부산대학교 도서관

Balancing supply and demand is crucial for efficient energy distribution. To achieve it, accurate short-term electrical load forecasting is essential. This study investigates the applicability of various machine learning architectures for short-term load forecasting, using an NSW load dataset from the Australian Energy Market Operator. The key finding is the superior performance of a hybrid model, which integrates LSTM and GRU layers, on the NSW load dataset. This study demonstrates hybrid neural network models can significantly improve the accuracy and reliability of energy load predictions, thereby suggesting a viable pathway for enhancing future utility management practices.