Household Electricity Scheduling Strategy Solution Based on SA-α-QLearning

Household Electricity Scheduling Strategy Solution Based on SA-α-QLearning

Traditional household power dispatching methods are difficult to deal with the complexity of dispatching environment and the randomness of power consumption behavior, and the QLearning algorithm is prone to fall into local optimal solutions and slow convergence when solving problems, this paper proposes a new method based on SA-α-QLearning’s home electricity scheduling strategy solution method. Firstly, a multi-intelligent Markov decision process model is established based on household electrical equipment; then the learning rate of a single value in the QLearning algorithm is replaced by a linear iterative learning rate; finally, a simulated annealing (SA) is used to optimize the QLearning algorithm to solve the model, by taking the Q value change difference as the new solution acceptance probability of Metropoils criterion and the dynamic adjustment temperature reduction coefficient, it alleviates the problem that the QLearing algorithm is easy to fall into the local optimal solution and the convergence speed is slow. Through a large number of comparative experiments, it is proved that the proposed method has a significant improvement in the solution of household electricity dispatching strategy.