An Ensemble Learning Approach for Energy Demand Forecasting in Microgrids Using Fog Computing

Tuğçe Keskin, Gökhan İnce*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Increased usage of smart meters enables information exchange between customers and utility providers in smart grid systems. Nowadays, the cloud-centric architecture has become a bottleneck for the decentralized and data-driven microgrids evolving from centralized Smart grids. Hence, fog computing is an appropriate paradigm to build distributed, latency-aware, and privacy-preserving energy demand applications in microgrid systems. In this work, we proposed a 3-tier architecture of a microgrid energy demand management system comprising edge, fog, and cloud layers. We set up a simulation environment where Raspberry Pi devices act as fog nodes and resource-efficient Docker applications run on these nodes. As the main contribution of the work, we developed a short-term load forecasting application based on an ensemble model that integrates support vector regression (SVR) and long-short term memory (LSTM) by leveraging the potential of distributed and low-latency fog nodes for complex models. We evaluated the forecasting model deployed in a fog-based simulation environment using the public REFIT Electrical Load dataset. We also tested the deployed fog-based simulation environment based on latency and execution time metrics.

Original languageEnglish
Title of host publicationIntelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference
EditorsCengiz Kahraman, Selcuk Cebi, Sezi Cevik Onar, Basar Oztaysi, A. Cagri Tolga, Irem Ucal Sari
PublisherSpringer Science and Business Media Deutschland GmbH
Pages170-178
Number of pages9
ISBN (Print)9783030855765
DOIs
Publication statusPublished - 2022
EventInternational Conference on Intelligent and Fuzzy Systems, INFUS 2021 - Istanbul, Turkey
Duration: 24 Aug 202126 Aug 2021

Publication series

NameLecture Notes in Networks and Systems
Volume308
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

ConferenceInternational Conference on Intelligent and Fuzzy Systems, INFUS 2021
Country/TerritoryTurkey
CityIstanbul
Period24/08/2126/08/21

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

  • Energy demand forecasting
  • Ensemble learning
  • Fog computing
  • Internet of Things
  • Microgrids
  • Smart grids

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