Long-Term Load Forecasting Incorporating GDP and Population Dynamics
Downloads
This research paper seeks to create a dependable long-term load forecasting (LTLF) model for Nigeria that includes socio-economic indicators like Gross Domestic Product (GDP) and population trends. Accurate electricity demand forecasts help policymakers plan infrastructure expansion to meet demand increases due to population growth and socio-economic development. A multi-model approach integrating Linear Regression with Support Vector Machines and Artificial Neural Networks was used to reach this objective. The performance of these models was assessed through implementation and comparison on MATLAB and Python platforms to determine predictive capabilities. The Artificial Neural Network model achieved the best results among its counterparts by having the lowest RMSE and MAPE values. By employing a multi-model approach, the test results showed that this approach gave an RMSE less than 1000GWh compared to all forecast methods giving an RMSE less than 9000GWh and greater than 1500GWh thereby establishing it as the most dependable forecast technique with superior error metrics. The findings encompass electricity load predictions from 2024 through 2035 for every model.
Isaac, S., Tolulope, O., Ayokunle A. & Peter, A (2016). Short-term load forecasting using the time series and artificial neural network methods. Journal of Electrical and Electronics Engineering, 11(1), 72-81.
Thomas, O. E.& Okafor, E. N. C. (2021). Electrical Energy Demand Forecast in Nigeria Between 2020 - 2040 Using Probabilistic Extrapolation Method. International Journal of Engineering Science and Application. 5(3), 71-85.
Onochie, U. P., Egware, H. O. & Eyakwanor, T. O. (2015). The Nigeria Electric Power Sector, Opportunities and Challenges. Journal of Multidisciplinary Engineering Science and Technology (JMEST). 2(4), 112-124.
McGrath, A., & Jonker, A. (2024). What is load forecasting?. IBM Knowledge Center. Retrieved from https://www.ibm.com. Discusses different types of load forecasting and their challenges in modern grids.
Bureau of Statistics, (2022), Demographic Statistical Bulletin.Retrieved on September 10, 2024 from
https://nigerianstat.gov.ng/elibrary/read/12414.
Hammad, M. A., Jereb, B., Rosi, B. & Dragan, D. (2020): Methods and Models for Electricity Load Forecasting: A Comprehensive Review. Logistics & Sustainable Transport. 11(1), 51-76.
Addeh E. (2023). Nigeria’s Power supply woes continue. This day
https://thisdaylive.com/index.php/2024/02/11/nigerias-power-supply-woes-continue.
Sasu, D. D.(2023) Electricity in Nigeria - statistics & facts.Statista. Retrieved on October 1, 2024 from https://www.statista.com/topics/9470/electricity-in-nigeria/#topicOverview.
IEA (2021), World Energy Outlook 2021, IEA, Paris https://www.iea.org/reports/world-energy-outlook-2021.
Sovacool, B. K., Rayan, S. et al. (2017) Integrating social science in energy research. Energy Research and Social Sciences. 6(1):95-99.
Zhanga, X., Wanga, J.,& Zhang, k. (2017). Short-Term Electric Load Forecasting Based on Singular Spectrum Analysis and Support Vector Machine Optimized by Cuckoo Search Algorithm. Electric Power Systems Research, 146(1), 270–285.
Ben-Hur, Asa; Horn, David; Siegelmann, Hava; Vapnik, Vladimir N. ""Support vector clustering" (2001);". Journal of Machine Learning Research. 2: 125–137.
Buitrago J and Asfour S 2017 Short-Term Forecasting of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks with Exogenous Vector Inputs 1–24.
