Data Mining Techniques for Detecting Household Characteristics Based on Smart Meter Data (original) (raw)

Revealing household characteristics from smart meter data

Energy, 2014

Utilities are currently deploying smart electricity meters in millions of households worldwide to collect fine-grained electricity consumption data. We present an approach to automatically analyzing this data to enable personalized and scalable energy efficiency programs for private households. In particular, we develop and evaluate a system that uses supervised machine learning techniques to automatically estimate specific "characteristics" of a household from its electricity consumption. The characteristics are related to a household's socioeconomic status, its dwelling, or its appliance stock. We evaluate our approach by analyzing smart meter data collected from 4,232 households in Ireland at a 30-minute granularity over a period of 1.5 years. Our analysis shows that revealing characteristics from smart meter data is feasible, as our method achieves an accuracy of more than 70% over all households for many of the characteristics and even exceeds 80% for some of the characteristics. The findings are applicable to all smart metering systems without making changes to the measurement infrastructure. The inferred knowledge paves the way for targeted energy efficiency programs and other services that benefit from improved customer insights. On the basis of these promising results, the paper discusses the potential for utilities as well as policy and privacy implications.

Lessons Learnt from Mining Meter Data of Residential Consumers

Periodica polytechnica. Electrical engineering and computer science /, 2016

Tracking end-users' usage patterns can enable more accurate demand forecasting and the automation of demand response execution. Accordingly, more advanced applications, such as electricity market design, integration of distributed generation and theft detection can be developed. By employing data mining techniques on smart meter recordings, the suppliers can efficiently investigate the load patterns of consumers. This paper presents applications where data mining of energy usage can derive useful information. Higher demands, on one side, and the energy price increase on the other side, have caused serious issues with regards to electricity theft, especially among developing countries. This phenomenon leads to considerable operational losses within the electrical network. In order to identify illegal residential consumers, a new method of analysing and identifying electricity consumption patterns of consumers is proposed in this paper. Moreover, the importance of data mining for analysing the consumer's usage curves was investigated. This helps to determine the behaviour of endusers for demand response purposes and improve the reliability and security of the electricity network. Clustering load profiles for large scale energy datasets are discussed in detail.

Residential appliance identification and future usage prediction from smart meter

Energy management for residential homes and/or offices requires both identification and prediction of the future usages or service requests of different appliances present in the buildings. The aim of this work is to identify residential appliances from aggregate reading at the smart meter and to predict their states in order to minimize their energy consumption. For this purpose, our work is divided in two distinct modules: Appliance identification and future usage prediction. Both identification and prediction are based on multi-label learners which takes inter-appliance co-relation into account. The first part of the paper concerns the identification of electrical appliance usages from the smart meter monitoring. The main objective is to be able to identify individual loads from the aggregate power consumption in a non-intrusive manner. In this work, high energy consuming appliances are identified at 1-hour sampling rate using novel set of meta-features for this domain. The second part of the paper concerns future usage prediction. A comparison of algorithms for future appliance usage prediction using identification and direct consumption reading is presented. This work is based on a real residential dataset, called IRISE: 100 houses monitored every 10 minutes to one hour during one year (including weather informations).

Deducing Energy Consumer Behavior from Smart Meter Data

Future Internet

The ongoing upgrade of electricity meters to smart ones has opened a new market of intelligent services to analyze the recorded meter data. This paper introduces an open architecture and a unified framework for deducing user behavior from its smart main electricity meter data and presenting the results in a natural language. The framework allows a fast exploration and integration of a variety of machine learning algorithms combined with data recovery mechanisms for improving the recognition's accuracy. Consequently, the framework generates natural language reports of the user's behavior from the recognized home appliances. The framework uses open standard interfaces for exchanging data. The framework has been validated through comprehensive experiments that are related to an European Smart Grid project.

