Energy usage and demand forecasting is an essential and complex task in real time implementation. Proper coordination is required between the consumer and power companies for monitoring, scheduling and operating the electrical devices without any damages. SEO technology proposes a novel neural network based optimization approach for energy demand prediction. Initially, the Conventional Neural Network (CNN) approach is employed to find the required energy demand prediction at the consumer end. Secondly, Neural Network based Genetic Algorithm (NNGA) and Neural Network based Particle Swarm Optimization (NNPSO) approaches are implemented where the weights of the neural network are automatically adjusted.
Our technology reveals that it is possible to manage the demand and supply, planning of power grid and prediction of future energy requirement in the smart grid.
By changing the electricity market in smart grids, the consumers will be able to react to the electricity price. As close correlation of price and load, the density of this reaction can affect to demand curve and shift it in market.
SEO proposes an accurate prediction model for optimal operation as well as planning in power system. For this purpose, we propose a new hybrid forecast model based on dual-tree complex wavelet transform and multi-stage forecast engine (MSFE)