A Prediction-based Smart Meter Data Generator

Nadeem Iftikhar, Xiufeng Liu, Finn Ebertsen Nordbjerg, Sergiu Danalachi

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


    With the prevalence of cloud computing and In-ternet of Things (IoT), smart meters have become one of the main components of smart city strategy. Smart meters generate large amounts of fine-grained data that is used to provide useful information to consumers and utility companies for decision-making. Now-a-days, smart meter analytics systems consist of
    analytical algorithms that process massive amounts of data. These analytics algorithms require ample amounts of realistic data for testing and verification purposes. However, it is usually difficult to obtain adequate amounts of realistic data, mainly due to privacy issues. This paper proposes a smart meter data generator that can generate realistic energy consumption data by making use of a small real-world dataset as seed. The generator generates data using a prediction-based method that depends on historical energy consumption patterns along with Gaussian white noise. In this paper, we comprehensively evaluate the efficiency and effectiveness of the proposed method based on a real-world energy data set.
    Original languageEnglish
    Title of host publicationProceedings of the 19th International Conference on Network-Based Information Systems (NBiS), 2016
    Number of pages8
    Publication date2016
    ISBN (Electronic)978-1-5090-0979-4
    Publication statusPublished - 2016
    Event19th IEEE International Conference on Network-Based Information Systems - Ostrava, Czech Republic
    Duration: 7 Sep 20169 Oct 2016


    Conference19th IEEE International Conference on Network-Based Information Systems
    CountryCzech Republic

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