Deep Learning Models for Multi-Energy Prediction of Combined Electrical, Heat and Gas network systems

Corneliu Arsene, Alessandra Parisio

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

Abstract

Effective methods for predicting power consumptions of electrical, heat or gas systems are important for knowing in advance the state of the systems. Deep Learning (DL) models have been used extensively in predicting the electrical power consumptions and more recently for predicting heat or gas power consumptions. This paper presents Convolutional Neural Networks (CNNs) applied to individual prediction of multi-energy power consumptions of combined electrical, heat and gas systems. The CNNs are chosen because of their previous success and the designed models include three convolutional layers, with subsequent pooling and a fully connected layer for regression. The CNNs are trained and tested on real datasets of combined electrical, heat and gas systems. The predictions results are mainly evaluated in terms of the Signal to Noise Ratio (SNR) and the Normalized Root Mean Square Error (NRMSE). Single and combined input variables are being investigated and high correlation is identified between the present and the previous 24 hours power consumptions for any of the three studied systems electrical, heat or gas. The results demonstrate that the multi-energy loads can be predicted well for the testing datasets that is the electrical (SNR=34.63dB, NRMSE=0.0085), the heat (SNR=17.42dB, NRMSE=0.061) and the gas (SNR=8.86 dB, NRMSE=0.14) systems.

Original languageEnglish
Title of host publicationIJCNN 2023 - International Joint Conference on Neural Networks, Proceedings
PublisherIEEE
ISBN (Electronic)9781665488679
ISBN (Print)9781665488679
DOIs
Publication statusE-pub ahead of print - 2 Aug 2023
Event2023 International Joint Conference on Neural Networks, IJCNN 2023 - Gold Coast, Australia
Duration: 18 Jun 202323 Jun 2023

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2023-June

Conference

Conference2023 International Joint Conference on Neural Networks, IJCNN 2023
Country/TerritoryAustralia
CityGold Coast
Period18/06/2323/06/23

Keywords

  • Convolutional Neural Networks
  • Deep Learning models
  • Electrical Network
  • Gas Network
  • Heat Network
  • Multi-Energy vector

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