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Electricity Demand Forecasting with Use of Artificial Intelligence: The Case of Gokceada Island

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  • Mustafa Saglam

    (Energy Institute, Bartlett School Environment, Energy and Resources, University College London, London WC1E 6BT, UK)

  • Catalina Spataru

    (Energy Institute, Bartlett School Environment, Energy and Resources, University College London, London WC1E 6BT, UK)

  • Omer Ali Karaman

    (Department of Electronic and Automation, Vocational School, Batman University, Batman 72100, Turkey)

Abstract

This study reviews a selection of approaches that have used Artificial Neural Networks (ANN), Particle Swarm Optimization (PSO), and Multi Linear Regression (MLR) to forecast electricity demand for Gokceada Island. Artificial Neural Networks, Particle Swarm Optimization, and Linear Regression methods are frequently used in the literature. Imports, exports, car numbers, and tourist-passenger numbers are used as based on input values from 2014 to 2020 for Gokceada Island, and the electricity energy demands up to 2040 are estimated as an output value. The results obtained were analyzed using statistical error metrics such as R 2 , MSE, RMSE, and MAE. The confidence interval analysis of the methods was performed. The correlation matrix is used to show the relationship between the actual value and method outputs and the relationship between independent and dependent variables. It was observed that ANN yields the highest confidence interval of 95% among the method utilized, and the statistical error metrics have the highest correlation for ANN methods between electricity demand output and actual data.

Suggested Citation

  • Mustafa Saglam & Catalina Spataru & Omer Ali Karaman, 2022. "Electricity Demand Forecasting with Use of Artificial Intelligence: The Case of Gokceada Island," Energies, MDPI, vol. 15(16), pages 1-22, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:16:p:5950-:d:890153
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    References listed on IDEAS

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    1. Mustafa Saglam & Catalina Spataru & Omer Ali Karaman, 2023. "Forecasting Electricity Demand in Turkey Using Optimization and Machine Learning Algorithms," Energies, MDPI, vol. 16(11), pages 1-23, June.

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