Machine Learning Approaches to Reduce Electrical Waste and Improve Power Grid Stability

Machine Learning Approaches to Reduce Electrical Waste and Improve Power Grid Stability

Mark Valovage

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Doctoral Consortium. Pages 5791-5792. https://doi.org/10.24963/ijcai.2018/839

My research contributions are summarized as follows: In electricity disaggregation, I introduced the first label correction approach for supervised training samples. For unsupervised disaggregation, I introduced event detection that does not require parameter tuning and appliance discovery that makes no assumptions on appliance types. These improvements produce better accuracy, faster computation, and more scalability than any previously introduced method and can be applied to natural gas disaggregation, water disaggregation, and other source separation domains. My current work challenges long-held assumptions in time series shapelets, a classification tool with applicability in electrical time series and dozens of additional domains.
Keywords:
Machine Learning: Classification
Machine Learning: Data Mining
Machine Learning: Feature Selection/Construction
Machine Learning: Time-series/Data Streams