Low-Power Appliance Monitoring Using Factorial Hidden Markov Models

To optimize the energy utilization, intelligent energy management solutions require appliance-specific consumption statistics. One can obtain such information by deploying smart power outlets on every device of interest, however it incurs extra hardware cost and installation complexity. Alternatively, a single sensor can be used to measure total electricity consumption and thereafter disaggregation algorithms can be applied to obtain appliance specific usage information. In such a case, it is quite challenging to discern low-power appliances in the presence of high-power loads. To improve the recognition of low-power appliance states, we propose a solution that makes use of circuit-level power measurements. We examine the use of a specialized variant of Hidden Markov Model (HMM) known as Factorial HMM (FHMM) to recognize appliance specific load patterns from the aggregated power measurements. Further, we demonstrate that feature concatenation can improve the disaggregation performance of the model allowing it to identify device states with an accuracy of 90% for binary and 80% for multi-state appliances. Through experimental evaluations, we show that our solution performs better than the traditional event based approach. In addition, we develop a prototype system that allows real-time monitoring of appliance states

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

Using hidden Markov models for iterative non-intrusive appliance monitoring

Non-intrusive appliance load monitoring is the process of breaking down a household’s total electricity consumption into its contributing appliances. In this paper we propose an approach by which individual appliances are iteratively separated from the aggregate load. Our approach does not require training data to be collected by sub-metering individual appliances. Instead, prior models of general appliance types are tuned to specific appliance instances using only signatures extracted from the aggregate load. The tuned appliance models are used to estimate each appliance’s load, which is subsequently subtracted from the aggregate load. We evaluate our approach using the REDD data set, and show that it can disaggregate 35% of a typical household’s total energy consumption to an accuracy of 83% by only disaggregating three of its highest energy consuming appliances

A comparison of generative and discriminative appliance recognition models for load monitoring

Appliance-level Load Monitoring (ALM) is essential, not only to optimize energy utilization, but also to promote energy awareness amongst consumers through real-time feedback mechanisms. Non-intrusive load monitoring is an attractive method to perform ALM that allows tracking of appliance states within the aggregated power measurements. It makes use of generative and discriminative machine learning models to perform load identification. However, particularly for low-power appliances, these algorithms achieve sub-optimal performance in a real world environment due to ambiguous overlapping of appliance power features. In our work, we report a performance comparison of generative and discriminative Appliance Recognition (AR) models for binary and multi-state appliance operations. Furthermore, it has been shown through experimental evaluations that a significant performance improvement in AR can be achieved if we make use of acoustic information generated as a by-product of appliance activity. We demonstrate that our a discriminative model FF-AR trained using a hybrid feature set which is a catenation of audio and power features improves the multi-state AR accuracy up to 10 %, in comparison to a generative FHMM-AR model

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

Integration of Legacy Appliances into Home Energy Management Systems

The progressive installation of renewable energy sources requires the coordination of energy consuming devices. At consumer level, this coordination can be done by a home energy management system (HEMS). Interoperability issues need to be solved among smart appliances as well as between smart and non-smart, i.e., legacy devices. We expect current standardization efforts to soon provide technologies to design smart appliances in order to cope with the current interoperability issues. Nevertheless, common electrical devices affect energy consumption significantly and therefore deserve consideration within energy management applications. This paper discusses the integration of smart and legacy devices into a generic system architecture and, subsequently, elaborates the requirements and components which are necessary to realize such an architecture including an application of load detection for the identification of running loads and their integration into existing HEM systems. We assess the feasibility of such an approach with a case study based on a measurement campaign on real households. We show how the information of detected appliances can be extracted in order to create device profiles allowing for their integration and management within a HEMS

Neural NILM: Deep Neural Networks Applied to Energy Disaggregation

Energy disaggregation estimates appliance-by-appliance electricity consumption from a single meter that measures the whole home's electricity demand. Recently, deep neural networks have driven remarkable improvements in classification performance in neighbouring machine learning fields such as image classification and automatic speech recognition. In this paper, we adapt three deep neural network architectures to energy disaggregation: 1) a form of recurrent neural network called `long short-term memory' (LSTM); 2) denoising autoencoders; and 3) a network which regresses the start time, end time and average power demand of each appliance activation. We use seven metrics to test the performance of these algorithms on real aggregate power data from five appliances. Tests are performed against a house not seen during training and against houses seen during training. We find that all three neural nets achieve better F1 scores (averaged over all five appliances) than either combinatorial optimisation or factorial hidden Markov models and that our neural net algorithms generalise well to an unseen house.Comment: To appear in ACM BuildSys'15, November 4--5, 2015, Seou

Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation

In this paper we evaluate several well-known and widely used machine learning algorithms for regression in the energy disaggregation task. Specifically, the Non-Intrusive Load Monitoring approach was considered and the K-Nearest-Neighbours, Support Vector Machines, Deep Neural Networks and Random Forest algorithms were evaluated across five datasets using seven different sets of statistical and electrical features. The experimental results demonstrated the importance of selecting both appropriate features and regression algorithms. Analysis on device level showed that linear devices can be disaggregated using statistical features, while for non-linear devices the use of electrical features significantly improves the disaggregation accuracy, as non-linear appliances have non-sinusoidal current draw and thus cannot be well parametrized only by their active power consumption. The best performance in terms of energy disaggregation accuracy was achieved by the Random Forest regression algorithm.Peer reviewedFinal Published versio