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On the Benefits of Using Metaheuristics in the Hyperparameter Tuning of Deep Learning Models for Energy Load Forecasting

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  • Nebojsa Bacanin

    (Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11010 Belgrade, Serbia)

  • Catalin Stoean

    (Department of Computer Science, University of Craiova, A.I.Cuza, 13, 200585 Craiova, Romania)

  • Miodrag Zivkovic

    (Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11010 Belgrade, Serbia)

  • Miomir Rakic

    (Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11010 Belgrade, Serbia)

  • Roma Strulak-Wójcikiewicz

    (Faculty of Economics and Transport Engineering, Maritime University of Szczecin, Wały Chrobrego 1/2, 70-500 Szczecin, Poland)

  • Ruxandra Stoean

    (Department of Computer Science, University of Craiova, A.I.Cuza, 13, 200585 Craiova, Romania)

Abstract

An effective energy oversight represents a major concern throughout the world, and the problem has become even more stringent recently. The prediction of energy load and consumption depends on various factors such as temperature, plugged load, etc. The machine learning and deep learning (DL) approaches developed in the last decade provide a very high level of accuracy for various types of applications, including time-series forecasting. Accordingly, the number of prediction models for this task is continuously growing. The current study does not only overview the most recent and relevant DL for energy supply and demand, but it also emphasizes the fact that not many recent methods use parameter tuning for enhancing the results. To fill the abovementioned gap, in the research conducted for the purpose of this manuscript, a canonical and straightforward long short-term memory (LSTM) DL model for electricity load is developed and tuned for multivariate time-series forecasting. One open dataset from Europe is used as a benchmark, and the performance of LSTM models for a one-step-ahead prediction is evaluated. Reported results can be used as a benchmark for hybrid LSTM-optimization approaches for multivariate energy time-series forecasting in power systems. The current work highlights that parameter tuning leads to better results when using metaheuristics for this purpose in all cases: while grid search achieves a coefficient of determination ( R 2 ) of 0.9136, the metaheuristic that led to the worst result is still notably better with the corresponding score of 0.9515.

Suggested Citation

  • Nebojsa Bacanin & Catalin Stoean & Miodrag Zivkovic & Miomir Rakic & Roma Strulak-Wójcikiewicz & Ruxandra Stoean, 2023. "On the Benefits of Using Metaheuristics in the Hyperparameter Tuning of Deep Learning Models for Energy Load Forecasting," Energies, MDPI, vol. 16(3), pages 1-21, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1434-:d:1053690
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    References listed on IDEAS

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    1. Ismail Shah & Hasnain Iftikhar & Sajid Ali, 2020. "Modeling and Forecasting Medium-Term Electricity Consumption Using Component Estimation Technique," Forecasting, MDPI, vol. 2(2), pages 1-17, May.
    2. Liang, Yi & Niu, Dongxiao & Hong, Wei-Chiang, 2019. "Short term load forecasting based on feature extraction and improved general regression neural network model," Energy, Elsevier, vol. 166(C), pages 653-663.
    3. Salam, Abdulwahed & El Hibaoui, Abdelaaziz, 2021. "Energy consumption prediction model with deep inception residual network inspiration and LSTM," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 190(C), pages 97-109.
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