Summary of Ltpnet Integration Of Deep Learning and Environmental Decision Support Systems For Renewable Energy Demand Forecasting, by Te Li and Mengze Zhang and Yan Zhou
LTPNet Integration of Deep Learning and Environmental Decision Support Systems for Renewable Energy Demand Forecasting
by Te Li, Mengze Zhang, Yan Zhou
First submitted to arxiv on: 20 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: General Economics (econ.GN)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach that combines deep learning techniques with environmental decision support systems to improve renewable energy demand forecasting. The model integrates Long Short-Term Memory (LSTM) and Transformer architectures, along with the Particle Swarm Optimization (PSO) algorithm for parameter optimization. This hybrid approach significantly enhances predictive performance and practical applicability, achieving substantial improvements in metrics such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and Mean Squared Error (MSE). The results demonstrate the model’s effectiveness and reliability in predicting renewable energy demands. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a better way to predict how much energy we need from renewable sources, like solar and wind power. Right now, predicting this is hard because traditional methods struggle with complex data and aren’t very accurate. To fix this, the authors use special computer programs called deep learning techniques that can learn from big amounts of data. They combine these programs with a system that helps make decisions about the environment. The new approach works really well and makes more accurate predictions. |
Keywords
» Artificial intelligence » Deep learning » Lstm » Mae » Mse » Optimization » Transformer
