What question did this study set out to answer?

The study aims to develop an accurate prediction model for evaluating the effects of legal regulations on energy prices using LSTM neural networks.

April 16, 2026Open Access

The construction of energy price legal regulation effect prediction model based on LSTM neural network

Key Points

The study aims to develop an accurate prediction model for evaluating the effects of legal regulations on energy prices using LSTM neural networks.
Developed an LSTM-based prediction model for energy price regulations.
Utilized semantic feature extraction to analyze policy texts.
Created a spatiotemporal fusion framework for integrating multi-source data.
Implemented a dynamic regulation module to adjust to policy changes.
Reduced training-set MAE from 56.72 CNY/ton to 28.9 CNY with refined policies.
Observed a 45.6% decrease in price volatility after tiered pricing implementation.
Found that clearer policies improved model accuracy, reducing price trend capture error by 66.6%.

Abstract

As global energy markets grow more complex and volatile, existing forecasting accuracy and policy effect evaluations remain insufficient.An urgent need exists for a precise prediction model integrating legal policies and multi-dimensional energy market data.This paper proposes an LSTM-based model to predict legal regulation effects on energy prices: it uses semantic feature extraction for policy texts, builds a spatiotemporal fusion framework for multi-source heterogeneous data, and designs a hierarchical memory unit's dynamic regulation module to adapt to stage-specific policy adjustments.In historical data, the model's training-set MAE declined fluctuatingly: 56.72 CNY/ton standard coal (vague initial policies) dropped to 28.9 CNY (refined policies).After tiered pricing, policy-related price volatility fell to 45.6%.Policy clarity correlates positively with model accuracy; refinement reduced LSTM's price trend capture error by 66.6%.

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