Using Supervised Machine Learning to Explore Energy Consumption Data in Private Sector Housing
|Professorship/Faculty:||Information Systems and Energy Efficient Systems||Authors:||Sodenkamp, Mariya; Hopf, Konstantin ; Staake, Thorsten||Title of the compilation:||Handbook of Research on Organizational Transformations through Big Data Analytics||Editors:||Tavana, Madjid; Puranam, Kartikeya|
|Publisher Information:||Hershey, Pennsylvania (USA) : IGI Global||Year of publication:||2015||Pages / Size:||320 - 333||ISBN:||9781466672727||Language(s):||English||DOI:||10.4018/978-1-4666-7272-7||Document Type:||Contribution to an Articlecollection||Abstract:||
Smart electricity meters allow capturing consumption load profiles of residential buildings. Besides several other applications, the retrieved data renders it possible to reveal household characteristics including the number of persons per apartment, age of the dwelling, etc., which helps to develop targeted energy conservation services. The goal of this chapter is to develop further related methods of smart meter data analytics that infer such household characteristics using weekly load curves. The contribution of this chapter to the state of the art is threefold. The authors first quadruplicate the number of defined features that describe electricity load curves to preserve relevant structures for classification. Then, they suggest feature filtering techniques to reduce the dimension of the input to a set of a few significant ones. Finally, the authors redefine class labels for some properties. As a result, the classification accuracy is elevated up to 82%, while the runtime complexity is significantly reduced.
|URI:||https://fis.uni-bamberg.de/handle/uniba/21416||Release Date:||14. February 2015|