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Forecasting of Electrical Energy Consumption of Households in a Smart Grid

Author

Listed:
  • Felix Ghislain Yem Souhe

    (Department of Electrical Engineering, University of Douala-ENSET, 1872-Douala, Douala, Cameroon,)

  • Camille Franklin Mbey

    (Department of Electrical Engineering, University of Douala-ENSET, 1872-Douala, Douala, Cameroon,)

  • Alexandre Teplaira Boum

    (Department of Electrical Engineering, University of Douala-ENSET, 1872-Douala, Douala, Cameroon,)

  • Pierre Ele

    (Department of Electrical Engineering, University of Yaounde 1, Polytechnic, Yaounde, Cameroon.)

Abstract

This paper aims to develop a hybrid model for forecasting electrical energy consumption of households based on a Particle Swarm Optimization (PSO) algorithm associated with the Grey and Adaptive Neuro-Fuzzy Inference System (ANFIS). This paper proposes a new Grey-ANFIS-PSO model that is based on historical data from smart meters in order to estimate and improve the accuracy of forecasting electrical energy consumption. This accuracy will be characterized by coefficients such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The PSO will allow to optimally design the Neuro-fuzzy forecasting. This method is implemented on Cameroon consumption data over the 24-years period in order to forecast energy consumption for the next years. Using this model, we were able to estimate that electricity consumption will be 1867 GWH in 2028 with 0.20158 RMSE and 0.62917% MAPE. The simulation results obtained show that implementation of this new optimized Neuro-fuzzy model on consumption data for a long period presents better results on prediction of electrical energy consumption compared to single artificial intelligence models of literature such as Support Vector Machine (SVM) and Artificial Neural Network (ANN).

Suggested Citation

  • Felix Ghislain Yem Souhe & Camille Franklin Mbey & Alexandre Teplaira Boum & Pierre Ele, 2021. "Forecasting of Electrical Energy Consumption of Households in a Smart Grid," International Journal of Energy Economics and Policy, Econjournals, vol. 11(6), pages 221-233.
    • Handle: RePEc:eco:journ2:2021-06-26
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    References listed on IDEAS

    as
    1. Kaboli, S. Hr. Aghay & Fallahpour, A. & Selvaraj, J. & Rahim, N.A., 2017. "Long-term electrical energy consumption formulating and forecasting via optimized gene expression programming," Energy, Elsevier, vol. 126(C), pages 144-164.
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    3. Bahrami, Saadat & Hooshmand, Rahmat-Allah & Parastegari, Moein, 2014. "Short term electric load forecasting by wavelet transform and grey model improved by PSO (particle swarm optimization) algorithm," Energy, Elsevier, vol. 72(C), pages 434-442.
    4. Maher AbuBaker, 2021. "Household Electricity Load Forecasting Toward Demand Response Program Using Data Mining Techniques in A Traditional Power Grid," International Journal of Energy Economics and Policy, Econjournals, vol. 11(4), pages 132-148.
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    Cited by:

    1. Priyadharshini Ramu & Sivasankar Gangatharan & Sankar Rangasamy & Lucian Mihet-Popa, 2023. "Categorization of Loads in Educational Institutions to Effectively Manage Peak Demand and Minimize Energy Cost Using an Intelligent Load Management Technique," Sustainability, MDPI, vol. 15(16), pages 1-28, August.
    2. Wulfran Fendzi Mbasso & Reagan Jean Jacques Molu & Serge Raoul Dzonde Naoussi & Saatong Kenfack, 2022. "Demand-Supply Forecasting based on Deep Learning for Electricity Balance in Cameroon," International Journal of Energy Economics and Policy, Econjournals, vol. 12(4), pages 99-103, July.

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    More about this item

    Keywords

    Forecast model; PSO; ANFIS model; Grey model; electricity consumption;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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