Classification of household devices by electricity usage profiles (original) (raw)

Appliance usage prediction using a time series based classification approach

IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society, 2012

Energy management for residential homes and offices require the prediction of the usage(s) or service request(s) of different appliances present in the house. The hardware requirement is more simplified and practical if the task is only based on energy consumption data and no other sensors are used. The proposed model tries to formalize such an approach using a time-series based multi-label classifier which takes into account correlation between different appliances among other factors. In this work, prediction results are shown for 1-hour in the future but this approach can be extended to predict more hours in the future as per the requirement(with restrictions). The learned models and decision tree showing the important factors in the input information is also discussed.

Machine learning approaches for electric appliance classification

2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA), 2012

We report on the development of an innovative system which can automatically recognize home appliances based on their electric consumption profiles. The purpose of our system is to apply adequate rules to control electric appliance in order to save energy and money. The novelty of our approach is in the use of plug-based low-end sensors that measure the electric consumption at low frequency, typically every 10 seconds. Another novelty is the use of machine learning approaches to perform the classification of the appliances. In this paper, we present the system architecture, the data acquisition protocol and the evaluation framework. More details are also given on the feature extraction and classification models being used. The evaluation showed promising results with a correct rate of identification of 85%.

Prediction of domestic appliances usage based on electrical consumption

Energy Informatics

Forecasting or modeling the on-off times of domestic appliances has gained increasing attention in recent years. However, comparing currently published results is difficult due to the many different data-sets and performance measures employed. In this paper, we evaluate the performance of three increasingly sophisticated approaches within a common framework on three data-sets each spanning 2 years. The approaches forecast the future on-off times of the appliances for the next 24 h on an hourly basis, solely based on historic energy consumption data. The appliances investigated are driven by user behavior and consume a significant fraction of the household's total electrical energy consumption. We find that for all algorithms the average area under curve (AUC) in the receiver operating characteristic (ROC) is in the range between 72% and 73%, i.e. indicating mediocre prediction quality. We conclude that historic consumption data alone is not sufficient for a good quality hourly forecast.

Noninvasive Detection of Appliance Utilization Patterns in Residential Electricity Demand

Energies, 2021

Smart meters with automatic meter reading functionalities are becoming popular across the world As a result, load measurements at various sampling frequencies are now available Several methods have been proposed to infer device usage characteristics from household load measurements However, many techniques are based on highly intensive computations that incur heavy computational costs;moreover, they often rely on private household information In this paper, we propose a technique for the detection of appliance utilization patterns using low-computational-cost algorithms that do not require any information about households Appliance utilization patterns are identified only from the system status behavior, represented by large system status datasets, by using dimensionality reduction and clustering algorithms Principal component analysis, k-means, and the elbow method are used to define the clusters, and the minimum spanning tree is used to visualize the results that show the appearan...

Non-Intrusive Electrical Appliances Monitoring and Classification using K-Nearest Neighbors

2019 2nd International Conference on Innovation in Engineering and Technology (ICIET), 2019

Non-Intrusive Load Monitoring (NILM) is the method of detecting an individual device's energy signal from an aggregated energy consumption signature [1]. As existing energy meters provide very little to no information regarding the energy consumptions of individual appliances apart from the aggregated power rating, the spotting of individual appliances' energy usages by NILM will not only provide consumers the feedback of appliance-specific energy usage but also lead to the changes of their consumption behavior which facilitate energy conservation. B Neenan et al. [2] have demonstrated that direct individual appliance-specific energy usage signals lead to consumers' behavioral changes which improves energy efficiency by as much as 15%. Upon disaggregation of an energy signal, the signal needs to be classified according to the appropriate appliance. Hence, the goal of this paper is to disaggregate total energy consumption data to individual appliance signature and then classify appliance-specific energy loads using a prominent supervised classification method known as K-Nearest Neighbors (KNN). To perform this operation we have used a publicly accessible dataset of power signals from several houses known as the REDD dataset. Before applying KNN, data is preprocessed for each device. Then KNN is applied to check whether their energy consumption signature is separable or not. KNN is applied with K=5.

ANN-based appliance recognition from low-frequency energy monitoring data

2013 IEEE 14th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM), 2013

The rational use and management of energy is a key objective for the evolution towards the smart grid. In particular in the private home domain the adoption of widescale energy consumption monitoring techniques can help end users in optimizing energy consumption behaviors. While most existing approaches for load disaggregation and classification requires high-frequency monitoring data, in this paper we propose an approach for detecting and identifying the appliances in use by analysing low-frequency monitoring data gathered by meters (i.e. smart plugs) distributed in the home. Our approach implements a supervised classification algorithm with artificial neural networks and has been tested with a dataset of power traces collected in real-world home settings.

