Environmental policy-driven electricity consumption prediction: A novel buffer-corrected Hausdorff fractional grey...

Area of Science:

• Energy Economics and Policy
• Environmental Science
• Computational Intelligence

Background:

• Electricity consumption is intrinsically linked to carbon emissions, making accurate forecasting vital for carbon neutrality goals.
• Long-term electricity consumption prediction is foundational for power system planning and urban development, yet has been less explored than short-term forecasting.
• Existing forecasting models may struggle with external shocks and parameter optimization, necessitating advanced approaches.

Purpose of the Study:

• To develop and validate an innovative hybrid Hausdorff fractional grey model (HfGM) for long-term electricity consumption prediction.
• To enhance the HfGM by incorporating a weakening buffer operator (WBo) and optimizing its parameters using a novel multi-objective slime mould algorithm.
• To apply the validated model to forecast China's electricity consumption during the 15th Five-Year Plan and assess environmental impacts.

Main Methods:

• Development of a hybrid Hausdorff fractional grey model (HfGM) integrating a weakening buffer operator (WBo) to mitigate data interference.
• Implementation of a two-stage, multi-objective enhanced slime mould algorithm for optimizing HfGM core parameters, aiming for Pareto optimality.
• Empirical validation of the proposed HfGM against comparative models using electricity consumption data.

Main Results:

• The proposed HfGM demonstrated superior performance compared to existing models in electricity consumption prediction.
• The model was successfully applied to forecast long-term electricity consumption trends in China.
• Environmental impacts associated with the predicted electricity consumption were assessed.

Conclusions:

• The developed HfGM offers a robust and accurate method for long-term electricity consumption forecasting.
• The research provides critical insights for integrating environmental management into electricity demand planning to support carbon reduction goals.
• Policy recommendations are offered to assist authorities in managing demand growth and achieving sustainability targets.