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access icon free Multi-nodal short-term energy forecasting using smart meter data

  • Author(s): Barry P. Hayes 1 ; Jorn K. Gruber 2 ; Milan Prodanovic 3
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  • Source: Volume 12, Issue 12, 10 July 2018, p. 2988 – 2994
    DOI:  10.1049/iet-gtd.2017.1599 , Print ISSN 1751-8687, Online ISSN 1751-8695
© The Institution of Engineering and Technology
Received 12/10/2017, Accepted 29/03/2018, Revised 09/03/2018, Published 04/04/2018

This paper deals with the short-term forecasting of electrical energy demands at the local level, incorporating advanced metering infrastructure (AMI), or ‘smart meter’ data. It provides a study of the effects of aggregation on electrical energy demand modelling and multi-nodal demand forecasting. This paper then presents a detailed assessment of the variables which affect electrical energy demand, and how these effects vary at different levels of demand aggregation. Finally, this study outlines an approach for incorporating AMI data in short-term forecasting at the local level, in order to improve forecasting accuracy for applications in distributed energy systems, microgrids and transactive energy. The analysis presented in this study is carried out using large AMI data sets comprised of recorded demand and local weather data from test sites in two European countries.

Inspec keywords: distributed power generation; load forecasting; smart meters

Other keywords: electrical energy demand modelling; transactive energy; AMI; multinodal demand forecasting; distributed energy system; smart meter data; advanced metering infrastructure; European country; microgrid; multinodal short-term energy forecasting

Subjects: Distributed power generation; Power system measurement and metering; Power system planning and layout

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