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Phase change material (PCM) in concrete structures significantly reduces energy consumption by 32%. This innovation aids carbon neutrality efforts and summer energy demand prediction.
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Area of Science:
- Materials Science
- Sustainable Construction
- Artificial Intelligence
Background:
- Efficient energy use is critical for carbon neutrality goals.
- Phase Change Materials (PCMs) are being investigated for integration into concrete structures.
- Reducing energy consumption in buildings is a key sustainability challenge.
Purpose of the Study:
- To simulate and predict the energy consumption of concrete structures incorporating Phase Change Materials (PCMs).
- To evaluate the effectiveness of PCM-enhanced concrete in reducing overall energy usage.
- To develop an accurate predictive model for energy consumption in buildings.
Main Methods:
- Energy consumption simulations were conducted using data from concrete mock-up structures.
- A Bayesian approach was employed for threshold investigation.
- A recurrent neural network (RNN) was developed by integrating modularized spiking neural network components to predict energy consumption.
- High-accuracy data prediction was achieved with an R² value of 0.95 or higher.
Main Results:
- The developed RNN model demonstrated high accuracy in predicting energy consumption, achieving an R² value of 0.95+.
- Concrete structures containing PCM exhibited a significant reduction in energy consumption compared to normal concrete.
- PCM-containing concrete demonstrated up to 32% less energy consumption than conventional concrete.
Conclusions:
- The integration of Phase Change Materials (PCMs) into concrete structures is a viable strategy for substantial energy savings.
- The developed AI-driven simulation approach provides a reliable method for predicting energy consumption, particularly during summer months.
- This research offers a promising solution for reducing the carbon footprint of the construction industry and improving building energy efficiency.