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LSTM networks enhance electricity price prediction with adaptive learning

By PulseAugur Editorial · [1 sources] ·

Researchers have developed an adaptive online learning framework using Long Short-Term Memory (LSTM) networks to improve the accuracy of day-ahead electricity price predictions in the California energy market. The model incorporates historical prices, weather data, and energy generation mix, enhanced by a novel custom loss function combining Mean Absolute Error (MAE), Jensen-Shannon Divergence (JSD), and a smoothness penalty. This adaptive approach demonstrated superior performance over static models, reducing Mean Squared Error (MSE), MAE, and Root Mean Square Error (RMSE) by significant margins. AI

IMPACT This research offers a more robust framework for electricity price forecasting, potentially leading to better decision-making in dynamic energy markets.

RANK_REASON Academic paper detailing a new machine learning model and framework. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LSTM networks enhance electricity price prediction with adaptive learning

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Academic paper detailing a new machine learning model and framework. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Salih Salihoglu, Ibrahim Ahmed, Afshin Asadi ·

    Adaptive Online Learning with LSTM Networks for Energy Price Prediction

    arXiv:2510.16898v2 Announce Type: replace-cross Abstract: Accurate prediction of electricity prices is crucial for stakeholders in the energy market, particularly for grid operators, energy producers, and consumers. This study focuses on developing a predictive model leveraging L…

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