MEDIUM TERM LOAD FORECASTING OF 33 kV LINE LOADING: A CASE STUDY OF OTA 132/33 kV SUB-STATION OGUN STATE, NIGERIA

Authors

  • O. O. Olusanya Department of Computer Engineering, Bells University of Technology, Ota, Ogun State. Nigeria https://orcid.org/0000-0001-6893-0472
  • A. O. Onokwai Department of Mechanical Engineering, Pan Atlantic University, Lagos. Nigeria https://orcid.org/0000-0002-6573-4668
  • R. O. Oyebisi Department of Electrical/Electronics and Telecommunication Engineering, Bells University of Technology, Ota, Ogun State. Nigeria

DOI:

https://doi.org/10.4314/njt.v43i4.14

Keywords:

ARIMA: Auto Regressive Integrated Moving Average, MAE: Mean Absolute Error, MAPE: Mean Absolute Percent-age Error, RMSE: Root Mean Square Error, R-squared

Abstract

Peak load forecasting plays a pivotal role in the efficient operation and planning of power systems, influencing decision-making processes for resource allocation and infrastructure development. The Ota 132/33 kV substation in Nigeria is facing increasing demand due to rapid industrialization and urbanization. This has strained the substation's infrastructure, leading to issues like transformer overloading, voltage fluctuations, and power outages. The area's trade center status and numerous industries further stress transformers, increasing wear and tear and potentially jeopardizing their reliability. This study delves into the realm of 33 kV feeders at the OTA Transmission Substation, aiming to unravel the intricacies of peak load patterns and provide a forecast for the next five years. Leveraging historical data spanning from 2018 to 2022, sourced from OTA Transmission Substation was used to forecast from 2023 to 2027. The research employs the Auto-Regressive Integrated Moving Average (ARIMA) model to discern trends and project future peak loads. Performance metrics, including Mean Absolute Error, Mean Absolute Percentage Error, Root Mean Square Error, and R-squared, are meticulously evaluated to assess the robustness of the forecasting model. The findings shed light on the unique characteristics of each feeder, with Sumo, Amje, and Idiroko having a better predictive accuracy performance with minimal errors, while Sango, FSM, and Estate show a moderate level of predictive accuracy probably due to the presence of little nuance in their data set.  Where the Sango 33 kV feeder displayed an upward trend of 18.91MW in 2023 and 19.34 MW in 2027. Sumo 33 kV feeder exhibits a decline trend from 3.21 MW (2023) to 2.23 MW in 2027. FSM 33 kV feeder shows a fluctuation pattern while Amje 33 kV feeder indicates a highly stable trend of 22.29 MW all over. Idiroko 33 kV feeder shows a steadily increasing trend of 16.09 MW (2024) to 16.25 MW in 2027. The Estate 33 kV feeder on the other hand depicts a relatively stable pattern. This study not only contributes to the localized understanding of peak load dynamics but also serves as a template for similar investigations in other power distribution networks and unveils other alternative data science-based models for future researchers.

Author Biographies

  • O. O. Olusanya, Department of Computer Engineering, Bells University of Technology, Ota, Ogun State. Nigeria

    Department of Computer Engineering, Bells University of Technology, Ota, Ogun State. Nigeria

    Associate Professor

  • A. O. Onokwai, Department of Mechanical Engineering, Pan Atlantic University, Lagos. Nigeria

    2Department of Mechanical Engineering,Bells University of Technology, Ota, Ogun State. Nigeria

    Senioe Lecturer

  • R. O. Oyebisi, Department of Electrical/Electronics and Telecommunication Engineering, Bells University of Technology, Ota, Ogun State. Nigeria

    3Department of Electrical/Electronics and Telecommunication Engineering, Bells University of Technology, Ota, Ogun State. Nigeria.

References

[1] Alizadeh, R., Soltanisehat, L., Lund, P. and Zamanisabzi, H., “Improving renewable energy policy planning and decision-making through a hybrid MCDM method”. Energy Policy, Vol 137, 2020. p. 111174

[2] Chanchangi, Y. N. et al., “Nigeria's energy review: Focusing on solar energy potential and penetration”. Environment, Development and Sustainability, 2022. pp. 1-42.

[3] Gomes, I. S. F., Perez, Y. and Suomalainen, E., “Coupling small batteries and PV generation: A review”. Renewable and Sustainable Energy Reviews, Vol 126, 2020. p. 109835.

[4] Okafor, C. C. et al., “Biomass utilization for energy production in Nigeria: A review”. Cleaner Energy Systems, 2022. p. 100043.

[5] Edomah, N., Ndulue, G. and Lemaire, X., “A review of stakeholders and interventions in Nigeria's electricity sector”. Heliyon , 7(9). 2021.

[6] Olaleye, T. et al.,.” Predictive Analytics and Software Defect Severity: A Systematic Review and Future Directions”. Scientific Programming, 2023. pp. 1-18.

[7] Tyralis, H., Karakatsanis, G., Tzouka, K. and Mamassis, N., “ Exploratory data analysis of the electrical energy demand in the time domain in Greece”. Energy, Vol 134, 2017. pp. 902-918.

[8] Jiang, Z. and Liu, Z., “Policies and exploitative and exploratory innovations of the wind power industry in China: The role of technological path dependence” . Technological Forecasting and Social Change, Vol 77 , 2022. p. 121519.

