Published February 28, 2024 | Version CC-BY-NC-ND 4.0
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Modelling and Distribution of Electricity Load Forecasting in Nigeria Power System (Olu-Ode Community)

  • 1. Department of Electrical and Electronic Engineering, Osun State Polytechnic IREE
  • 1. Department of Electrical and Electronic Engineering, Osun State Polytechnic IREE
  • 2. Department of Electrical and Electronic Engineering, Osun State Polytechnic IREE


Abstract: To plan for energy generation to fulfill customer demand as the population grows, load forecasting is often used to anticipate and predict a region's power demand growth. A power load To sell, plan, and purchase energy for power systems, forecasting might be employed. From electrical energy production through distribution, it is highly helpful. Power system forecasting may be broadly categorized into three classes: An hour to a week is considered short-term electric load forecasting, a week (7 days) to a year is considered medium-term electric load forecasting, and a year and beyond is considered long-term electric load forecasting. In emerging nations where the energy demand is erratic due to fast economic expansion and a rise in the rate of rural-urban migration, accurate load forecasting may aid in creating a strategy. Various load forecasting techniques, including expert systems, fuzzy logic, regression techniques, and artificial neural networks (ANN), were researched. However, current methods may only sometimes provide more accuracy in predicting short-term stress. To address this issue, a novel strategy for anticipating short-term load is put forward in this study. Long short-term memory (LSTM) and convolutional neural networks are included in the created approach. The technique is used to anticipate the short-term electrical demand for the power system in Nigeria. Additionally, the usefulness of the proposed method is confirmed by comparing the forecasting errors of the suggested method with those of other existing methods like the long short-term memory network, the radial basis function network, and the extreme gradient boosting algorithm. It is discovered that the suggested technique produces better short-term load forecasting precision and accuracy.



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Manuscript received on 21 November 2023 | Revised Manuscript received on 05 January 2024 | Manuscript Accepted on 15 February 2024 | Manuscript published on 28 February 2024.


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