Real-Time Recognition and Profiling of Appliances through a Single Electricity Sensor

2010 7th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON), 2010

Sensing, monitoring and actuating systems are expected to play a key role in reducing buildings overall energy consumption. Leveraging sensor systems to support energy efficiency in buildings poses novel research challenges in monitoring space usage, controlling devices, interfacing with smart energy meters and communicating with the energy grid. In the attempt of reducing electricity consumption in buildings, identifying individual sources of energy consumption is key to generate energy awareness and improve efficiency of available energy resources usage. Previous work studied several non-intrusive load monitoring techniques to classify appliances; however, the literature lacks of an comprehensive system that can be easily installed in existing buildings to empower users profiling, benchmarking and recognizing loads in real-time. This has been a major reason holding back the practice adoption of load monitoring techniques. In this paper we present RECAP: RECognition of electrical Appliances and Profiling in real-time. RECAP uses a single wireless energy monitoring sensor easily clipped to the main electrical unit. The energy monitoring unit transmits energy data wirelessly to a local machine for data processing and storage. The RECAP system consists of three parts: (1) Guiding the user for profiling electrical appliances within premises and generating a database of unique appliance signatures; (2) Using those signatures to train an artificial neural network that is then employed to recognize appliance activities (3) Providing a Load descriptor to allow peer appliance benchmarking. RECAP addresses the need of an integrated and intuitive tool to empower building owners with energy awareness. Enabling real-time appliance recognition is a stepping-stone towards reducing energy consumption and allowing a number of major applications including load-shifting techniques, energy expenditure breakdown per appliance, detection of power hungry and faulty appliances, and recognition of occupant activity. This paper describes the system design and performance evaluation in domestic environment.

Using pattern recognition to identify habitual behavior in residential electricity consumption

Energy and Buildings, 2012

Recognizing habitual behavior and providing feedback in context are key to empower individuals to take control over residential electricity consumption. Yet, it is a challenge to change habitual behavior, embedded in everyday routines. This paper intends to discover whether habitual behavior can be identi-fied by pattern recognition techniques. The data source is an experiment similar to a utility led advanced metering infrastructure implementation.

Classifying office plug load Appliance events in the context of nilm using Time-series data mining

Smart building energy management requires knowledge of individual appliance operation from reduced metering points. The key purpose of this study is to present a classification framework for offices that can help discover individual appliances and its operational modes from single-point aggregate measurements. This approach to non-intrusive load monitoring is supervised through labeled Office Plug Load Dataset. The classification approach is based on short episodes (also called subsequences) from time-series dataset within which appliance events lie hidden. A popular technique for discretizing time-series data known as Symbolic Aggregate approXimation lies at the heart of this framework. Mining large timeseries dataset, extracting characteristic appliance features and classifying them appropriately based on individual appliance events is facilitated through “Bag of Patterns” based Vector Space Model. This study focuses on classifying multiple events from three common aggregate appliance use-case scenarios in an office environment. The approach is promising at analyzing subsequence patterns from more than 1700 time-series episodes in the dataset. The results from classifying multi-functional device operations from aggregate signature show errors less than 22% in scenario where three appliances are in operation, whereas error is less than 37% when two appliances are in operation. The results also indicate that the approach is likely to work better as the dataset grows as in the case of big data. Additionally, the proposed approach enables visualizing subsequences of a timeseries using color-coding scheme. Such visualization helps in understanding the relative specificity of an event to others in the time series.

Extracting discriminative features for event-based electricity disaggregation

2014 IEEE Conference on Technologies for Sustainability (SusTech), 2014

We describe a novel method for electricity load disaggregation based on the machine learning method of time series shapelets. We frame the electricity disaggregation problem as that of event detection and event classification from time series data. We use existing shapelet-based algorithms to separate appliance activity periods (caused by switching on/off of appliances and denoted as events) from time periods without any such activity. We then identify which type of appliances in a household correspond to the events detected within the power consumption data. Such appliance-level feedback is critical for end-users in managing their energy use efficiently. We use the BLUED dataset for experimental evaluation of the proposed method. This dataset is a fully labeled publicly available dataset of electricity consumption of a household in the United States for one week, the data being recorded at a very high frequency and externally labeled with the times when specific appliances were switched on or off. The proposed approach is able to achieve approximately 98% accuracy for event detection and between 77% to 84% accuracy for event classification. The data segments that were identified as being most discriminative for electricity disaggregation are visually interpretable, and the appliances identified to be responsible for heavy energy consumption can be reported to consumers to encourage reduction in energy consumption.