State-of-the-art predictive and prescriptive analytics for IEEE CIS 3rd Technical Challenge
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Abstract (arXiv)
In this paper, we describe our proposed methodology to approach the predict+optimise challenge introduced in the IEEE CIS 3rd Technical Challenge. The predictive model employs an ensemble of LightGBM models and the prescriptive analysis employs mathematical optimisation to efficiently prescribe solutions that minimise the average cost over multiple scenarios. Our solutions ranked 1st in the optimisation and 2nd in the prediction challenge of the competition.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 0 of its 6 references that are in the map. First posted on arXiv 7 Dec 2021.
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Citing papers per year: 2022: 5; 2023: 2; 2024: 1.
8 papers.
- 2022Forecast Evaluation for Data Scientists: Common Pitfalls and Best Practicescited by 55
- 2022Predict+Optimize Problem in Renewable Energy Schedulingcited by 3
- 2022Evolutionary scheduling of university activities based on consumption forecasts to minimise electricity costscited by 2
- 2022How to predict and optimise with asymmetric error metricscited by 2
- 2023Improving the forecast accuracy of wind power by leveraging multiple hierarchical structurecited by 1
- 2022The intersection of machine learning with forecasting and optimisation: theory and applicationscited by 0
- 2023Approximating Solutions to the Knapsack Problem using the Lagrangian Dual Frameworkcited by 0
- 2024Digital Twins for forecasting and decision optimisation with machine learning: applications in wastewater treatmentcited by 0
- 2024Digital Twins for forecasting and decision optimisation with machine learning: applications in wastewater treatment23 Apr 2024
- 2023Approximating Solutions to the Knapsack Problem using the Lagrangian Dual Framework6 Dec 2023
- 2023Improving the forecast accuracy of wind power by leveraging multiple hierarchical structure7 Aug 2023
- 2022Predict+Optimize Problem in Renewable Energy Scheduling21 Dec 2022
- 2022How to predict and optimise with asymmetric error metrics24 Nov 2022
- 2022The intersection of machine learning with forecasting and optimisation: theory and applications24 Nov 2022
- 2022Forecast Evaluation for Data Scientists: Common Pitfalls and Best Practices21 Mar 2022
- 2022Evolutionary scheduling of university activities based on consumption forecasts to minimise electricity costs25 Feb 2022