[9] Shamim, G. and Rihan, M., “Exploratory Data Analytics and PCA-Based Dimensionality Reduction for Improvement in Smart Meter Data Clustering”. Journal of Research, 2023. pp. 1-10.

[10] Chodakowska, E., Nazarko, J. and Nazarko, Ł., “Arima models in electrical load forecasting and their robustness to noise”. Energies, 14(23), 2021. p. 7952.

[11] Dat, N. Q., Anh, N. T. N., Anh, N. N. and Solanki, V. K., “Hybrid online model based multi seasonal decompose for short-term electricity load forecasting using ARIMA and online RNN”. Journal of Intelligent & Fuzzy Systems, 41(5), 2021. pp. 5639-5652.

[12] Gupta, A. and Kumar, A., “Mid term daily load forecasting using ARIMA, wavelet-ARIMA and machine learning”, IEEE, 2020. pp. 1-5.

[13] Ade-Ikuesan, O. O., Osifeko, M. O., Okakwu, I..K., Folaranmi, K. S., and Alao, P. O “Prediction of electricity consumption demand pattern for 2018 in Ogun State, Nigeria”. Journal of Applied Sciences and Environmental Management, 22(6), 2018. pp. 883-886.

[14] Rajbhandari, Y., Marahatta, A. and Ghimire, B., “Impact study of temperature on the time series electricity demand of urban nepal for short-term load forecasting”. Applied System Innovation, 4(3), 2021. p. 43.

[15] Li, R., Jiang, P., Yang, H. and Li, C., “A novel hybrid forecasting scheme for electricity demand time series” . Sustainable Cities and Society, Vol 55, 2020. p. 102036.

[16] Bouktif, S., Fiaz, A., Ouni, A. and Serhani, M. A., “Single and Multi-Sequence Deep Learning Models for Short and Medium Term Electric Load Forecasting”. Energies, 12(1), 2019. p. 149.

[17] Noureen, S., Atique, S., Roy, V. and Bayne, S., “Analysis and application of seasonal ARIMA model in Energy Demand Forecasting: A case study of small scale agricultural load. Dallas” , s.n., 2019. pp. 521-524.

[18] Alasali, F., Nusair, K., Alhmoud, L. and Zarour, E., “Impact of the COVID-19 Pandemic on Electricity Demand and Load Forecasting” . Sustainability, 13(3). 2021.

[19] Shah, I., Iftikhar, H., Ali, S. and Wang, D., “Short-Term Electricity Demand Forecasting Using Components Estimation Technique” . Energies, 12(13), 2019. p. 2532.

[20] Maldonado, S., González, A. and Crone, S., “Automatic time series analysis for electric load forecasting via support vector regression”. Applied Soft Computing, Vol 83, 2019. p. 105616.

[21] Ngabesong, R. and McLauchlan, L., “Implementing “R” Programming for Time Series Analysis and Forecasting of Electricity Demand for Texas, USA. Lafayette”, IEEE, 2019.pp. 1-4.

[22] Aseeri, A. O. “Effective RNN-Based Forecasting Methodology Design for Improvi-ng Short-Term Power Load Forecasts: Application to Large-Scale Power-Grid Time Series”, Journal of Computational Science, Vol 68. 2023. p. 101984.

[23] Nepal, B., Yamaha, M., Yokoe, A., and Yamaji, T., “Electricity load forecasting using clustering and ARIMA model for energy management in buildings”. Japan Architectural Review, 3(1), 2019. pp. 62-76.

[24] Ade-Ikuesan, O. O., Oyedeji, A. O. and Osifeko, M. O. “Linear regression long-term energy demand forecast modelling in Ogun State, Nigeria”. Journal of Applied Sciences and Environmental Management, 23(4), 2019. pp. 753-757.

[25] Obi, P. I, Okonkwo. I. I. and. Ogba, C. O., “Power Supply Enhancement in Onitsha Distribution Network using Distribution Generations”. Nigerian Journal of Technology (NIJOTECH); 41(2), 2022. pp.318-329 .http://dx.doi.org/10.4314/njt.v41i2.14.

[26] Ashigwuike, E. C., Aluya, A. R. A., Emechebe, J. E. C. and Benson, S. A. “ Medium Term Electrical Load Forecast of Abuja Municipal Area Council using Artificial Neural Network Method”. Nigerian Journal of Technology (NIJOTECH) 39( 3), 2020, pp. 860 – 870. http://dx.doi.org/10.4314/njt.v39i3.28

[27] Tian, L. W., Xu, H. and Tong, H., “Short-term power load forecasting model based on t-SNE dimension reduction visualization analysis, VMD and LSSVM improved with chaotic sparrow search algorithm optimization”. Journal of Electrical Engineering & Technology, 17(5), 2022, pp. 2675-2691.

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Published

2025-01-08

Issue

Section

Computer, Telecommunications, Software, Electrical & Electronics Engineering

How to Cite

MEDIUM TERM LOAD FORECASTING OF 33 kV LINE LOADING: A CASE STUDY OF OTA 132/33 kV SUB-STATION OGUN STATE, NIGERIA. (2025). Nigerian Journal of Technology, 43(4), 743 – 753. https://doi.org/10.4314/njt.v43i4.14