Electricity Market Price Forecasting Strategies for Effective Bidding Using LSTM Technique

Accurately estimating the pricing of the electricity market is crucial for market players to maximize their profitability and bid tactics. This paper proposed the LSTM method and strategies used to estimate prices in the energy market, with an emphasis on their applicability and efficiency in directing bidders' actions in future markets. A wide range of predictive modelling techniques used in electricity price forecasting. Historically, the underlying trends and patterns in historical pricing data have been captured using statistical models such as econometric models and time series analysis. Neural networks, support vector machines, and ensemble methods are a few promising approaches that can be used to incorporate a large number of input variables and capture nonlinear relationships. To improve the accuracy, precision and adaptability of electricity price prediction by utilizing temporal dependencies and multi resolution capabilities, Long Short Term Memory (LSTM) is proposed. The proposed approach enhancing price forecast performance by combining the advantages of statistical and machine learning techniques for accurate bidding. The results achieved as MSE is 0.1459 and RMSE is 0.4871. The proposed approach will help in effective bidding for market players.