Project Abstract

The purpose of the project is to deal with the current issue of “energy utilization efficiency” and sustainability issues through data-driven intelligent energy-saving technologies. In modern society, the combination of the growing demand for power and the environmental problems and resource depletion requires a turning point in energy management and usage. The traditional way of handling energy management is that it is not accurate and futuristic, which leads to low efficiency, and waste of resources. This project has been stimulated by the identification of the enormous power of data and novel computing methods, which can play an increasingly important role in energy management. The aim of this project lies in the invention and implementation of data-driven approaches for minimizing energy consumption, enhancing operational carbon intensity, and promoting sustainability in the current energy management.1.Which of the current challenges is the most crucial in the area of power issue production?2.What implications do data-focused strategies that utilize the python programming language and machine learning algorithms have regarding the unfair distribution of welfare? 3.How would the regressive models be developed for data mining of previous energy consumption behavior and what methodologies can be used? Data-driven intelligent energy-efficiency strategy becomes the principal idea in the new energy management system which is being based on the hypothesis of technology and data analysis made to generate the significant effect. Through a Python-based tool utilization with machine learning algorithms for energy utilization optimization, cost reduction, and the control of environmental impact, organizations open up new ways. Their research work is proposed to apply data-driven techniques, which include mainly using Python programming language and machine learning models to implement the aim of saving energy. The report is focused on a thorough analysis of the energy consumption patterns of buildings and the usage of data-driven approaches for optimization of energy use and promotion of sustainable practices.

Keywords: Energy Utilization Efficiency, Sustainability, Data-Driven Technologies

 Conference Details

Session: Presentation Stream 29 at Presentation Slot 5

Location: GH011 at Wednesday 8th 13:30 – 17:00

Markers: Alex Warren (GTA), Deshan Sumanathilaka (GTA)

Course: MSc Advanced Computer Science, Masters PG

Future Plans: I’m undecided