TCN-QRNN model for short term energy consumption forecasting with increased accuracy and optimized computational efficiency

In the context of the growing volume and complexity of data, traditional methods of energy consumption forecasting, such as Recurrent Neural Networks (RNN), face computational complexity issues that limit their real-time application. This also complicates the effective management of energy systems. In this work, a new model is proposed that combines the advantages of Temporal Convolutional Networks (TCN) and Quasi-Recurrent Neural Networks (QRNN) for energy consumption forecasting. TCN allows for effective processing of long time series, capturing essential temporal dependencies. Meanwhile, QRNN reduces computational costs through parallelization of operations and an optimized architecture. The effectiveness of the proposed model has been assessed in comparison with traditional methods such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, as well as other convolutional approaches. Experimental results show that the proposed TCN-QRNN model outperforms traditional methods by 40% in accuracy compared to LSTM and by 8% in terms of metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) compared to TCN-LSTM, while reducing data processing time by 30%. Additionally, the model has a significantly smaller number of parameters than LSTM and GRU, making it suitable for environments with limited computational resources. The proposed model ensures a high level of energy consumption forecasting accuracy while significantly reducing processing time, making it promising for use in real-world energy systems.