BiGTA-Net: A Hybrid Deep Learning-Based Electrical Energy Forecasting Model for Building Energy Management Systems

The growth of urban areas and the management energy resources highlight need for precise short-term load forecasting (STLF) in systems to improve economic gains reduce peak usage. Traditional deep learning models STLF present challenges addressing these demands efficiently due their limitations modeling complex temporal dependencies processing large amounts data. This study presents a groundbreaking hybrid model, BiGTA-net, which integrates bi-directional gated recurrent unit (Bi-GRU), convolutional network (TCN), an attention mechanism. Designed explicitly day-ahead 24-point multistep-ahead building electricity consumption forecasting, BiGTA-net undergoes rigorous testing against diverse neural networks activation functions. Its performance is marked by lowest mean absolute percentage error (MAPE) 5.37 root squared (RMSE) 171.3 on educational dataset. Furthermore, it exhibits flexibility competitive accuracy Appliances Energy Prediction (AEP) Compared traditional models, reports remarkable average improvement approximately 36.9% MAPE. advancement emphasizes model’s significant contribution accentuating efficacy proposed approach power system optimizations smart city enhancements.