In this paper we evaluate several well-known and widely used machine learning algorithms for regression in the energy disaggregation task. Specifically, the Non-Intrusive Load Monitoring approach was considered and the K-Nearest-Neighbours, Support Vector Machines, Deep Neural Networks and Random Forest algorithms were evaluated across five datasets using seven different sets of statistical and electrical features. The experimental results demonstrated the importance of selecting both appropriate features and regression algorithms. The best performance in terms of energy disaggregation accuracy was achieved by the Random Forest regression algorithm.
|Name||10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019|
|Conference||10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019|
|Period||15/07/19 → 17/07/19|
- Energy disaggregation
- Feature selection
- Non-Intrusive Load Monitoring (NILM)