Abstract
This paper presents a supervised classification model, where the indicators of correlation between dependent and independent variables within each class are utilized for a transformation of the large-scale input data to a lower dimension without loss of recognition relevant information. In the case study, we use the consumption data recorded by smart electricity meters of 4200 Irish dwellings along with half-hourly outdoor temperature to derive 12 household properties (such as type of heating, floor area, age of house, number of inhabitants, etc.). Survey data containing characteristics of 3500 households enables algorithm training. The results show that the presented model outperforms ordinary classifiers with regard to the accuracy and temporal characteristics. The model allows incorporating any kind of data affecting energy consumption time series, or in a more general case, the data affecting class-dependent variable, while minimizing the risk of the curse of dimensionality. The gained information on household characteristics renders targeted energy-efficiency measures of utility companies and public bodies possible.
Highlights
Reducing energy consumption is the best sustainable long-term answer to the challenges associated with increasing demand for energy, fluctuating oil prices, uncertain energy supplies, and fears of global warming (European commission 2008; Wenig et al 2015)
Since the household sector represents around 30 % of the final global energy consumption (International Energy Agency 2014), customized energy efficiency measures can contribute significantly to the reduction of air pollution, carbon emissions, and economic growth
Application of DID‐Class to household classification based on smart electricity meter data and weather conditions we present a classification of residential units based on smart electricity meter data and weather variables by DID-Class
Summary
Reducing energy consumption is the best sustainable long-term answer to the challenges associated with increasing demand for energy, fluctuating oil prices, uncertain energy supplies, and fears of global warming (European commission 2008; Wenig et al 2015). We propose a supervised machine learning method called DID-Class (Dependent-independent data classification) that tackles magnitudes of interaction (correlation) among multiple classification-relevant variables It establishes a classification machine (probabilistic regression) with embedded correlation indices and a single normalized dataset. Step 2: Integration of dependent and independent measurements In order to take the correlation coefficients revealed on the previous step into consideration, and transform the multivariate input data to a lower dimension without loss of classification-relevant information, we normalize the dependent variables with respect to the independent ones. DID-Class can perform better or worse than a classifier that analyzes all data in its initial form (without correlation information), depending on the number of categories a and dimension of independent variables. Show that the correlation between independent and dependent measurements exists
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
