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The increased integration of wind power into the electric grid, as it occurs today in Portugal, poses new challenges due to its intermittency and volatility. Wind power forecasting plays a key role in tackling these challenges. A novel hybrid approach, combining wavelet transform, particle swarm optimization, and an adaptive-network-based fuzzy inference system, is proposed in this paper for short-term...
This paper presents the development of an Artificial Neural Networks and Particle Swarm Optimization (ANN-PSO) based short-term load forecasting model with improved generalization technique for the Regional Power Control Center of Saudi Electricity Company, Western Operation Area (SEC-WOA). Weather, load demand, wind speed, wind direction, heat, sunlight, etc. are quite different in a desert land...
This paper presents a comprehensive study of forecasting a day-ahead of load and locational marginal pricing (LMP) using artificial intelligent systems. An artificial neural network (ANN) is trained with a stochastic optimization technique called particle swarm optimization (PSO). This training algorithm works to adjust the network weights and biases as to minimize the error function. Wavelet transformed...
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