  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.
