Using Machine Learning and Deep Learning for Load disaggregation and Recognition of Activities in Household
Public Deposited- Resource Type
- Creator
- Abstract
Providing statistics about energy consumptions and accurate forecasts at appliances level can help energy clients and users to make informed decisions and reduce their electricity bills. This thesis presents a forecasting strategy that combines the benefits of load disaggregation, weather information, and ensemble learning to improve the accuracy of short-term load forecasts in a household. This thesis used combinatorial optimization to extract signature features of appliances from aggregated house energy consumption. The ensemble learning based on long short-term memory and the random forecast was designed to learn from the estimated energy consumption of appliances and predict the household energy consumption for three days ahead. The model was tested on a publicly available dataset, and the results showed that the load forecasting approach that considered the dynamic behaviors of home residents outperforms the accuracy of the benchmark forecasting methods that are trained with weather information or prior aggregated house power demand.
- Subject
- Language
- Publisher
- Thesis Degree Level
- Thesis Degree Name
- Thesis Degree Discipline
- Identifier
- Rights Notes
Copyright © 2020 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
- Date Created
- 2020
Relations
- In Collection:
Items
Thumbnail | Title | Date Uploaded | Visibility | Actions |
---|---|---|---|---|
bimenyimana-usingmachinelearninganddeeplearningforload.pdf | 2023-05-05 | Public | Download |