Short Term Load Forecasting for Smart Distribution System Planning Using Deep Neural Networks: A Hybrid Approach

International Journal of Electrical and Electronics Engineering, Journal Year: 2024, Volume and Issue: 11(5), P. 138 - 149

Published: May 31, 2024

Accurate load forecasting plays a crucial role in the management and control of electrical power distribution systems. Short-Term Load Forecasting (STLF) is particularly vital for planning, as it provides precise predictions immediate future. This paper introduces an innovative hybrid deep-learning model specifically designed STLF The proposed combines strengths Bidirectional Long Memory (Bi-LSTM) Gated Recurrent Unit (GRU) networks. study utilizes high-resolution real-world dataset, consisting historical consumption weather-related features, with 30-minute intervals from period January 1, 2006, to December 31, 2010. benchmarked against prominent standalone models such Bi-LSTM, GRU, LSTM, CNN, like CNN-LSTM ConvLSTM-GRU. model's performance evaluated using various validation metrics, including Rsquared error, Root Mean Squared Error (RMSE), (MSE), Absolute (MAE), Percentage (MAPE). results show that outperforms all conventional models, offering significant improvements forecast accuracy. Thus, highlights potential revolutionizing methodologies, paving way smart system.

Language: Английский

Fine-Grained Agricultural Facility Power Forecasting Based on Empirical Mode Decomposition

2022 International Joint Conference on Neural Networks (IJCNN), Journal Year: 2024, Volume and Issue: 47, P. 1 - 8

Published: June 30, 2024

Language: Английский