TL;DR
The integration of RFE and Cuckoo Search enhances the model’s ability to identify relevant features and optimize hyperparameters, enabling efficient learning of complex temporal dependencies and nonlinear relationships in energy consumption data.
Against the backdrop of global energy conservation and emission reduction strategies, university teaching buildings are key targets for carbon emission management due to their multifunctional characteristics and distinct usage patterns. To address the limitations of conventional prediction models in capturing dynamic c...
A hybrid model combining TCN and NLSTM to leverage the strengths of both architectures is proposed, achieving up to a 12.7% reduction in Root Mean Square Error (RMSE) and a mutation-inspired modification of the Driving Training-Based Optimization algorithm dynamically tunes the model’s hyperparameters.
The Indonesian capital market, particularly the LQ45 Index and its leading sectoral stocks, exhibits high volatility and complex non-linear price patterns. This complexity renders conventional statistical methods insufficient for accurate forecasting and risk mitigation. This study aims to develop and compare three Dee...
Energy conservation in smart homes is crucial for optimizing electricity usage, reducing costs, and contributing to sustainability efforts. This study evaluates the performance of two deep learning models, the Bidirectional Long Short-Term Memory with Gated Recurrent Units (BiLSTM-GRU) and the traditional Recurrent Neu...
A comprehensive comparative analysis of the deep learning architectures such as Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Convolutional Neural Networks (CNN), Transformer models, and hybrid models based on the benchmark of four widely used renewable energy datasets revealed that the hybrid CNN-LSTM mo...
Overall, the study demonstrates that recurrent deep learning architectures provide reliable and operationally relevant solutions for electricity consumption forecasting in highly structured energy demand series.
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