Electricity Price

Electricity price forecasting is crucial for efficient energy market operation and sustainable development, focusing on accurately predicting volatile and often non-stationary price patterns. Current research employs diverse machine learning models, including recurrent neural networks (like LSTMs and GRUs), hybrid models combining time series analysis with neural networks, and causal inference methods to understand the impact of policies and external factors (e.g., renewable energy generation, fuel prices, consumer behavior). These advancements aim to improve prediction accuracy, enhance grid management, and inform policy decisions related to carbon emissions and the integration of renewable energy sources.