Applying an Agent-based Distributed AI Framework to Forecast Power for the Mini-Grid Stability

Iacovos I. Ioannou, Saher Javaid, Vasos Vassiliou, Andreas Pitsillides, Yasuo Tan

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

Abstract

Renewable energy sources, expected to form about 70% of power systems by 2050, bring challenges like fluctuating outputs and grid instability. Advanced power monitoring systems, crucial in environments like research facilities and hospitals, must navigate these dynamic scenarios. Traditional power management, especially Uninterruptible Power Supply (UPS) systems, often needs to catch up due to high costs and limited response to varied power demands, focusing mainly on constant power supply without differentiating between constant and fluctuating loads. In response, Artificial intelligence (AI) techniques are becoming indispensable for real-time power prediction and control. A distributed AI framework forecasts power needs, considering renewable sources, loads, and storage. This is key to ensuring smooth mini-grid operations, balancing operational demands with environmental considerations, and advancing intelligent energy management. Such systems are essential in optimizing energy usage, aligning it with available power to enhance efficiency and reduce waste. This is particularly important for mini-grids, with or without UPS systems, where predictive monitoring can substantially cut operational costs and extend lifespan.The paper focuses on providing consistent, constant, and fluctuating power by predicting mini-grid power needs hourly from the previous day's data. We use a Temporal Convolutional Network (TCN) for time series prediction, integrated within the BDIx agent's belief system through TensorFlow Lite. This approach accurately predicts upcoming power needs, ensures smooth operation, and prevents power outages. The TCN model's predictive capabilities highlight a significant stride in combining AI with energy management to address the complexities of modern power systems.

Original languageEnglish
Title of host publication11th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages255-256
Number of pages2
ISBN (Electronic)9798350386844
DOIs
Publication statusPublished - 2024
Externally publishedYes
Event11th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2024 - Taichung, Taiwan, Province of China
Duration: 9 Jul 202411 Jul 2024

Publication series

Name11th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2024

Conference

Conference11th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2024
Country/TerritoryTaiwan, Province of China
CityTaichung
Period9/07/2411/07/24

Keywords

  • Distributed AI
  • constant power load prediction
  • distributed power loads
  • distributed power sources
  • fluctuating power prediction
  • power control
  • power fluctuations

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Electrical and Electronic Engineering
  • Media Technology
  • Modeling and Simulation
  • Instrumentation

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