Power system load forecasting based on MHBMO algorithm and neural network

Considering the necessity of accurate power load demand prediction, a sufficient method based on Modified Honey Bee Mating Algorithm (MHBMO) and Artificial Neural Network (ANN) is proposed to enhance the degree of conformity of the predicted power demand to its actual value. In recent years ANN has been among the most popular methods used in load prediction. In fact it has proved its powerful performance to detect nonlinear mappings among different variables and as a result has become successful in prediction applications. On the other hand, in recent years MHBMO algorithm has been known as one of the most famous and effective optimization tools. Ability in finding global optimum solution and handling complex multi-objective optimization problems has demonstrated its superiority than the other optimization algorithms. Therefore, in this essay for the first time MHBMO algorithm is utilized to adjust the weight matrix of ANN and so optimizing the degree of uncertainty existing in load demand prediction.