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Towards Electric Price and Load Forecasting Using CNN-Based Ensembler in Smart Grid

Author

Listed:
  • Shahzad Aslam

    (Department of Statistics and Mathematics, Institute of Southern Punjab, Multan 66000, Pakistan)

  • Nasir Ayub

    (School of Electrical Engineering and Computer Science, National University of Science and Technology, Islamabad 44000, Pakistan)

  • Umer Farooq

    (Department of Computer Science, Islamia University Bahawalpur, Bahawalpur 63100, Pakistan)

  • Muhammad Junaid Alvi

    (Electrical Engineering Department, NFC Institute of Engineering and Fertilizer Research, Faisalabad 38000, Pakistan)

  • Fahad R. Albogamy

    (Computer Sciences Program, Turabah University College, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

  • Gul Rukh

    (Department of Electrical Engineering, University of Engineering and Technology, Mardan 23200, Pakistan)

  • Syed Irtaza Haider

    (College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia)

  • Ahmad Taher Azar

    (College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
    Faculty of Computers and Artificial Intelligence, Benha University, Benha 13518, Egypt)

  • Rasool Bukhsh

    (Department of Computer Science, COMSATS University Islamabad (CUI), Islamabad 44000, Pakistan)

Abstract

Medium-term electricity consumption and load forecasting in smart grids is an attractive topic of study, especially using innovative data analysis approaches for future energy consumption trends. Loss of electricity during generation and use is also a problem to be addressed. Both consumers and utilities can benefit from a predictive study of electricity demand and pricing. In this study, we used a new machine learning approach called AdaBoost to identify key features from an ISO-NE dataset that includes daily consumption data over eight years. Moreover, the DT classifier and RF are widely used to extract the best features from the dataset. Moreover, we predicted the electricity load and price using machine learning techniques including support vector machine (SVM) and deep learning techniques such as a convolutional neural network (CNN). Coronavirus herd immunity optimization (CHIO), a novel optimization approach, was used to modify the hyperparameters to increase efficiency, and it used classifiers to improve the performance of our classifier. By adding additional layers to the CNN and fine-tuning its parameters, the probability of overfitting the classifier was reduced. For method validation, we compared our proposed models with several benchmarks. MAE, MAPE, MSE, RMSE, the f1 score, recall, precision, and accuracy were the measures used for performance evaluation. Moreover, seven different forms of statistical analysis were given to show why our proposed approaches are preferable. The proposed CNN-CHIO and SVM techniques had the lowest MAPE error rates of 6% and 8%, respectively, and the highest accuracy rates of 95% and 92%, respectively.

Suggested Citation

  • Shahzad Aslam & Nasir Ayub & Umer Farooq & Muhammad Junaid Alvi & Fahad R. Albogamy & Gul Rukh & Syed Irtaza Haider & Ahmad Taher Azar & Rasool Bukhsh, 2021. "Towards Electric Price and Load Forecasting Using CNN-Based Ensembler in Smart Grid," Sustainability, MDPI, vol. 13(22), pages 1-28, November.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:22:p:12653-:d:680508
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    References listed on IDEAS

    as
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    3. Haokun Su & Xiangang Peng & Hanyu Liu & Huan Quan & Kaitong Wu & Zhiwen Chen, 2022. "Multi-Step-Ahead Electricity Price Forecasting Based on Temporal Graph Convolutional Network," Mathematics, MDPI, vol. 10(14), pages 1-16, July.
    4. Radosław Drozd & Radosław Wolniak & Jan Piwnik, 2023. "Systemic analysis of a manufacturing process based on a small scale bakery," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(2), pages 1421-1437, April.
    5. Nazila Pourhaji & Mohammad Asadpour & Ali Ahmadian & Ali Elkamel, 2022. "The Investigation of Monthly/Seasonal Data Clustering Impact on Short-Term Electricity Price Forecasting Accuracy: Ontario Province Case Study," Sustainability, MDPI, vol. 14(5), pages 1-14, March.
    6. Nasir Ayub & Usman Ali & Kainat Mustafa & Syed Muhammad Mohsin & Sheraz Aslam, 2022. "Predictive Data Analytics for Electricity Fraud Detection Using Tuned CNN Ensembler in Smart Grid," Forecasting, MDPI, vol. 4(4), pages 1-13, November.

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