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Project # 3
Household Energy Consumption Prediction Using Machine Learning
| Team Lead: | Karan Rajendra Saraswat |
| Other Team Members: | |
| Faculty Advisor(s): | Dr. Aera LeBoulluec |
| Department: | IMSE |
| Project Type: | Graduate Student |
| Title: | Household Energy Consumption Prediction Using Machine Learning |
| Abstract: | This project aims to develop an accurate predictive model for household energy consumption using a comprehensive dataset from Dataport. The dataset encompasses electricity usage records for 120 houses, collected at 15-minute intervals over a two-year period, providing a rich source of information for analysis and prediction. The primary objective is to create a machine learning model that can forecast energy consumption based on the specific equipment present in a household. This approach will enable more precise energy management and potentially lead to more efficient use of resources. The methodology involves preprocessing the extensive time-series data, extracting relevant features, and applying various machine learning algorithms. Models under consideration include Random Forest, Gradient Boosting, and Neural Networks. Each model will be trained, validated, and tested using the dataset, with performance evaluated using metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The project will compare the effectiveness of different models, ultimately selecting the one that demonstrates the highest accuracy and reliability in predicting household energy consumption. The results of this study could have significant implications for energy conservation efforts, smart home technologies, and personalized energy management strategies. |
| Poster: | View Poster #3 (PDF) |
| Judges Scoring: | Submit Evaluation (judges only) |