# Predict+Optimize Problem in Renewable Energy Scheduling

arXiv: https://arxiv.org/abs/2212.10723 · HTML: https://mlsanity.com/paper/2212.10723/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 75 references that are in the map.
- First posted on arXiv 21 Dec 2022.

## Builds on (most cited first, 17 of 17)

- [Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon](https://mlsanity.com/paper/1811.06128/) (2018)
- [OptNet: Differentiable Optimization as a Layer in Neural Networks](https://mlsanity.com/paper/1703.00443/) (2017)
- [Data-Efficient Hierarchical Reinforcement Learning](https://mlsanity.com/paper/1805.08296/) (2018)
- [A Transformer-based Framework for Multivariate Time Series Representation Learning](https://mlsanity.com/paper/2010.02803/) (2020)
- [Smart "Predict, then Optimize"](https://mlsanity.com/paper/1710.08005/) (2017)
- [End-to-End Constrained Optimization Learning: A Survey](https://mlsanity.com/paper/2103.16378/) (2021)
- [Smart Predict-and-Optimize for Hard Combinatorial Optimization Problems](https://mlsanity.com/paper/1911.10092/) (2019)
- [Forecast Evaluation for Data Scientists: Common Pitfalls and Best Practices](https://mlsanity.com/paper/2203.10716/) (2022)
- [Decision Trees for Decision-Making under the Predict-then-Optimize Framework](https://mlsanity.com/paper/2003.00360/) (2020)
- [LfEdNet: A Task-based Day-ahead Load Forecasting Model for Stochastic Economic Dispatch](https://mlsanity.com/paper/2008.07025/) (2020)
- [Cost-Oriented Load Forecasting](https://mlsanity.com/paper/2107.01861/) (2021)
- [State-of-the-art predictive and prescriptive analytics for IEEE CIS 3rd Technical Challenge](https://mlsanity.com/paper/2112.03595/) (2021)
- [Feature-driven Improvement of Renewable Energy Forecasting and Trading](https://mlsanity.com/paper/1907.07580/) (2019)
- [Methodology for forecasting and optimization in IEEE-CIS 3rd Technical Challenge](https://mlsanity.com/paper/2202.00894/) (2022)
- [Evolutionary scheduling of university activities based on consumption forecasts to minimise electricity costs](https://mlsanity.com/paper/2202.12595/) (2022)
- [How to predict and optimise with asymmetric error metrics](https://mlsanity.com/paper/2211.13586/) (2022)
- [Optimal activity and battery scheduling algorithm using load and solar generation forecasts](https://mlsanity.com/paper/2210.12990/) (2022)

## Built on by (most cited first, 3 of 3)

- [Decision-Focused Learning Enhanced by Automated Feature Engineering for Energy Storage Optimisation](https://mlsanity.com/paper/2509.05772/) (2025)
- [The Energy Prediction Smart-Meter Dataset: Analysis of Previous Competitions and Beyond](https://mlsanity.com/paper/2311.04007/) (2023)
- [Decision-focused learning for optimal PV-Battery scheduling](https://mlsanity.com/paper/2605.28340/) (2026)

## Abstract (arXiv)

Predict+Optimize frameworks integrate forecasting and optimization to address real-world challenges such as renewable energy scheduling, where variability and uncertainty are critical factors. This paper benchmarks solutions from the IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling, focusing on forecasting renewable production and demand and optimizing energy cost. The competition attracted 49 participants in total. The top-ranked method employed stochastic optimization using LightGBM ensembles, and achieved at least a 2% reduction in energy costs compared to deterministic approaches, demonstrating that the most accurate point forecast does not necessarily guarantee the best performance in downstream optimization. The published data and problem setting establish a benchmark for further research into integrated forecasting-optimization methods for energy systems, highlighting the importance of considering forecast uncertainty in optimization models to achieve cost-effective and reliable energy management. The novelty of this work lies in its comprehensive evaluation of Predict+Optimize methodologies applied to a real-world renewable energy scheduling problem, providing insights into the scalability, generalizability, and effectiveness of the proposed solutions. Potential applications extend beyond energy systems to any domain requiring integrated forecasting and optimization, such as supply chain management, transportation planning, and financial portfolio optimization.