A HYBRID METHOD FOR LOAD FORECASTING IN SMART GRID BASED ON NEURAL NETWORKS AND CUCKOO SEARCH OPTIMIZATION APPROACH

Load balancing is one of the most challenging goals in smart grid systems. Obviously, to response this challenge, a selfish user’s behavior necessitates the use of incentive compatible mechanisms. In the mechanisms, the incentives should be provided in a manner to motivate consumers to cooperate for regulation of demand and supply. Dynamic pricing is one of the best mechanisms in which the price is being adjusted dynamically according to make a balance between supply and demand. In the balance, the consumer’s demand for energy through financial incentives is adjusted. To determine and announce the appropriate electricity price, there should be a precise forecast for energy usage. This paper develops two neural networks for each influential factors based on the situation such as weather related or historical loads criteria. Afterwards, the outputs of neural networks are aggregated with the use of Induced Ordered Weighted Averaging Operator (IOWA). The argument ordering process is guided by mean square error. Also the cuckoo optimization algorithm is applied on artificial neural networks to improve the accuracy of them. The experimental result show that the precision of aggregated load forecasting based upon IOWA operator is improved significantly