# Reinforcement Learning based Dynamic Model Selection for Short-Term Load Forecasting

arXiv: https://arxiv.org/abs/1811.01846 · HTML: https://mlsanity.com/paper/1811.01846/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2018.

## Built on by (most cited first, 4 of 4)

- [Time Series Anomaly Detection via Reinforcement Learning-Based Model Selection](https://mlsanity.com/paper/2205.09884/) (2022)
- [Faster Machine Translation Ensembling with Reinforcement Learning and Competitive Correction](https://mlsanity.com/paper/2501.15219/) (2025)
- [FoodRL: A Reinforcement Learning Ensembling Framework For In-Kind Food Donation Forecasting](https://mlsanity.com/paper/2511.04865/) (2025)
- [Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-term Utility Demand Forecasting in Electricity Wholesale Markets](https://mlsanity.com/paper/2206.11715/) (2022)

## Abstract (arXiv)

With the growing prevalence of smart grid technology, short-term load forecasting (STLF) becomes particularly important in power system operations. There is a large collection of methods developed for STLF, but selecting a suitable method under varying conditions is still challenging. This paper develops a novel reinforcement learning based dynamic model selection (DMS) method for STLF. A forecasting model pool is first built, including ten state-of-the-art machine learning based forecasting models. Then a Q-learning agent learns the optimal policy of selecting the best forecasting model for the next time step, based on the model performance. The optimal DMS policy is applied to select the best model at each time step with a moving window. Numerical simulations on two-year load and weather data show that the Q-learning algorithm converges fast, resulting in effective and efficient DMS. The developed STLF model with Q-learning based DMS improves the forecasting accuracy by approximately 50%, compared to the state-of-the-art machine learning based STLF models.