machine Learning
for Sustainability, Lake Tahoe, NV, USA, 201
Energy Disaggregation for Real-Time Building Flexibility Detection
Energy is a limited resource which has to be managed wisely, taking into
account both supply-demand matching and capacity constraints in the
distribution grid. One aspect of the smart energy management at the building
level is given by the problem of real-time detection of flexible demand
available. In this paper we propose the use of energy disaggregation techniques
to perform this task. Firstly, we investigate the use of existing
classification methods to perform energy disaggregation. A comparison is
performed between four classifiers, namely Naive Bayes, k-Nearest Neighbors,
Support Vector Machine and AdaBoost. Secondly, we propose the use of Restricted
Boltzmann Machine to automatically perform feature extraction. The extracted
features are then used as inputs to the four classifiers and consequently shown
to improve their accuracy. The efficiency of our approach is demonstrated on a
real database consisting of detailed appliance-level measurements with high
temporal resolution, which has been used for energy disaggregation in previous
studies, namely the REDD. The results show robustness and good generalization
capabilities to newly presented buildings with at least 96% accuracy.Comment: To appear in IEEE PES General Meeting, 2016, Boston, US
An application of autoregressive hidden Markov models for identifying machine operations
Due to increasing energy costs there is a need for accurate management and planning of shop floor machine processes. This would entail identifying the different operation modes of production machines. The goal for industry is to provide energy monitors for all machines in factories. In addition, where they have been deployed, analysis is limited to aggregating data for subsequent processing later. In this paper, an Autoregressive Hidden Markov Model (ARHMM)-based algorithm is introduced, which can determine the operation mode of the machine in real-time and find direct application in intrusive load monitoring cases. Compared with other load monitoring techniques, such as transient analysis, no prior knowledge of the system to be monitored is required
Energy Disaggregation via Adaptive Filtering
The energy disaggregation problem is recovering device level power
consumption signals from the aggregate power consumption signal for a building.
We show in this paper how the disaggregation problem can be reformulated as an
adaptive filtering problem. This gives both a novel disaggregation algorithm
and a better theoretical understanding for disaggregation. In particular, we
show how the disaggregation problem can be solved online using a filter bank
and discuss its optimality.Comment: Submitted to 51st Annual Allerton Conference on Communication,
Control, and Computin
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