RTU Research Information System
Latviešu English

Publikācija: Analysis and Prediction of Electricity Consumption Using Smart Meter Data

Publication Type Full-text conference paper published in conference proceedings indexed in SCOPUS or WOS database
Funding for basic activity Unknown
Defending: ,
Publication language English (en)
Title in original language Analysis and Prediction of Electricity Consumption Using Smart Meter Data
Field of research 2. Engineering and technology
Sub-field of research 2.2 Electrical engineering, Electronic engineering, Information and communication engineering
Authors Antans Sauļus Sauhats
Renāta Varfolomejeva
Oļegs Linkevičs
Romāns Petričenko
Māris Kuņickis
Māris Balodis
Keywords smart meters, electricity consumption profiles, statistical analysis, load aggregators
Abstract This paper is considering application of smart meter data to predict electricity consumption of household consumers. The availability and amount of data is suitable for in- depth statistical analysis of electricity consumption profiles and the study of consumer’s behavior. Prediction of electricity consumption is very important for electricity traders to balance their electricity purchase and sales portfolio, as well as to prepare optimal price products (offers) for their clients. Electricity consumption data of 500 consumers divided into 6 consumers groups was analyzed. The consumption data was derived from smart meters. As the next step, modern methods of electricity consumption forecasts would be applied to predict household electricity consumption.
DOI: 10.1109/PowerEng.2015.7266290
Reference Sauhats, A., Varfolomejeva, R., Linkevičs, O., Petričenko, R., Kuņickis, M., Balodis, M. Analysis and Prediction of Electricity Consumption Using Smart Meter Data. In: 2015 IEEE 5th International Conference on Power Engineering, Energy and Electrical Drives (POWERENG): Proceedings, Latvia, Rīga, 11-13 May, 2015. Riga: Riga Technical University, 2015, pp.17-22. ISBN 978-1-4673-7203-9. e-ISBN 978-1-4799-9978-1. e-ISSN 2155-5532. Available from: doi:10.1109/PowerEng.2015.7266290
Full-text Full-text
Publication version
License
Additional information Citation count:
ID 20773