Harnessing Multidimensional Insights and Advanced Machine Learning for Optimized Energy Efficiency: Revolutionizing Sustainable Systems through Predictive Optimization, Ensemble Learning and IoT Integration for Enhanced Heating and Cooling Load Management

Abstract

Integrating Multidimensional Insights for Enhanced Feature Selection in Energy Transition Models presents a comprehensive approach to enhancing the energy efficiency of sustainable energy systems. The purpose of this research is to find the categorical features that can be boosted with ensemble learning for finding most relevant aspect in energy generation. The study leverages sophisticated machine learning techniques, including deep learning and ensemble methods, to improve the prediction and optimization of heating and cooling loads in systems using application of Advanced Machine Learning Algorithms. In this research article, we are trying to focus on critical energy consumption areas like heating and cooling. These are crucial aspects of building energy consumption, and the study's emphasis on these areas demonstrates an understanding of key factors in energy efficiency. This research represents a significant step forward in applying machine learning to sustainable design and energy savings. It underscores the potential of machine learning in transforming the way systems are designed and operated for better energy efficiency. Understanding the application of machine learning algorithms to cross-domain optimization, such as integrating building energy systems with electric vehicles and smart grid technologies, can create synergies that enhance overall energy efficiency. This holistic approach can lead to more significant energy savings by optimizing across multiple domains simultaneously. We also focus on improving the scalability and generalization capabilities of machine learning models to ensure they can be effectively applied across different types of buildings and geographic locations. It involves developing models that can adapt to diverse conditions without retraining. It enhances collaboration with IoT Devices and strengthening the collaboration between machine learning systems and IoT (Internet of Things) devices can enhance the granularity and precision of energy management. IoT devices can provide detailed, real-time data, which, when analyzed by advanced machine learning algorithms, can lead to more nuanced and effective energy-saving. The model is performing reasonably well, with the ability to predict values that correlate with the actual data. Feature Y1 is by far the most predictive of the model's output, which could mean that focusing on this feature could lead to improvements in the model's performance. The accuracy of our model is near 97% with further scope to improve with ensemble learning and XG boosting.