Artificial intelligence in energy industry: forecasting electricity consumption through cohort intelligence & adaptive neural fuzzy inference system

Salih Tutun, Ali Tosyali, Hossein Sangrody, Mohammad Khasawneh, Marina Johnson, Abdullah Albizri, Antoine Harfouche

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Demand forecasting is critical for energy systems, as energy is difficult to store and should only be supplied as needed. Researchers attempted to improve forecasts of energy consumption. However, they assume independent factors increase at a constant growth rate, which is unrealistic. Existing methods are designed to determine annual consumption, whereas energy-planning organizations rely on short- or medium-term consumption values. Therefore, we propose a new forecasting framework that introduces new models and scenarios. We apply a cohort intelligence-based adaptive neuro-fuzzy inference system (CI-ANFIS) with a subtractive clustering and grid partition approach to forecast net electricity consumption. One challenge in accurately predicting electricity consumption for specific projection intervals is missing values for factors independent of those known for existing net consumption. Then, we utilize a regression equation scenario approach. We test our framework using a real-world energy consumption dataset and show that our proposed framework outperforms the existing methods.

Original languageEnglish
JournalJournal of Business Analytics
DOIs
StateAccepted/In press - 2022

Keywords

  • ANFIS
  • artificial intelligence
  • Cohort Intelligence
  • electricity demand forecasting
  • Energy demand forecasting
  • machine learning
  • metaheuristics
  • parameter optimization

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