Climate change will affect us all, and we have to do everything we can to minimize the magnitude of change. Investments in renewable generation help to reduce the impact of energy usage on the supply side, but that will not get us all the way there, especially in the near term. Consumers will also have to become much more efficient with their energy use.
To become an efficient user of electricity, consumers need to know where their electricity usage comes from. Unfortunately, our current monthly electricity bill does not accurately reflect what drives our usage. What’s needed are machine learning techniques to derive detailed usage information from aggregate electricity measurements taken by smart meters, a process known as non-intrusive load monitoring (NILM).
In this project, you will study and improve machine learning techniques - including Hidden Markov Models (HMMs), Long Short-Term Memory (LSTM) deep learning networks and/or applications of the Bayesian information-theoretic Minimum Message Length (MML) principle - to model consumer electricity usage. We intend to improve accuracy by integrating additional features of known patterns of use (such as time-of-day associated with activities) and/or characteristic load patterns (such as transient load decay associated with refrigerator cooling cycles).
- Wallace, C.S. (2005), Statistical and Inductive Inference by Minimum Message Length, Springer (Series: Information Science and Statistics), 2005, XVI, 432 pp. [and preface - and p vi, also here]
- Wallace, C.S. and D.L. Dowe (1999a). “Minimum Message Length and Kolmogorov Complexity, Computer Journal” (special issue on Kolmogorov complexity), Vol. 42, No. 4, pp270-283
- Wallace, C.S. and D.L. Dowe (2000). “MML clustering of multi-state, Poisson, von Mises circular and Gaussian distributions”, Statistics and Computing, Vol. 10, No. 1, Jan. 2000, pp73-83
- Wong, Yung Fei, Lachlan LH Andrew, and Y. Ahmet Sekercioglu. "Hidden semi-Markov models for electricity load disaggregation." ACM SIGMETRICS Performance Evaluation Review 46.3 (2019): 18-23. https://dl.acm.org/doi/abs/10.1145/3308897.3308906
- Non-intrusive load monitoring toolkit: https://github.com/nilmtk/nilmtk
Candidates should have a strong computer science background with good programming skills, particularly in Java, Python and/or MATLAB. A good understanding of probability theory is a big plus, as is knowledge of at least one of mathematics, statistics and/or machine learning principles.