Energy Consumption Prediction in Buildings Using Ensemble Methods

Building usage needs to drastically change in light of the current state of the world. The intention behind this project is to develop a predictive model of energy consumption to facilitate more economical and eco-friendly decision-making. To guarantee the dependability and scalability of this predictive model, it will be trained on a large dataset. Based on information deduced from multiple buildings, including occupancy, weather, and daylight hours, it will be implemented. The paper's scope encompasses gathering data, preprocessing anticipated that this initiative will have two main effects: first, stakeholders can optimize their consumption, which will lower their energy costs and their carbon footprint. Furthermore, the study's findings will elucidate the intricate correlation among building architecture, environmental elements, and energy usage. Our objective is to adopt data analytics and artificial intelligence to improve energy efficiency, encourage sustainable practices, and pave the way for a more sustainable future.