Physics-Enhanced ML for time series forecasting in air pollution

Motivation

TBA

Tasks and Timeline

Stage 1:

• Define the forecasting problem for building loads;
• Delimitate the solution concept within deep learning methods;
• Review the state-of-the-art and proposed solutions in the literature;
• Brainstorm potential deep learning-based solutions for the problem.

Stage 2:

• Plan the experiments and test for the expected solution;
• Describe the dataset;
• Develop a pre-processing data pipeline;
• Select meaningful features from the dataset;
• Design a proof-of-concept for potential solutions via minimum viable product concept;
• Analyze the proposed proof-of-concept.

Stage 3:

• Evaluate the proof-of-concept with the proposed experiments and tests;
• Benchmark the proof-of-concept against other solutions in the literature.

Stage 4:

• Document the problem description and solution-design methodology;
• Show the results to reviewers and non-technical audiences.

Expected Outcomes

Mandatory Products

• Simulation of building loads, grid carbon intensity and solar generation;
• Mathematical analysis of the proposed algorithm;
• Software with documentation associated with the project;
• Technical report with problem description, proposed solutions, experimental results, and project conclusions by following the University guidelines;
• A public dissertation following the University guidelines.

Optional Products

• Summary paper from the technical report suitable for conferences or journals;
• 3-minute elevator pitch video of the project;
• Blog post or video explaining the problem and proposed solution for a general audience.

Bibliography

• M. Neukomm, V. Nubbe, and R. Fares, “Grid-Interactive Efficient Buildings Technical Report Series: Overview of Research Challenges and Gaps,” NREL/TP-5500-75470, DOE/GO-102019-5227, 1577966, Dec. 2019. doi: 10.2172/1577966.
• T. Brudermüller and M. Kreft, “Smart Meter Data Analytics: Practical Use-Cases and Best Practices of Machine Learning Applications for Energy Data in the Residential Sector,” presented at the ICLR 2023 Workshop: Tackling Climate Change with Machine Learning, Kigali, Rwanda, 2023.
• K. Nweye, A. Wu, H. Park, Y. Almilaify, and Z. Nagy, “CityLearn: A Tutorial on Reinforcement Learning Control for Grid-Interactive Efficient Buildings and Communities,” presented at the ICLR 2023 Workshop: Tackling Climate Change with Machine Learning, Kigali, Rwanda, 2023.