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

Abstract

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.