Development of a Machine-Learning-Based Recommender System for Optimizing Household Energy Consumption
Development of a Machine-Learning-Based Recommender System for Optimizing Household Energy Consumption
Requirements
- M.Sc. in Machine Learning, Data Science, Computer Science, Mathematics, Telecommunications, or similar
- Knowledge of Python
- Software development skills
- Basic concepts of time series processing
- Basic concepts of data science, concerning data analysis, processing and machine learning
Description
In the contemporary world, the significance of optimizing energy consumption has escalated due to the rising costs of energy, the urgent need to mitigate carbon emissions, and the global push towards sustainable living. Households, as critical units of energy consumption, present a vast potential for energy optimization through the intelligent scheduling of home appliance activities. This internship proposal outlines a project aimed at developing a state-of-the-art machine-learning-based recommender system designed to optimize household energy consumption. This system will focus on scheduling the operation of various home appliances to achieve objectives such as cost reduction, CO2 emissions minimization, and maximization of renewable energy usage. The internship aims at developing a machine-learning-based recommender system tailored for optimizing overall household energy consumption. This system aims to intelligently schedule the activities of various home appliances based on different optimization objective functions, such as reducing the cost, reducing the CO2 emissions or maximizing the renewable energy usage. The project will be carried out in the following phases:
- 1: Research and Data Collection Conduct a thorough literature review to understand current technologies and methodologies in energy consumption optimization and collect and analyze historical data on household energy consumption, including appliance usage patterns, user behaviors, and preferences.
- 2: System Design and Development Design the architecture of the recommender system, including data preprocessing, optimization algorithm development, and machine learning model integration. Develop optimization algorithms focusing on the stated objectives. Implement predictive models using machine learning to forecast energy consumption patterns and appliance usage.
- 3: System Testing and Evaluation Deploy the system in a controlled environment to test its functionality and efficiency.
An example of expected output can be found here. The proposed project will extend this foundation by focusing on the latest machine learning techniques and optimization algorithms to achieve even more efficient and user-friendly energy consumption optimization.