An efficient algorithm for extracting appliance-time association using smart meter data

Heliyon, 2019

Demand Response (DR) programs play a significant role for developing energy management solutions. Gaining home residents trust and respecting their appliances usage preferences are essential factors for promoting these programs. Extracting resident's usage behaviour is a challenging task with the infinite massive amount of data being generated from smart meters. The main contribution of this paper is to extract temporal association patterns of energy consumption at appliance level. The proposed approach extends the Utility-oriented Temporal Association Rules Mining (UTARM) algorithm to discover appliances usage preference at a time. The results achieved from the proposed work succeeded to discover appliance-time association considering appliances usage priority as a utility factor with respect to the 24-hours of the day as a temporal partitioning factor.

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...

Mining Temporal Patterns to Discover Inter-Appliance Associations Using Smart Meter Data

Big Data and Cognitive Computing, 2019

With the emergence of the smart grid environment, smart meters are considered one of the main key enablers for developing energy management solutions in residential home premises. Power consumption in the residential sector is affected by the behavior of home residents through using their home appliances. Respecting such behavior and preferences is essential for developing demand response programs. The main contribution of this paper is to discover the association between appliances’ usage through mining temporal association rules in addition to applying the temporal clustering technique for grouping appliances with similar usage at a particular time. The proposed method is applied on a time-series dataset, which is the United Kingdom Domestic Appliance-Level Electricity (UK-DALE), and the results that are achieved discovered appliance–appliance associations that have similar usage patterns with respect to the 24 h of the day.

Household Power Consumption Analysis using Machine Learning

IEEE Conference Proceedings , 2024

To understand the complex relationships between the power consumption of various household appliances and the overall power usage, this essay delves into the field of machine learning. The principal aim is to develop prognostic models that can estimate the overall active power consumption by analyzing the energy consumption of designated household spaces, such as the kitchen, laundry room, air conditioning unit, and electric water heater. Through meticulous analysis and implementation of machine learning techniques, insight into the fundamental connections that underpin patterns of energy consumption is shed. The implications of the findings extend beyond mere predictive accuracy, offering invaluable insights for optimizing energy usage and informing future power management strategies. This study emphasizes how important it is to apply machine learning to interpret trends in household power consumption to make informed decisions and promote sustainable power use. Momentarily, household power consumption monitoring utilizing the XG Boost algorithm will make use of sophisticated data analytics to maximize energy use, improve efficiency, and give customers individualized advice on sustainable living habits.

A Study on Pattern Discovery of Smart Meter Data for Energy Efficiency

2018

Infinite massive amount of data are being generated from smart meters. Precious information can be obtained by analyzing these data for efficient use of energy. Data mining algorithms are extensively used for extracting these valuable information. Researchers have been focusing on developing energy management solutions for a cleaner environment. Recognizing residents behavior and provisioning a feedback continuously about their usage is one of the effective ways to save energy in residential sector. It is assumed that the more they know and understand their consumption, the more they can track their behavior and save energy. This paper presents a study on the recent research covered for understanding behavior of household energy consumption using pattern mining algorithms as well as applications developed for reducing energy consumption and achieving a much better and efficient use of energy. The pattern discovery techniques applied during the recent 5 years are also presented.

INTELLIGENT SYSTEM FOR PREDICTING BEHAVIOR OF ELECTRICAL ENERGY CONSUMPTION

IJCSMC, 2019

This paper will explore the intelligent system that could predict the usage and saving of electricity and it plays a major role in the smart home era, since can provide benefits with regard to comfort, safety and energy savings to electricity consumers. Many authors have already explored residence monitoring and prediction systems, however, very few approached the residence detection for predicting the energy consumption and prediction by using smart meter data. In this work, it can be achieved by using solely electricity consumption data and integrating it into an intelligent system. Also, we address the problem of generalizing a classification model, i.e., we analyze the possibility of using a single classification model to monitor residence in multiple households. We found that a residence detection accuracy and predict the usage was possible by using a generic classification model. Regarding residence prediction, we showed that it is possible to predict residence in multiple households, by using solely electricity consumption data. In addition to a higher energy efficiency, residence monitoring also provides more safety to the consumers. If a high electricity consumption is verified in periods that are not supposed, residence monitoring systems can be used as an intruder's detector, by sending alarms in real time to the smartphones of the occupants. If we analyze residence at the room-level, residence monitoring systems can also be used for health monitoring applications.