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

arXiv: https://arxiv.org/abs/2410.15286 · HTML: https://mlsanity.com/paper/2410.15286/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 81 references that are in the map.
- First posted on arXiv 20 Oct 2024.

## Builds on (most cited first, 27 of 27)

- [LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inference](https://mlsanity.com/paper/2309.14331/) (2023)
- [Harnessing XGBoost for Robust Biomarker Selection of Obsessive-Compulsive Disorder (OCD) from Adolescent Brain Cognitive Development (ABCD) data](https://mlsanity.com/paper/2407.00028/) (2024)
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- [Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective](https://mlsanity.com/paper/2312.01397/) (2023)
- [Deep Learning-based Anomaly Detection and Log Analysis for Computer Networks](https://mlsanity.com/paper/2407.05639/) (2024)
- [Accel-GCN: High-Performance GPU Accelerator Design for Graph Convolution Networks](https://mlsanity.com/paper/2308.11825/) (2023)
- [Automatic News Generation and Fact-Checking System Based on Language Processing](https://mlsanity.com/paper/2405.10492/) (2024)
- [Theoretical Analysis of Meta Reinforcement Learning: Generalization Bounds and Convergence Guarantees](https://mlsanity.com/paper/2405.13290/) (2024)
- [Optimizing Automated Picking Systems in Warehouse Robots Using Machine Learning](https://mlsanity.com/paper/2408.16633/) (2024)
- [Image anomaly detection and prediction scheme based on SSA optimized ResNet50-BiGRU model](https://mlsanity.com/paper/2406.13987/) (2024)
- [Robust Domain Generalization for Multi-modal Object Recognition](https://mlsanity.com/paper/2408.05831/) (2024)
- [Identification of Prognostic Biomarkers for Stage III Non-Small Cell Lung Carcinoma in Female Nonsmokers Using Machine Learning](https://mlsanity.com/paper/2408.16068/) (2024)
- [Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments](https://mlsanity.com/paper/2409.14972/) (2024)
- [Enhancing Visual Question Answering through Ranking-Based Hybrid Training and Multimodal Fusion](https://mlsanity.com/paper/2408.07303/) (2024)
- [AdaPI: Facilitating DNN Model Adaptivity for Efficient Private Inference in Edge Computing](https://mlsanity.com/paper/2407.05633/) (2024)
- [The Design of Autonomous UAV Prototypes for Inspecting Tunnel Construction Environment](https://mlsanity.com/paper/2408.07286/) (2024)
- [Few-shot Name Entity Recognition on StackOverflow](https://mlsanity.com/paper/2404.09405/) (2024)
- [Wind Power Forecasting Considering Data Privacy Protection: A Federated Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2211.02674/) (2022)
- [Cross-border Commodity Pricing Strategy Optimization via Mixed Neural Network for Time Series Analysis](https://mlsanity.com/paper/2408.12115/) (2024)
- [Distributed Deep Reinforcement Learning for Intelligent Load Scheduling in Residential Smart Grids](https://mlsanity.com/paper/2006.16100/) (2020)
- [Mix of Experts Language Model for Named Entity Recognition](https://mlsanity.com/paper/2404.19192/) (2024)
- [Deep Analysis of Time Series Data for Smart Grid Startup Strategies: A Transformer-LSTM-PSO Model Approach](https://mlsanity.com/paper/2408.12129/) (2024)
- [Graphical Structural Learning of rs-fMRI data in Heavy Smokers](https://mlsanity.com/paper/2409.08395/) (2024)
- [Fleet Rebalancing for Expanding Shared e-Mobility Systems: A Multi-agent Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2211.06136/) (2022)
- [Recording Brain Activity While Listening to Music Using Wearable EEG Devices Combined with Bidirectional Long Short-Term Memory Networks](https://mlsanity.com/paper/2408.12124/) (2024)
- [EITNet: An IoT-Enhanced Framework for Real-Time Basketball Action Recognition](https://mlsanity.com/paper/2410.09954/) (2024)
- [Real-time Monitoring of Lower Limb Movement Resistance Based on Deep Learning](https://mlsanity.com/paper/2410.19769/) (2024)

## Built on by (most cited first, 7 of 7)

- [IoT-Based 3D Pose Estimation and Motion Optimization for Athletes: Application of C3D and OpenPose](https://mlsanity.com/paper/2411.12676/) (2024)
- [Optimized CNNs for Rapid 3D Point Cloud Object Recognition](https://mlsanity.com/paper/2412.02855/) (2024)
- [Intelligent logistics management robot path planning algorithm integrating transformer and GCN network](https://mlsanity.com/paper/2501.02749/) (2025)
- [CPLOYO: A Pulmonary Nodule Detection Model with Multi-Scale Feature Fusion and Nonlinear Feature Learning](https://mlsanity.com/paper/2503.10045/) (2025)
- [MamNet: A Novel Hybrid Model for Time-Series Forecasting and Frequency Pattern Analysis in Network Traffic](https://mlsanity.com/paper/2507.00304/) (2025)
- [Research on Short-Video Platform User Decision-Making via Multimodal Temporal Modeling and Reinforcement Learning](https://mlsanity.com/paper/2509.12269/) (2025)
- [DT-NeRF: A Diffusion and Transformer-Based Optimization Approach for Neural Radiance Fields in 3D Reconstruction](https://mlsanity.com/paper/2509.17232/) (2025)

## Abstract (arXiv)

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.