Consumption Forecasting

Consumption forecasting, primarily focused on energy (electricity, PV power, wind power, and multi-energy systems), aims to accurately predict future consumption patterns to optimize resource allocation and grid management. Current research emphasizes the use of advanced machine learning models, including deep learning architectures like Transformers, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs, particularly LSTMs and BLSTMs), and hybrid approaches combining statistical methods (e.g., ARIMA) with deep learning. These advancements improve forecasting accuracy and address challenges like handling intermittent renewable energy sources and incorporating diverse data types (e.g., weather, calendar information, textual news). The resulting improvements have significant implications for energy system planning, grid stability, and efficient resource management.