Summary of Triz Method For Urban Building Energy Optimization: Gwo-sarima-lstm Forecasting Model, by Shirong Zheng et al.
TRIZ Method for Urban Building Energy Optimization: GWO-SARIMA-LSTM Forecasting model
by Shirong Zheng, Shaobo Liu, Zhenhong Zhang, Dian Gu, Chunqiu Xia, Huadong Pang, Enock Mintah Ampaw
First submitted to arxiv on: 20 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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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 The proposed hybrid deep learning model combines TRIZ innovation theory with GWO, SARIMA, and LSTM to improve building energy consumption prediction accuracy. This model tackles the limitations of traditional methods by considering complex energy patterns, seasonal fluctuations, and dynamic changes. The TRIZ component plays a key role in model design, providing innovative solutions for balancing energy efficiency, cost, and comfort. GWO optimizes the model’s parameters to ensure high accuracy under varying conditions. SARIMA captures seasonal trends, while LSTM handles short-term and long-term dependencies. This robust model demonstrates a 15% reduction in prediction error compared to existing models. The approach enhances urban energy management and provides a new framework for optimizing energy use and reducing carbon emissions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a better way to predict how much energy buildings will use, which is important for making our cities more sustainable. Traditional methods don’t always work well because they can’t handle changes in the weather or other complex patterns. The researchers created a new model that combines different techniques to make predictions more accurate. This model uses a method called TRIZ to find creative solutions and balances things like energy efficiency, cost, and comfort. It also uses other techniques like GWO, SARIMA, and LSTM to handle seasonal changes and short-term dependencies. The result is a 15% reduction in prediction error compared to other models. |
Keywords
» Artificial intelligence » Deep learning » Lstm
