MULTISTEP ELECTRICITY PRICE FORECASTING USING DEEP LEARNING TECHNIQUES

DEEP LEARNING (AI)

Forecasting electricity prices is a crucial component of the energy sector, having ramifications for consumers, regulators, and market players. This study offers a multi-step method for projecting power prices that include both short- and long-term projections. To improve prediction accuracy, the suggested methodology combines time series analysis and fundamental market data. The program predicts power costs for the upcoming few hours using previous pricing data, meteorological data, and demand trends. Recurrent neural networks and long short-term memory are two examples of deep learning methods that are used to capture complex temporal dependencies and nonlinear correlations in the data. The model includes projections for renewable energy generation, macroeconomic variables, and policy changes that might have a long-term influence on power markets. The model gives insights into pricing patterns and potential disruptions by taking these various aspects into account. These short- and long-term forecasts are combined with the multi-step forecasting framework to provide a thorough understanding of power price dynamics. This strategy improves market players' ability to make decisions, allowing them to plan investments in renewable energy sources, optimize trading tactics, and adjust to shifting market conditions helping to more effective operations of the energy market and a transition towards sustainable and resilient electricity systems.

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