A rapid detection of meat spoilage using FTIR and neuro-fuzzy systems
Load forecasting is a critical element of power system operation and planning, involving prediction of the future level of demand to serve as the basis for supply and demand planning. This includes planning for transmission and distribution facilities as well as new generation plants. This paper presents the development of a novel hybrid intelligent model and validates its prediction on the short-term electric load forecasting of the Power System of the Greek Island of Crete. The architecture and learning scheme of a novel fuzzy logic system (AFINN) implemented in the framework of a neural network is proposed. The network constructs its initial rules by clustering while the final fuzzy rule base is determined by competitive learning. The results corresponding to the minimum and maximum load time-series indicate that the proposed load forecasting model provides significantly accurate forecasts, compared to conventional neural networks models.