Price prediction techniques for residential demand response using support vector regression

The bidirectional flow of information among utilities and energy customers can be easily adapted to increase awareness for user's involvement in demand response programs. In demand response programs to improve the interaction between utility and customer, price communication plays an important role. If the future prices for next day can be sent to end consumer, so with the prior knowledge of price, the consumer can schedule their appliances in the same accordance to get less amount in the bill. Therefore, to get prior price information prediction technique comes in the scenario. To enhance price prediction capability, it needs a call from optimization techniques. In this paper, we have proposed the price prediction by support vector regression with genetic algorithm (SVRGA) approach. The simulation result has shown the efficiency of proposed approach and proposed technique is also compared with other existing techniques as artificial neural network (ANN) and linear prediction model (LPM).