Title : The technological assessment of green buildings using artificial neural networks

1. Objectives
The primary objectives of this article are:
- Improving the accuracy of energy performance predictions: Utilizing Artificial Neural Networks (ANN) to predict energy consumption in green buildings with higher accuracy.
- Optimizing the use of natural resources: Enhancing the management of water, energy, and air quality in green buildings through data-driven decision-making.
- Reducing operational costs: Predicting the performance of building systems to minimize resource wastage and reduce maintenance costs.
- Improving indoor environmental quality: Using ANN models to monitor air quality and provide recommendations for ventilation and temperature control.

2. Strategies
The strategies employed in the study include:
- Data collection: Gathering real-world data on energy consumption, water usage, and indoor air quality.
- Data preprocessing: Preparing the collected data for analysis by reducing dimensionality and ensuring it is clean and consistent.
- Training ANN models: Using historical performance data from green buildings to train ANN models that can predict future performance.

3. Applications
The applications of ANN in green building assessment include:
- Energy consumption prediction: Using ANN to predict energy use in various building conditions, helping with energy management.
- Resource optimization: Predicting and optimizing the use of water, light, and other natural resources in green buildings.
- Air quality assessment: Monitoring and controlling indoor air quality using ANN models to ensure a healthier environment.
- Cost reduction: Minimizing operational costs by optimizing building management practices based on ANN predictions.

4. Models and Algorithms
The following ANN models are utilized in the study:
- Multilayer Perceptron (MLP): A feedforward ANN model used to predict building energy performance.
- Recurrent Neural Networks (RNN): Used for time-series data, such as predicting temperature and humidity fluctuations in buildings over time.
- Convolutional Neural Networks (CNN): Applied for spatial data, such as thermal images or floor plans, to analyze building energy efficiency.

5. Results
The results of the study include:
- Improved energy performance predictions: ANN models significantly improved the accuracy of predicting energy usage in green buildings.
- Reduced resource waste: ANN models helped in optimizing the use of energy, water, and air, leading to cost savings.
- Enhanced air quality management: The models successfully identified patterns in air quality and recommended effective solutions for ventilation and heating/cooling.

6. Challenges and Limitations
Some challenges and limitations faced in the study are:
- Data quality: The availability and quality of real-world data are crucial for training ANN models and ensuring accurate predictions.
- Implementation cost: Developing and deploying ANN models can be expensive and require technical expertise.
- Model updates: Continuous updates to the ANN models are required to keep up with changes in building operations and environmental factors.

7. Future Directions
Future research directions include:
- Exploring deep learning techniques: Investigating the use of Generative Adversarial Networks (GANs) to create synthetic data for model training.
- Hybrid models: Combining ANN with other optimization algorithms, such as genetic algorithms, to improve model predictions.
- Expanding to other sustainability metrics: Extending ANN models to assess additional sustainability factors such as life cycle analysis and supply chain management.