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An Improved Residential Electricity Load Forecasting Using a Machine-Learning-Based Feature Selection Approach and a Proposed Integration Strategy

Author

Listed:
  • Adnan Yousaf

    (Department of Electrical Engineering, Superior University, Lahore 54000, Pakistan)

  • Rao Muhammad Asif

    (Department of Electrical Engineering, Superior University, Lahore 54000, Pakistan)

  • Mustafa Shakir

    (Department of Electrical Engineering, Superior University, Lahore 54000, Pakistan)

  • Ateeq Ur Rehman

    (Department of Electrical Engineering, Government College University, Lahore 54000, Pakistan)

  • Mohmmed S. Adrees

    (College of Computer Science and Information Technology, Al Baha University, Al Baha 1988, Saudi Arabia)

Abstract

Load forecasting (LF) has become the main concern in decentralized power generation systems with the smart grid revolution in the 21st century. As an intriguing research topic, it facilitates generation systems by providing essential information for load scheduling, demand-side integration, and energy market pricing and reducing cost. An intelligent LF model of residential loads using a novel machine learning (ML)-based approach, achieved by assembling an integration strategy model in a smart grid context, is proposed. The proposed model improves the LF by optimizing the mean absolute percentage error (MAPE). The time-series-based autoregression schemes were carried out to collect historical data and set the objective functions of the proposed model. An algorithm consisting of seven different autoregression models was also developed and validated through a feedforward adaptive-network-based fuzzy inference system (ANFIS) model, based on the ML approach. Moreover, a binary genetic algorithm (BGA) was deployed for the best feature selection, and the best fitness score of the features was obtained with principal component analysis (PCA). A unique decision integration strategy is presented that led to a remarkably improved transformation in reducing MAPE. The model was tested using a one-year Pakistan Residential Electricity Consumption (PRECON) dataset, and the attained results verify that the proposed model obtained the best feature selection and achieved very promising values of MAPE of 1.70%, 1.77%, 1.80%, and 1.67% for summer, fall, winter, and spring seasons, respectively. The overall improvement percentage is 17%, which represents a substantial increase for small-scale decentralized generation units.

Suggested Citation

  • Adnan Yousaf & Rao Muhammad Asif & Mustafa Shakir & Ateeq Ur Rehman & Mohmmed S. Adrees, 2021. "An Improved Residential Electricity Load Forecasting Using a Machine-Learning-Based Feature Selection Approach and a Proposed Integration Strategy," Sustainability, MDPI, vol. 13(11), pages 1-20, May.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:11:p:6199-:d:566414
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    2. Roman V. Klyuev & Irbek D. Morgoev & Angelika D. Morgoeva & Oksana A. Gavrina & Nikita V. Martyushev & Egor A. Efremenkov & Qi Mengxu, 2022. "Methods of Forecasting Electric Energy Consumption: A Literature Review," Energies, MDPI, vol. 15(23), pages 1-33, November.
    3. Hao Wang & Chen Peng & Bolin Liao & Xinwei Cao & Shuai Li, 2023. "Wind Power Forecasting Based on WaveNet and Multitask Learning," Sustainability, MDPI, vol. 15(14), pages 1-22, July.
    4. Mehmet Türker Takcı & Tuba Gözel, 2022. "Effects of Predictors on Power Consumption Estimation for IT Rack in a Data Center: An Experimental Analysis," Sustainability, MDPI, vol. 14(21), pages 1-19, November.
    5. Adnan Yousaf & Rao Muhammad Asif & Mustafa Shakir & Ateeq Ur Rehman & Fawaz Alassery & Habib Hamam & Omar Cheikhrouhou, 2021. "A Novel Machine Learning-Based Price Forecasting for Energy Management Systems," Sustainability, MDPI, vol. 13(22), pages 1-26, November.
    6. Hina Maqbool & Adnan Yousaf & Rao Muhammad Asif & Ateeq Ur Rehman & Elsayed Tag Eldin & Muhammad Shafiq & Habib Hamam, 2022. "An Optimized Fuzzy Based Control Solution for Frequency Oscillation Reduction in Electric Grids," Energies, MDPI, vol. 15(19), pages 1-21, September.

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