Power Load Forecasting System Based on Deep Hybrid Learning Model

Abstract

Accurate prediction of electricity demand is crucial to ensure the stability and dependability of local power grids. Numerous scholars have put forth comprehensive prediction systems; However, the majority of these models fail to capture the inherent global features present within the data. This study introduces a novel integrated prediction system that leverages the synergistic capabilities of Time Convolutional Network (TCN) and Long Short-Term Memory (LSTM) architectures, aiming to enhance the accuracy of short-term electric load forecasting. The initial step is to establish separate prediction models for LSTM and TCN, with a focus on electric load data. After combining the output results of these models, the reciprocal of the error square ratio was used as a weighting factor. By using this approach, the LSTM-TCN model for combined prediction is created. This research paper performs an exhaustive examination of the case study by employing authentic data sourced from the Australian Energy Management Authority. The study's findings support that the LSTM-TCN model outperforms both single prediction models and traditional network models in terms of performance. The results indicate that the LSTM-TCN model exhibits greater accuracy in predicting short-term energy demand.