Data Mining Techniques for Detecting Household Characteristics Based on Smart Meter Data
نویسندگان
چکیده
The main goal of this research is to discover the structure of home appliances usage patterns, hence providing more intelligence in smart metering systems by taking into account the usage of selected home appliances and the time of their usage. In particular, we present and apply a set of unsupervised machine learning techniques to reveal specific usage patterns observed at an individual household. The work delivers the solutions applicable in smart metering systems that might: (1) contribute to higher energy awareness; (2) support accurate usage forecasting; and (3) provide the input for demand response systems in homes with timely energy saving recommendations for users. The results provided in this paper show that determining household characteristics from smart meter data is feasible and allows for quickly grasping general trends in data.
منابع مشابه
An Optimization K-Modes Clustering Algorithm with Elephant Herding Optimization Algorithm for Crime Clustering
The detection and prevention of crime, in the past few decades, required several years of research and analysis. However, today, thanks to smart systems based on data mining techniques, it is possible to detect and prevent crime in a considerably less time. Classification and clustering-based smart techniques can classify and cluster the crime-related samples. The most important factor in the c...
متن کاملComputing Electricity Consumption Profiles from Household Smart Meter Data
In this paper, we investigate a critical problem in smart meter data mining: computing electricity consumption profiles. We present a simple, interpretable and practical profiling framework for residential consumers, which accounts for variations in electricity consumption at different times of day and at different external temperatures. Our approach is to isolate the effect of external tempera...
متن کاملDiagnosis of diabetes by using a data mining method based on native data
Background & Aim: Detecting the abnormal performance of diabetes and subsequently getting proper treatment can reduce the mortality associated with the disease. Also, timely diagnosis will result in irreversible complications for the patient. The aim of this study was to determine the status of diabetes mellitus using data mining techniques. Methods: This is an analytical study and its databas...
متن کاملA Geometric View of Similarity Measures in Data Mining
The main objective of data mining is to acquire information from a set of data for prospect applications using a measure. The concerning issue is that one often has to deal with large scale data. Several dimensionality reduction techniques like various feature extraction methods have been developed to resolve the issue. However, the geometric view of the applied measure, as an additional consid...
متن کاملTime Will Tell: Using Smart Meter Time Series Data to Derive Household Features and Explain Heterogeneity in Pricing Programs
The recent nationwide adoption of smart meters provides a new source of rich data about individual household electricity consumption. Data science techniques can extract a variety of high temporal resolution, household-specific features from the hourly electricity time series itself and in combination with other readily available relevant information, like weather or census data. This allows us...
متن کامل