Next Article in Journal
Energy Stored in Above-Ground Biomass Fractions and Model Trees of the Main Coniferous Woody Plants
Next Article in Special Issue
The Structural Relationship between Service Quality and Sustainable Use Intention of Voice Search Technology in Korea
Previous Article in Journal
The Various Effects of Technology Trade on the Sustainable Market Value of Firms in OECD Countries
Previous Article in Special Issue
Analyzing the Impact of Cyber Security Related Attributes for Intrusion Detection Systems
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 13
Issue 22
10.3390/su132212693
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

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

by
Adnan Yousaf
1,
Rao Muhammad Asif
1,
Mustafa Shakir
1,
Ateeq Ur Rehman
2,*,
Fawaz Alassery
3,
Habib Hamam
4,5,6 and
Omar Cheikhrouhou
7
1
Department of Electrical Engineering, Superior University, Lahore 54000, Pakistan
2
Department of Electrical Engineering, Government College University, Lahore 54000, Pakistan
3
Department of Computer Engineering, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia
4
Faculty of Engineering, Uni de Moncton, Moncton, NB E1A 3E9, Canada
5
Canadian Institute of Technology, 1001 Tirana, Albania
6
Department of Electrical and Electronic Engineering Science, School of Electrical Engineering, University of Johannesburg, Johannesburg 2006, South Africa
7
CES Laboratory, National School of Engineers of Sfax, University of Sfax, Sfax 3038, Tunisia
*
Author to whom correspondence should be addressed.
Sustainability 2021, 13(22), 12693; https://doi.org/10.3390/su132212693
Submission received: 20 August 2021 / Revised: 15 October 2021 / Accepted: 10 November 2021 / Published: 16 November 2021
(This article belongs to the Special Issue Machine Learning, Data Mining and IoT Applications in Smart and Sustainable Networks)
Browse Figures
Versions Notes

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.
Keywords: binary genetic algorithm; price forecasting; energy management system; mean absolute percentage error; firefly algorithm binary genetic algorithm; price forecasting; energy management system; mean absolute percentage error; firefly algorithm

Share and Cite

MDPI and ACS Style

Yousaf, A.; Asif, R.M.; Shakir, M.; Rehman, A.U.; Alassery, F.; Hamam, H.; Cheikhrouhou, O. A Novel Machine Learning-Based Price Forecasting for Energy Management Systems. Sustainability 2021, 13, 12693. https://doi.org/10.3390/su132212693

AMA Style

Yousaf A, Asif RM, Shakir M, Rehman AU, Alassery F, Hamam H, Cheikhrouhou O. A Novel Machine Learning-Based Price Forecasting for Energy Management Systems. Sustainability. 2021; 13(22):12693. https://doi.org/10.3390/su132212693

Chicago/Turabian Style

Yousaf, Adnan, Rao Muhammad Asif, Mustafa Shakir, Ateeq Ur Rehman, Fawaz Alassery, Habib Hamam, and Omar Cheikhrouhou. 2021. "A Novel Machine Learning-Based Price Forecasting for Energy Management Systems" Sustainability 13, no. 22: 12693. https://doi.org/10.3390/su132212693

APA Style

Yousaf, A., Asif, R. M., Shakir, M., Rehman, A. U., Alassery, F., Hamam, H., & Cheikhrouhou, O. (2021). A Novel Machine Learning-Based Price Forecasting for Energy Management Systems. Sustainability, 13(22), 12693. https://doi.org/10.3390/su132212693

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop