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


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
Number of pages9
ISBN (Print)9783030855765
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
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389


ConferenceInternational Conference on Intelligent and Fuzzy Systems, INFUS 2021

Bibliographical note

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


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


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