# LfEdNet: A Task-based Day-ahead Load Forecasting Model for Stochastic Economic Dispatch

arXiv: https://arxiv.org/abs/2008.07025 · HTML: https://mlsanity.com/paper/2008.07025/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2020.

## Built on by (most cited first, 11 of 11)

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- [Privacy-preserving Decision-focused Learning for Multi-energy Systems](https://mlsanity.com/paper/2510.21858/) (2025)
- [Decision-Focused Continual Learning for Seaport Power-Logistics Scheduling: Generalization across Varying Tasks](https://mlsanity.com/paper/2511.07938/) (2025)
- [M2OE2-GL: A Family of Probabilistic Load Forecasters That Scales to Massive Customers](https://mlsanity.com/paper/2511.17623/) (2025)
- [Decision-focused learning for optimal PV-Battery scheduling](https://mlsanity.com/paper/2605.28340/) (2026)

## Abstract (arXiv)

Load forecasting is one of the most important and studied topics in modern power systems. Most of the existing researches on day-ahead load forecasting try to build a good model to improve the forecasting accuracy. The forecasted load is then used as the input to generation scheduling with the ultimate goal of minimizing the cost of generation schedules. However, existing day-ahead load forecasting models do not consider this ultimate goal at the training/forecasting stage. This paper proposes a task-based day-ahead load forecasting model labeled as LfEdNet that combines two individual layers in one model, including a load forecasting layer based on deep neural network (Lf layer) and a day-ahead stochastic economic dispatch (SED) layer (Ed layer). The training of LfEdNet aims to minimize the cost of the day-ahead SED in the Ed layer by updating the parameters of the Lf layer. Sequential quadratic programming (SQP) is used to solve the day-ahead SED in the Ed layer. The test results demonstrate that the forecasted results produced by LfEdNet can lead to lower cost of day-ahead SED while maintaining a relatively high forecasting accuracy.