LTPNet Integration of Deep Learning and Environmental Decision Support Systems for Renewable Energy Demand Forecasting

TL;DR

This paper presents a novel deep learning-based model integrating LSTM, Transformer, and PSO algorithms to improve renewable energy demand forecasting accuracy within environmental decision support systems, addressing traditional methods' limitations.

Contribution

It introduces a combined deep learning and optimization approach that significantly enhances forecasting accuracy and practical application in renewable energy demand prediction.

Findings

30% reduction in MAE

20% decrease in MAPE

25% drop in RMSE

Abstract

Against the backdrop of increasingly severe global environmental changes, accurately predicting and meeting renewable energy demands has become a key challenge for sustainable business development. Traditional energy demand forecasting methods often struggle with complex data processing and low prediction accuracy. To address these issues, this paper introduces a novel approach that combines deep learning techniques with environmental decision support systems. The model integrates advanced deep learning techniques, including LSTM and Transformer, and PSO algorithm for parameter optimization, significantly enhancing predictive performance and practical applicability. Results show that our model achieves substantial improvements across various metrics, including a 30% reduction in MAE, a 20% decrease in MAPE, a 25% drop in RMSE, and a 35% decline in MSE. These results validate the model's effectiveness and reliability in renewable energy demand forecasting. This research provides valuable insights for applying deep learning in environmental decision support systems.