The swelling energy demands and efficient management of resources call for accurate energy use forecasting.
This project, therefore, involves the development of a tool for the prediction of energy consumption for historical energy usage data and exogenous variables such as weather conditions. The energy provider will be aided by the forecasting tool characterized by time series analysis and machine learning models to predict the quantity of energy consumed in the future with great accuracy and hence enhance operations by handling the demand and supply of energy before time, which minimizes operational costs.
Week 1-2
Initial Planning and Requirement Analysis
Define project objectives, scope, and high-level requirements.
Gather data and resources.
Week 3-4
Data Collection and Preprocessing Phase
Collecting and preprocessing historic energy usage data along with relevant exogenous factors.
Trend analysis and pattern analysis are developed in this phase.
Week 5-6
Model Development Phase
Develop and train appropriate Time Series Analysis and Machine Learning models for Energy Consumption Forecasting.
Validate the accuracy and performance of the model against historical data.
Week 7-8
System Integration and Testing Phase
Implement an energy-use forecasting tool
Integrate it with the existing energy management system
Testing and refinement of the tool according to the performance metrics and user feedback.
Week 9-10
Deployment Phase
The inbuilt forecasting tools will be deployed into the environment where the energy is managed by the concerned energy management authority.
Tracking of tools performance and user activities.
Implement continuous improvement based on analytics and user feedback.
Week 11-12
Final Evaluation and Reporting Phase
Final evaluations and validation of the developed forecasting tool.
Compilation of final project report and documentation.
Individual report presentations by students.
