Industrial artificial intelligence based energy management system: Integrated framework for electricity load forecasting and fault prediction

Yusha Hu, Jigeng Li, Mengna Hong, Jingzheng Ren, Yi Man (Corresponding Author)

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)

Abstract

Forecasting accuracy electricity load can help industrial enterprises optimise production scheduling based on peak and off-peak electricity prices. The electricity load forecasting results can be provided to an electricity system to improve electricity generation efficiency and minimize energy consumption by developing electricity generation plans in advance and by avoiding over or under the generation of electricity. However, because of the different informatization levels in different industries, few reliable intelligent electricity management systems are applied on the power supply side. Based on industrial big data and machine learning algorithms, this study proposes an integrated model to forecast short-term electricity load. The hybrid model based on the hybrid mode decomposition algorithms is proposed to decompose the total electricity load signal. To improve the generalisation ability of the forecasting model, a dynamic forecasting model is proposed based on the improved hybrid intelligent algorithm to forecast the short-term electricity load. The results show that the accuracy of the proposed dynamic integrated electricity load forecasting model is as high as 99%. The integrated framework could forecast abnormal electricity consumption in time and provide reliable evidence for production process scheduling.
Original languageEnglish
Article number123195
Pages (from-to)123195
Number of pages16
JournalEnergy
Volume244
Issue numberPart B
DOIs
Publication statusPublished - 1 Apr 2022

Keywords

  • Electricity load
  • Dynamic forecasting model
  • Energy system analysis
  • Energy system optimisation
  • Artificial intelligence

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