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Wind turbine power output is totally intermittent in the nature. For grid connected wind turbine generators, power system operators (transmission system operators) need reliable and robust wind power forecasting system. Rapid changes in the wind generation relative to the load require proper energy management system to maintain the power system stability and of course to balance the power generation,...
The past few years have witnessed a growing rate of attraction in adoption of Artificial Intelligence (AI) techniques to solve different engineering problems. Besides, Short Term Electrical Load Forecasting (STLF) is one of the important concerns of power systems and accurate load forecasting is vital for managing supply and demand of electricity. This study estimates short term electricity loads...
Short-term load forecasting in power system is necessary for management and control of power system. A new method for short-term load forecasting was presented based on neural networks optimized by genetic algorithm (GA) is proposed in this paper, short-term load forecasting model for power system was setup as sample sets for Elman neural network (Elman NN), with GA's optimizing and Elman NN's dynamic...
Short term load forecasting is very essential to the operation of electricity companies. It enhances the energy-efficient and reliable operation of power system. Artificial neural networks are employed for nonlinear short term load forecasting owing to their powerful nonlinear mapping capabilities. These are generally trained through back-propagation, genetic algorithm (GA), particle swarm optimization...
This paper presents a methodology of short term generation scheduling (unit commitment) for thermal units integrated with wind energy system considering the unexpected deviation on load demand. The deviation in load demand occurs mainly due to variation in temperature which in turns yields error in load forecasting. Since the usual unit commitment (UC) scheduling as well as economic power dispatch...
An integrated BP neural network and particle swarm optimization (PSO) for load forecasting method is presented in this paper. From the signal analysis point of view, load can also be considered as a linear combination of different frequencies. The proposed approach decomposes the historical load into an approximate part and several detail parts through the wavelet transform. Then based on the maximum...
Short term load forecasting is very essential to the operation of electricity companies. It enhances the energy-efficient and reliable operation of power system. Artificial neural networks have long been proven as a very accurate non-linear mapper. ANN based STLF models generally use back propagation algorithm which does not converge optimally & requires much longer time for training, which makes...
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