International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 11, Issue 5 (May 2024), Pages: 230-248

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 Original Research Paper

Enhancing smart grid electricity prediction with the fusion of intelligent modeling and XAI integration

 Author(s): 

 Jamshaid Iqbal Janjua 1, 2, Reyaz Ahmad 3, Sagheer Abbas 4, Abdul Salam Mohammed 3, Muhammad Saleem Khan 1, Ali Daud 5, Tahir Abbas 6, Muhammad Adnan Khan 7, 8, 9, *

 Affiliation(s):

 1School of Computer Science, National College of Business Administration and Economics, Lahore, Pakistan
 2Al-Khawarizmi Institute of Computer Science (KICS), University of Engineering and Technology, Lahore, Pakistan
 3Department of General Education, Skyline University College, University City Sharjah, Sharjah, United Arab Emirates
 4Department of Computer Science, Bahria University, Lahore, Pakistan
 5Faculty of Resilience, Rabdan Academy, Abu Dhabi, United Arab Emirates
 6Department of Computer Science, TIMES Institute, Multan, Pakistan
 7School of Computing, Skyline University College, University City Sharjah, Sharjah, United Arab Emirates
 8Riphah School of Computing and Innovation, Faculty of Computing, Riphah International University, Lahore, Pakistan
 9Department of Software, Faculty of Artificial Intelligence and Software, Gachon University, Seongnam, South Korea

 Full text

  Full Text - PDF

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0003-4854-9935

 Digital Object Identifier (DOI)

 https://doi.org/10.21833/ijaas.2024.05.025

 Abstract

This study examines the vital role of accurate load forecasting in the energy planning of smart cities. It introduces a hybrid approach that uses machine learning (ML) to forecast electricity usage in homes, improving accuracy through the extraction of correlated features. The accuracy of predictions is assessed using loss functions and the root mean square error (RMSE). In response to increasing interest in explainable artificial intelligence (XAI), this paper proposes a framework for predicting energy consumption in smart homes. This user-friendly approach helps users understand their energy consumption patterns by employing shapley additive explanations (SHAP) techniques to provide clear explanations. The research uses gradient boosting and long short-term memory neural networks to forecast energy usage. In the context of sustainable urban development, it emphasizes the importance of conserving energy in homes. The paper explores AI and ML methods for predicting residential energy use, aiming to make socially meaningful impacts. It highlights the need to understand the factors affecting predictions to improve the accountability, reliability, and justification of decisions in energy optimization. Explainable AI techniques are used to gain insights into the prediction models and identify factors influencing household energy consumption. This research aids in decision-making processes related to electricity forecasting, advancing discussions on intelligent decision-making in power management, especially in smart grids and sustainable urban development.

 © 2024 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 Smart cities, Machine learning, Energy forecasting, Explainable artificial intelligence, Residential energy conservation

 Article history

 Received 4 January 2024, Received in revised form 13 May 2024, Accepted 16 May 2024

 Acknowledgment 

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Janjua JI, Ahmad R, Abbas S, Mohammed AS, Khan MS, Daud A, Abbas T, Khan MA (2024). Enhancing smart grid electricity prediction with the fusion of intelligent modeling and XAI integration. International Journal of Advanced and Applied Sciences, 11(5): 230-248

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 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7 Fig. 8 Fig. 9 Fig. 10 Fig. 11 Fig. 12 Fig. 13 

 Tables

 Table 1 Table 2 

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