Kernekoncepter
Factors influencing energy efficiency and cost reduction in buildings are analyzed using advanced regression models and metaheuristic techniques.
Resumé
The content explores the importance of energy efficiency in buildings, focusing on factors affecting consumption patterns. It delves into the use of machine learning algorithms to predict energy use accurately. The study emphasizes the significance of financial aspects, utility information, building characteristics, and customer data in optimizing resource use for sustainable development.
Structure:
- Introduction:
- Global trends in energy consumption.
- Importance of energy conservation.
- Literature Review:
- Overview of studies on building energy consumption prediction.
- Methodology:
- CRISP-DM methodology phases explained.
- Modeling:
- Data preprocessing, hyperparameter tuning, model training, evaluation, and comparison.
- Evaluation:
- Comparison of models based on AIC criteria for different target variables.
- Optimization:
- Improving Decision Tree algorithm performance using Genetic Algorithm.
- Decision Tree Analysis:
- Key findings from decision tree analysis for different target variables.
- Conclusion:
- Summary of influential factors on energy consumption and costs.
Statistik
In China, building energy consumption accounted for 28% in 2011.
Residential and commercial buildings account for 32% of final energy consumption according to IEA.
Citater
"Buildings must adopt energy-efficient practices to mitigate global energy consumption."
"Machine learning models offer practical approaches to predict building energy usage."