BOL-LPP: A Bayesian-Optimized LSTM Model for Day-Ahead Load Price Forecasting in the ERCOT Market

Precise short-term load price forecasting is critical for uninterrupted and efficient power-system operation and energy-market performance. Although machine-learning techniques have been widely employed to predict market prices, achieving reliable day-ahead load price forecasts remains challenging in practice, especially in the Electric Reliability Council of Texas (ERCOT) energy-only market. This paper targets sufficiently accurate day-ahead load price prediction for ERCOT's zonal markets by modeling historical load, price, and weather data with a Long Short-Term Memory (LSTM) network whose hyperparameters are tuned via Bayesian Optimization (BO). The resulting Bayesian-Optimized LSTM for load price Prediction (BOL-LPP) is evaluated against classical statistical and deep-learning baselines. On the North-zone test set, BOL-LPP attains a Mean Absolute Error (MAE) of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\$}$</tex-math></inline-formula>0.0044/MWh, cutting the MAE by 32% relative to the strongest deep baseline (BiLSTM, MAE of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\$}$</tex-math></inline-formula>0.0065/MWh) and by over 99% compared with SARIMAX. Its MAE remains below <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\$}$</tex-math></inline-formula>0.006/MWh on the Coast and South zones, confirming robust generalization. These numerical results, along with the reported Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE), validate the performance gains delivered by the proposed model. BOL-LPP therefore promises markedly improved short-term load price forecasts, supporting informed decision-making and enhanced operational efficiency in the ERCOT market.