# Cost-Oriented Load Forecasting

arXiv: https://arxiv.org/abs/2107.01861 · HTML: https://mlsanity.com/paper/2107.01861/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2021.

## Built on by (most cited first, 8 of 8)

- [Task-Aware Machine Unlearning and Its Application in Load Forecasting](https://mlsanity.com/paper/2308.14412/) (2023)
- [Decision-Focused Fine-Tuning of Time Series Foundation Models for Dispatchable Feeder Optimization](https://mlsanity.com/paper/2503.01936/) (2025)
- [Predict+Optimize Problem in Renewable Energy Scheduling](https://mlsanity.com/paper/2212.10723/) (2022)
- [Availability Adversarial Attack and Countermeasures for Deep Learning-based Load Forecasting](https://mlsanity.com/paper/2301.01832/) (2023)
- [Towards Improving Unit Commitment Economics: An Add-On Tailor for Renewable Energy and Reserve Predictions](https://mlsanity.com/paper/2208.13065/) (2022)
- [An Asymmetric Loss with Anomaly Detection LSTM Framework for Power Consumption Prediction](https://mlsanity.com/paper/2302.10889/) (2023)
- [Learning-Augmented Power System Operations: A Unified Optimization View](https://mlsanity.com/paper/2505.05203/) (2025)
- [Privacy-preserving Decision-focused Learning for Multi-energy Systems](https://mlsanity.com/paper/2510.21858/) (2025)

## Abstract (arXiv)

Accurate load prediction is an effective way to reduce power system operation costs. Traditionally, the mean square error (MSE) is a common-used loss function to guide the training of an accurate load forecasting model. However, the MSE loss function is unable to precisely reflect the real costs associated with forecasting errors because the cost caused by forecasting errors in the real power system is probably neither symmetric nor quadratic. To tackle this issue, this paper proposes a generalized cost-oriented load forecasting framework. Specifically, how to obtain a differentiable loss function that reflects real cost and how to integrate the loss function with regression models are studied. The economy and effectiveness of the proposed load forecasting method are verified by the case studies of an optimal dispatch problem that is built on the IEEE 30-bus system and the open load dataset from the Global Energy Forecasting Competition 2012 (GEFCom2012).