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Forecasting based on an ensemble Autoregressive Moving Average - Adaptive neuro - Fuzzy inference system – Neural network - Genetic Algorithm Framework


  • Prado, Francisco
  • Minutolo, Marcel C.
  • Kristjanpoller, Werner


This paper proposes a novel ensemble methodology comprising an auto regressive integrated moving average, artificial neural network, fuzzy inference system model, adaptive neuro fuzzy inference system, support vector regression, extreme machine learning, and genetic algorithm to forecast aggregated, long-term energy demand. After comparing the framework with several benchmark methods by the loss functions mean squared error and mean absolute percentage error, and applying a model confidence set this work suggests that the proposed method improves forecasting accuracy over previous approaches. The proposed approach resulted in a mean squared error decrease of 22.3% and mean absolute percentage error by 33.1% with respect to the best artificial intelligence and econometric models in a sample study. Post-processing optimization of the forecasting ensemble in this methodology improves prediction accuracy. The approach developed herein provides an addition to the field for how hybridized models and augmented forecasting accuracy can be improved. Continued improvements to forecasting techniques are extremely important especially in areas where there are upper bound constraints on supply and lower bound on minimum operation levels.

Suggested Citation

  • Prado, Francisco & Minutolo, Marcel C. & Kristjanpoller, Werner, 2020. "Forecasting based on an ensemble Autoregressive Moving Average - Adaptive neuro - Fuzzy inference system – Neural network - Genetic Algorithm Framework," Energy, Elsevier, vol. 197(C).
  • Handle: RePEc:eee:energy:v:197:y:2020:i:c:s0360544220302668
    DOI: 10.1016/

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    References listed on IDEAS

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    2. Imani, Maryam, 2021. "Electrical load-temperature CNN for residential load forecasting," Energy, Elsevier, vol. 227(C).
    3. Feras Alasali & Khaled Nusair & Lina Alhmoud & Eyad Zarour, 2021. "Impact of the COVID-19 Pandemic on Electricity Demand and Load Forecasting," Sustainability, MDPI, Open Access Journal, vol. 13(3), pages 1-22, January.
    4. Qu, Zhijian & Xu, Juan & Wang, Zixiao & Chi, Rui & Liu, Hanxin, 2021. "Prediction of electricity generation from a combined cycle power plant based on a stacking ensemble and its hyperparameter optimization with a grid-search method," Energy, Elsevier, vol. 227(C).
    5. Ahmad, Tanveer & Zhang, Hongcai, 2020. "Novel deep supervised ML models with feature selection approach for large-scale utilities and buildings short and medium-term load requirement forecasts," Energy, Elsevier, vol. 209(C).
    6. Ahmad, Tanveer & Zhang, Dongdong & Huang, Chao, 2021. "Methodological framework for short-and medium-term energy, solar and wind power forecasting with stochastic-based machine learning approach to monetary and energy policy applications," Energy, Elsevier, vol. 231(C).

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