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 Journal of Statistics and Management Systems cover
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0014·ISSN (Print): 0972-0510

Monthly Journal: Publishes peer-reviewed aticles on theoretical and applied statistics and management systems, expoloring industrial statistics, actuarial and decision sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis Online
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Open Access Research Article

Interdisciplinary role of statistics in energy consumption prediction within smart grid big data analytics

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pp. 95–105Vol. 28Issue 1January 2025DOI: 10.47974/JSMS-1316XML
Received:
08 Feb 2024
Published Online:
15 Jan 2025
Article type:
Research Article
Language:
EN
Article no.:
JSMS-1316
Pages:
95–105

Abstract

The study of energy consumption in buildings is crucial due to environmental and financial impacts. Smart metering is suggested to improve energy efficiency, but predicting electrical consumption accurately remains challenging, considering various factors. To tackle this, we propose a Multi-objective Golden Eagle Optimization (MGE) based Hybrid Deep Dynamic Convolutional Fuzzy Network (HDDCFN) method. This approach optimizes algorithm parameters and prediction models using multi-objective optimization, considering historical usage and weather conditions. The research is conducted using Matlab.

Keywords

Subject Classifications

68T07

References

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