DEVELOPMENT OF AN IMPROVED SHORT-TERM PEAK LOAD FORECASTING MODEL BASED ON SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE AND NONLINEAR AUTOREGRESSIVE NEURAL NETWORK FOR NIGERIA POWER SYSTEM GRID

📄 Project Abstract

Electricity demand forecasting is a central and integral process for planning periodical operations and facility expansion in the electricity sector. Demand pattern is very complex due to the highly unpredictable behavior of consumers load consumption. Therefore, finding an appropriate forecasting model for a specific electricity network at peak demand is not an easy task for the utilities and policymakers. Many load forecasting methods developed in the past decades were characterized by poor precision, and large forecast error because of their inability to adapt to changes in dynamics of load demand. To fill this gap, this research has developed an improved short-term daily peak load forecasting model based on Seasonal Autoregressive Integrated Moving Average (SARIMA) and Nonlinear Autoregressive Neural Network (NARX). The developed model used SARIMA to captures the linear pattern (trend) and seasonality of the load time series but due to seasonal and cyclical nature of the load behav...