Electricity Demand

Electricity demand forecasting aims to accurately predict future energy consumption, crucial for efficient resource allocation and grid stability. Current research heavily utilizes deep learning models, including recurrent neural networks (RNNs) like LSTMs, convolutional neural networks (CNNs), and transformer architectures, often combined with traditional time series methods or enhanced by techniques like transfer learning and contrastive learning to improve accuracy and handle data complexities. These advancements are significant for optimizing energy production, reducing costs, integrating renewable sources, and mitigating the environmental impact of energy consumption. Furthermore, incorporating diverse data sources, such as news sentiment analysis and building characteristics, is improving forecast precision.