My PhD student Dilini Sewwandi Rajapaksha and I made first place in the FUZZ-IEEE Explainable Energy Prediction Competition. The winners were announced at the IEEE International Conference on Fuzzy Systems, Luxembourg, 2021.
We proposed a novel algorithm to provide Local Interpretable Model-agnostic Rule-based Explanations for Forecasting, a paper and more descriptions will be available hopefully soon.
We’ve just launched the IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling, with US$20k in prize money. You’ll need to forecast energy demand and solar power production for a couple of buildings on the Monash Clayton campus, and then use the forecasts to optimally schedule lectures and battery charging/discharging. We’re looking forward to your great submissions!
More information is here
- Dilini Rajapaksha, Christoph Bergmeir (2022) LIMREF: Local Interpretable Model Agnostic Rule-based Explanations for Forecasting, with an Application to Eletricity Smart Meter Data. In: Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22). Abstract bib
- Seyedali Meghdadi, Guido Tack, Ariel Liebman, Nicolas Langrene, Christoph Bergmeir (2021) Versatile and Robust Transient Stability Assessment via Instance Transfer Learning. In: Working Paper. Abstract pdf bib
- Chang Wei Tan, Christoph Bergmeir, Francois Petitjean, Geoffrey I Webb (2021) Time series extrinsic regression. In: Data Mining and Knowledge Discovery, 35, (3), pp. 1032-1060. Abstract pdf bib
- Dilini Rajapaksha, Chakkrit Tantithamthavorn, Christoph Bergmeir, Wray Buntine, Jirayus Jiarpakdee, John Grundy (2021) SQAPlanner: Generating data-informed software quality improvement plans. In: IEEE Transactions on Software Engineering. Abstract pdf bib
- Hansika Hewamalage, Christoph Bergmeir, Kasun Bandara (2021) Recurrent neural networks for time series forecasting: Current status and future directions. In: International Journal of Forecasting, 37, (1), pp. 388-427. Abstract pdf bib