The Number of Nodes Effect to Predict the Electrical Consumption in Seven Distinct Countries


  • Yanuar Mahfudz Safarudin United Arab Emirates University
  • Namya Musthafa Electrical and Communication Engineering Department, College of Engineering, United Arab Emirates University
  • Abdelrahman Elkhidir Electrical and Communication Engineering Department, College of Engineering, United Arab Emirates University
  • Shah Zahid Khan Electrical and Communication Engineering Department, College of Engineering, United Arab Emirates University



forecast, artificial neural network, electrical consumption, hidden layers


This paper presents a machine learning-based approach for forecasting electrical consumption in seven selected countries across different geographical categories. The data, sourced from The International Energy Agency, is analysed and condensed to focus on specific nations: Northern (Norway, Canada), Southern (Chile, Australia), Four-season (France, Japan), and a Tropical country (Colombia). The unique electrical consumption patterns influenced by regional climate characteristics make this study compelling for machine learning applications. From the dataset comprising over 132,000 records from January 2010 to May 2023 across 53 countries, a refined dataset focusing on 791 data points from seven specifically chosen countries to simplify the study. A significant part of the paper details the machine learning design for electrical consumption forecasting. Specifically, Artificial neural network architecture is proposed to predict consumption. The input features encompass the year, month, and country, with the output being the anticipated electrical usage.


Ishak, Izzaamirah, Nor Salwati Othman, and Nor Hamisham Harun. "Forecasting electricity consumption of Malaysia’s residential sector: Evidence from an exponential smoothing model." F1000Research 11 (2022): 54.


Ciro Eduardo Bazán Navarro, Víctor Josué Álvarez-Quiroz, James Sampi, Adolfo Alfredo Arana Sánchez, Does economic growth promote electric power consumption? Implications for electricity conservation, expansive, and security policies, The Electricity Journal, Volume 36, Issue 1, 2023, 107235, ISSN 1040-6190, (

Bakare, M.S., Abdulkarim, A., Zeeshan, M. et al. A comprehensive overview on demand-side energy management towards smart grids: challenges, solutions, and future direction. Energy Inform 6, 4 (2023).

Dai, Liuyi, Rui Jia, and Xinran Wang. "Relationship between economic growth and energy consumption from the perspective of sustainable development." Journal of Environmental and Public Health 2022 (2022).

Kosowski P, Kosowska K, Janiga D. Primary Energy Consumption Patterns in Selected European Countries from 1990 to 2021: A Cluster Analysis Approach. Energies. 2023; 16(19):6941.

Corazza M, Conti V, Genovese A, Ortenzi F, Valentini MP. A Procedure to Estimate Air Conditioning Consumption of Urban Buses Related to Climate and Main Operational Characteristics. World Electric Vehicle Journal. 2021; 12(1):29.

Hasan Rafiq, Prajowal Manandhar, Edwin Rodriguez-Ubinas, Juan David Barbosa, Omer Ahmed Qureshi, Analysis of residential electricity consumption patterns utilizing smart-meter data: Dubai as a case study, Energy and Buildings, Volume 291, 2023, 113103, ISSN 0378-7788,

Madhukumar, Mithun & Sebastian, Albino & Liang, Xiaodong & Jamil, Mohsin & Shabbir, Md. Nasmus Sakib Khan. (2022). Regression Model-Based Short-Term Load Forecasting for University Campus Load. IEEE Access. PP. 1-1. 10.1109/ACCESS.2022.3144206.

Rui Wang, Hongguang Yun, Rakiba Rayhana, Junchi Bin, Chengkai Zhang, Omar E. Herrera, Zheng Liu, Walter Mérida, An adaptive federated learning system for community building energy load forecasting and anomaly prediction, Energy and Buildings, Volume 295, 2023, 113215, ISSN 0378-7788,

Zongxi Jiang, Luliang Zhang, Tianyao Ji, NSDAR: A neural network-based model for similar day screening and electric load forecasting, Applied Energy, Volume 349, 2023, 121647, ISSN 0306-2619,

Guo-Feng Fan, Ying-Ying Han, Jing-Jing Wang, Hao-Li Jia, Li-Ling Peng, Hsin-Pou Huang, Wei-Chiang Hong, A new intelligent hybrid forecasting method for power load considering uncertainty, Knowledge-Based Systems, Volume 280, 2023, 111034, ISSN 0950-7051,

Khansa Dab, Nilson Henao, Shaival Nagarsheth, Yves Dubé, Simon Sansregret, Kodjo Agbossou, Consensus-based time-series clustering approach to short-term load forecasting for residential electricity demand, Energy and Buildings, Volume 299, 2023, 113550, ISSN 0378-7788,

M. Gilanifar, H. Wang, L. M. K. Sriram, E. E. Ozguven and R. Arghandeh, "Multitask Bayesian Spatiotemporal Gaussian Processes for Short-Term Load Forecasting," in IEEE Transactions on Industrial Electronics, vol. 67, no. 6, pp. 5132-5143, June 2020, doi: 10.1109/TIE.2019.2928275.

The International Energy Agency, " Monthly Electricity Statistics"

J. Son, J. Cha, H. Kim and Y. -M. Wi, "Day-Ahead Short-Term Load Forecasting for Holidays Based on Modification of Similar Days’ Load Profiles," in IEEE Access, vol. 10, pp. 17864-17880, 2022, doi: 10.1109/ACCESS.2022.3150344




How to Cite

Y. M. Safarudin, N. Musthafa, A. Elkhidir, and S. Z. Khan, “The Number of Nodes Effect to Predict the Electrical Consumption in Seven Distinct Countries”, Jartel, vol. 13, no. 4, pp. 395-401, Dec. 2023.