Short Term Price Forecasting Using Adaptive Generalized Neuron Model

Abstract

This article described how in the competitive deregulated electricity market forecasting has become one of the essential planning tool that assists the planners in preparing the power systems for future demands. The commercial success of the market players depends on their competitive bidding strategy which is suffuicient enough to meet the regulatory requirements and minimize the cost. Artificial neural networks due to their capability of non-linear mapping finds extensive application in the field of price forecasting. Although, they are extensively used as forecasting model, they have certain limitations which are detrimental to system performance. The training time of the artificial neural network is affected by the complexity of the system, and moreover, they require a large amount of data for complex problems. The worl presented in this article deals with the application of the generalized neuron model for forecasting the electricity price. The generalized neuron model overcomes the limitation of the conventional ANN. The electricity price of the New South Wales electricity market is forecast to test the performance of the proposed model. The free parameters of the proposed model are trained using fuzzy tuned genetic algorithms to increase efficacy of the model.