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A Novel Machine Learning-Based Price Forecasting for Energy Management Systems

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)

  • Fawaz Alassery

    (Department of Computer Engineering, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia)

  • Habib Hamam

    (Faculty of Engineering, Uni de Moncton, Moncton, NB E1A 3E9, Canada
    Canadian Institute of Technology, 1001 Tirana, Albania
    Department of Electrical and Electronic Engineering Science, School of Electrical Engineering, University of Johannesburg, Johannesburg 2006, South Africa)

  • Omar Cheikhrouhou

    (CES Laboratory, National School of Engineers of Sfax, University of Sfax, Sfax 3038, Tunisia)

Abstract

Price forecasting (PF) is the primary concern in distributed power generation. This paper presents a novel and improved technique to forecast electricity prices. The data of various power producers, Capacity Purchase Price (CPP), Power Purchase Price (PPP), Tariff rates, and load demand from National Electric Power Regulatory Authority (NEPRA) are considered for MAPE reduction in PF. Eight time-series and auto-regression algorithms are developed for data fetching and setting the objective function. The feed-forward ANFIS based on the ML approach and space vector regression (SVR) is introduced to PF by taking the input from time series and auto-regression (AR) algorithms. Best-feature selection is conducted by adopting the Binary Genetic Algorithm (BGA)-Principal Component Analysis (PCA) approach that ultimately minimizes the complexity and computational time of the model. The proposed integration strategy computes the mean absolute percentage error (MAPE), and the overall improvement percentage is 9.24%, which is valuable in price forecasting of the energy management system (EMS). In the end, EMS based on the Firefly algorithm (FA) has been presented, and by implementing FA, the cost of electricity has been reduced by 21%, 19%, and 20% for building 1, 2, and 3, respectively.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:22:p:12693-:d:680686
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    References listed on IDEAS

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