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Short-Term Electric Power Demand Forecasting Using NSGA II-ANFIS Model

Author

Listed:
  • Aydin Jadidi

    (Department of Electrical Engineering, Polytechnic School, Federal University of Bahia, Salvador 40210-630, Brazil)

  • Raimundo Menezes

    (Department of Electrical Engineering, Polytechnic School, Federal University of Bahia, Salvador 40210-630, Brazil)

  • Nilmar de Souza

    (Department of Electrical Engineering, Polytechnic School, Federal University of Bahia, Salvador 40210-630, Brazil)

  • Antonio Cezar de Castro Lima

    (Department of Electrical Engineering, Polytechnic School, Federal University of Bahia, Salvador 40210-630, Brazil)

Abstract

Load forecasting is of crucial importance for smart grids and the electricity market in terms of the meeting the demand for and distribution of electrical energy. This research proposes a hybrid algorithm for improving the forecasting accuracy where a non-dominated sorting genetic algorithm II (NSGA II) is employed for selecting the input vector, where its fitness function is a multi-layer perceptron neural network (MLPNN). Thus, the output of the NSGA II is the output of the best-trained MLPNN which has the best combination of inputs. The result of NSGA II is fed to the Adaptive Neuro-Fuzzy Inference System (ANFIS) as its input and the results demonstrate an improved forecasting accuracy of the MLPNN-ANFIS compared to the MLPNN and ANFIS models. In addition, genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), differential evolution (DE), and imperialistic competitive algorithm (ICA) are used for optimized design of the ANFIS. Electricity demand data for Bonneville, Oregon are used to test the model and among the different tested models, NSGA II-ANFIS-GA provides better accuracy. Obtained values of error indicators for one-hour-ahead demand forecasting are 107.2644, 1.5063, 65.4250, 1.0570, and 0.9940 for RMSE, RMSE%, MAE, MAPE, and R, respectively.

Suggested Citation

  • Aydin Jadidi & Raimundo Menezes & Nilmar de Souza & Antonio Cezar de Castro Lima, 2019. "Short-Term Electric Power Demand Forecasting Using NSGA II-ANFIS Model," Energies, MDPI, vol. 12(10), pages 1-14, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:10:p:1891-:d:232249
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    References listed on IDEAS

    as
    1. Aydin Jadidi & Raimundo Menezes & Nilmar De Souza & Antonio Cezar De Castro Lima, 2018. "A Hybrid GA–MLPNN Model for One-Hour-Ahead Forecasting of the Global Horizontal Irradiance in Elizabeth City, North Carolina," Energies, MDPI, vol. 11(10), pages 1-18, October.
    2. Jihoon Moon & Yongsung Kim & Minjae Son & Eenjun Hwang, 2018. "Hybrid Short-Term Load Forecasting Scheme Using Random Forest and Multilayer Perceptron," Energies, MDPI, vol. 11(12), pages 1-20, November.
    3. Srete Nikolovski & Hamid Reza Baghaee & Dragan Mlakić, 2018. "ANFIS-Based Peak Power Shaving/Curtailment in Microgrids Including PV Units and BESSs," Energies, MDPI, vol. 11(11), pages 1-23, October.
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    7. Ashfaq Ahmad & Nadeem Javaid & Abdul Mateen & Muhammad Awais & Zahoor Ali Khan, 2019. "Short-Term Load Forecasting in Smart Grids: An Intelligent Modular Approach," Energies, MDPI, vol. 12(1), pages 1-21, January.
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