Enhancing Short-Term Load Forecasting Precision with Hybridized RNN and LSTM Model

Abstract

This study evaluates the efficiency of various machine learning models for short-term load forecasting (STLF) at the 132KV/33KV Bida Transmission Station. Using load outage data from the Kutigi and Lemu feeder stations and meteorological data from NIMET, the research compares models such as RNN, GRU, LSTM, and a hybrid RNN-LSTM model. The hybrid RNN-LSTM model demonstrated superior performance, achieving low error metrics (MAE: 0.101, RMSE: 0.138) and a near-perfect coefficient of determination (R²: 0.999). Pearson's correlation analysis was pivotal in feature selection, enhancing the model's predictive accuracy. These findings highlight the hybrid RNN-LSTM model's ability to effectively capture complex temporal patterns, making it a valuable tool for precise STLF. The outcomes have significant implications for improving grid reliability and operational efficiency in the energy sector.