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The short term load forecasting plays a significant role in the management of power system supply for countries and regions, in particular in cases of insufficient electric energy for increased needs. A back-propagation artificial neural-network (BP-ANN) genetic algorithm (GA) based optimizing technique for improved accuracy of predictions short term loads is proposed. With GA's optimizing and BP-ANN's...
Short term load forecasting (STLF) plays a significant role in the management of power system of countries and regions on the grounds of insufficient electric energy for increased need. This paper presents an approach of back propagation neural network based genetic algorithm (GA) optimizing to develop the accuracy of predictions. With GA's optimizing and BP neural network's dynamic feature, the weight...
A new combined BP neural network model based on accelerating genetic algorithm is put forward in this paper. On the foundation of traditional BP neural network, this method is given better iteration values improved by accelerating genetic algorithm, thus and increase iteration rate and avoid sinking into local minimum. Then, it is applied to forecast the heat load in a certain area, and compared with...
Considering the features of long term load forecasting are complicated, this paper proposes a generic neural network model that is able to adapt to and learn from amount of non-linear or imprecise rules, so it is a model with highly robustness. For avoiding the inflexibility of the generic neural network itself, many experiences and opinions of experts are introduced during the use, so that a comprehensive...
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...
Load demand prediction is vital for maintaining stability and controlling risks of electricity market. An improved model which combines neural network with genetic algorithm is proposed to accurately predict load demand at equilibrium situation of day-ahead electricity market. In the proposed model, load demand prediction problem is converted into optimization problem of error minimization between...
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