Paper Titles
Performance Evaluation of Reverse Power Flow on Electrical Power Network with Wind Energy Source
p.91
Techno-Economic Modeling of Hybrid PV/Wind/Biomass Microgrid Renewable Energy Systems Using Load Following Dispatch Method
p.103
MPC-Based Power Transmission Line Model for Fast Recovery from Perpetual Faults
p.117
A Compact Review on the Impacts of Integration of Large-Scale Renewable Energy Sources into Grid-Connected Power Systems
p.125
Development of a Predictive Model for Monthly Electricity Consumption Using Population and Weather Data with Web-Based Programming Language
p.139
Analysis of Metal Oxide Surge Arrester’s Dynamic Performance for Lightning Protection
p.147
Dependency of Soil Resistivity on Soil Factors for Optimal Grounding System Performance: A Case Study of Six Sites at Funaab, Nigeria
p.163
Optimized Design for the Flap Folding System of a Carton Sealer Machine by Automation
p.177
Mobile Robot Selection Using AHP MOORA and Simulation Modelling for Material Handling Automation
p.185

Development of a Predictive Model for Monthly Electricity Consumption Using Population and Weather Data with Web-Based Programming Language

Article Preview

Abstract:

Predicting accurate electricity consumption on electrical distribution network is essential for efficient energy management, particularly in institutional settings where demand fluctuates due to population growth and weather variations. Traditional prediction models often lack real-time accessibility. This study presents a browser-based simulation model for monthly electricity consumption using historical data and dynamic weather inputs, implemented with HTML, CSS, and JavaScript. The model generates forecasts by applying statistically plausible variations (±5%) to historical consumption patterns, integrated with simulated weather data for real-time scenario testing. Compared to complex machine learning approaches, this lightweight solution offers enhanced scalability, remote accessibility, and instant updates without server dependencies which makes it more applicable for smart grid systems and utility management. Results demonstrate its utility as a practical tool for preliminary energy trend analysis, supporting integration with cloud-based data sources. This research will contribute to accessible energy forecasting tools and provides a practical tool for optimizing electricity consumption on electrical distribution network in institutional environments and for institutional planning.

You might also be interested in these eBooks
Power Engineering, Robotics and Automation, Fault Detection View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 932)

Pages:

139-145

DOI:

https://doi.org/10.4028/p-6YP5kS

Citation:

Cite this paper

Online since:

February 2026

Authors:

Patrick Taiwo Ogunboyo*, Emoemi S. Magi, Emmanuel Ayomide Ekson

Keywords:

Electricity Forecasting, Energy Management, Statistical Variation, Weather Data, Web-Based Simulation

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

© 2026 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

References

[1] A. O. Melodi, and P. T. Ogunboyo, Power Distribution Problems on 11kV Feeder Networks in Akure Urban Load Environment, IEEE Xplore, (2013) 292-300.

DOI: 10.1109/nigercon.2013.6715668

Google Scholar

[2] International Energy Agency (IEA), Energy efficiency 2021: Analysis and forecast to 2024. https://www.iea.org/reports/energy-efficiency-(2021).

Google Scholar

[3] P. T. Ogunboyo, and A. O. Melodi, Investigation of Load Power Variation on Ilesha Electrical Distribution Feeder, Akure, Ondo State, Nigeria,'' European International Journal of Science and Technology (EIJST), Vol. 5, No. 7, (2016) 58-65, ISSN:2304-9693.

DOI: 10.4028/www.scientific.net/jera.37.52

Google Scholar

[4] P. T. Ogunboyo, R. Tiako, and I. E. Davidson, Effectiveness of Dynamic Voltage Restorer for Unbalanced Voltage Mitigation and Voltage Profile Improvement in Secondary Distribution System, in Canadian Journal of Electrical and Computer Engineering, vol.42, no. 2, (2018) 105-115, Spring 2018.

DOI: 10.1109/CJECE.2018.2858841

Google Scholar

[5] M.O. Oseni, Energy access in sub-Saharan Africa: Progress and challenges. Renewable Energy Journal, 14(2), (2019) 198–214.

Google Scholar

[6] H.I. Mohammed, L.E. Ismaila, A. Ahmed, and O.F. Onifade, .An intelligent predictive model for electricity consumption in institutional buildings. International Journal of Computer Science and Technology, 10(3), (2019) 21–24.

Google Scholar

[7] P. T. Ogunboyo, R. Tiako, and I. E. Davidson, .An Improvement of Voltage Unbalance in a Low Voltage 11/0.4 kV Electric Power distribution Network under 3-phase Unbalance Load Condition using Dynamic Voltage Restorer. IEEE Xplore, (2017) 126-131.

DOI: 10.1109/powerafrica.2017.7991211

Google Scholar

[8] F. Magoulès, and H. Zhao, Machine learning approaches for energy consumption forecasting. Computational Intelligence in Energy Management, 5(1), (2016) 23–39.

Google Scholar

[9] P. T. Ogunboyo, and O. Ogunlade, Stochastic Evaluation of Load Power System of Lines in Sub-Saharan Africa under Condition of Stability Advances in Science and Technology Journal, ISSN: 1662-0356, Vol. 154, ( 2024) 191-201.

DOI: 10.4028/p-gcup3t

Google Scholar

[10] P. T. Ogunboyo, and M. O. Adegoke, Electrical Distribution Industry- Long Term Load Forecast on Ilesha Road 11kV Feeder, Akure, Ondo State, Nigeria, International Journal of Engineering Research in Africa, Vol. 37, (2018) 52-61.

DOI: 10.4028/www.scientific.net/jera.37.52

Google Scholar

[11] C. K. Tang, Determinants of electricity consumption in Malaysian institutions. Journal of Energy Research, 12(1), (2019) 34–47.

Google Scholar

[12] C. Deb, S. E. Lee, M. Santamouris, and T. Zhang, Forecasting cooling loads in institutional buildings: A data-driven approach. Energy and Buildings, 128, (2016) 601–613.

DOI: 10.1016/j.enbuild.2016.07.001

Google Scholar

[13] P. T. Ogunboyo, and I. E. Davidson, Impact of DVR in Power Quality Disturbances Mitigation in Municipal Distribution Systems, IEEE Xplore, (2016) 1-6, 2025.https://ieeexplore.ieee.org/xp/conhome/.

Google Scholar

[14] A. A. Ogundipe, F. A. Adebayo, and T. F. Odebiyi, Electricity consumption and economic growth in Nigeria. African Development Review, 28(3), (2016) 243–257.

DOI: 10.1111/1467-8268.12203

Google Scholar

[15] M. Rahman, M. Masud, and P. Biswas, Prediction of electric energy consumption using recurrent neural networks. International Journal of Smartcare Home, 1(1), (2021) 23–34.

DOI: 10.21742/26531941.1.1.03

Google Scholar

[16] G. V. Reddy, L.J. Aitha, and C. Poojitha, Electricity consumption prediction using machine learning.E3S Web of Conferences, 391, ( 2023) 01048.

DOI: 10.1051/e3sconf/202339101048

Google Scholar

[17] L. J. Aitha, G. V. Reddy, and C. Poojitha, Electricity consumption prediction using machine learning.E3S Web of Conferences, 391, (2023) 01048.

DOI: 10.1051/e3sconf/202339101048

Google Scholar