Comparison of feedforward and feedback neural network architectures for short term wind speed prediction

Welch, Richard L.; Ruffing, Stephen M.; Venayagamoorthy, G.K.

doi:10.1109/ijcnn.2009.5179034

Cited by 80 publications

(30 citation statements)

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“…Neural networks have been extensively studied and shown to provide accurate load predictions [22,23]. Neural networks have also shown to be promising tools for wind and solar energy predictions [24,25]. When alternative sources of energy are connected to the power grid, be it wind farms or plug-in vehicles, dynamic load and electric energy at a given time needs to be predicted in order to carry out an efficient and economical operation of a smart grid.…”

Section: CI Methods For Smart Gridsmentioning

confidence: 99%

Potentials and promises of computational intelligence for smart grids

Venayagamoorthy

2009

2009 IEEE Power &Amp; Energy Society General Meeting

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The electric power grid is a complex adaptive system under semi-autonomous distributed control. It is spatially and temporally complex, non-convex, nonlinear and non-stationary with a lot of uncertainties. The integration of renewable energy such as wind farms, and plug-in hybrid and electric vehicles further adds complexity and challenges to the various controllers at all levels of the power grid. A lot of efforts have gone into the development of a smart grid to align the interests of the electric utilities, consumers and environmentalists. Advanced computational methods are required for planning and optimization, fast control of power system elements, processing of field data and coordination across the grid. Distributed and coordinated intelligence at all levels and across levels of the electric power grid -generation, transmission and distribution is inevitable if a true smart grid is to be reality. Computational intelligence (CI) is the study of adaptive mechanisms to enable or facilitate intelligent behavior in complex, uncertain and changing environments. These adaptive mechanisms include artificial and bioinspired intelligence paradigms that exhibit an ability to learn or adapt to new situations, to generalize, abstract, discover and associate. The paradigms of CI mimic nature for solving complex problems. CI is successor of artificial intelligence and is the way of the future computing. This paper presents the potentials and promises of CI to realize an intelligent smart grid.Index Terms-adaptive control, adaptive critic designs, computational intelligence, FACTS, plug-in vehicles, renewable energy, wide area monitoring and control.

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Section: CI Methods For Smart Gridsmentioning

confidence: 99%

Potentials and promises of computational intelligence for smart grids

Venayagamoorthy

2009

2009 IEEE Power &Amp; Energy Society General Meeting

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“…The hourly wind speed data are divided into two parts which are the training part and the testing part [2][3][16][17]; the first 80% of the data will be taken as a training data while theremaining 20% will be the testing data. The actual and predicted wind speed using our proposed passive congregation model is shown in Figures 1-3.…”

Section: Experimental Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Without this ability, a wind farm operator is prone to allocate more generation units or supplemental energy reserves than necessary in order to ensure budgeted electricity outputs are met, with an end result of increased operating costs [2]. Thus, the further prediction accuracy improvement of the wind speed predication becomes a fundamental issue in the wind industry [2-3].In short term wind speed prediction, Feedforward Neural Networks (FNN) trained using tabu search [4] and Recurrent Neural Networks (RNN) [5] have been used.Particle Swarm Optimization (PSO) which is a population based stochastic algorithm was used in training both FNN and RNN wind speed predictors to enhance their prediction accuracy [2]. Also, Genetic Algorithm (GA) operators was introduced to increase the training accuracy of PSO learning algorithm, to form GA/ PSO hybrid model to enable searching new regions in the search space [6][7].…”

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Short Term Wind Speed Prediction Using a New Hybrid Model With Passive Congregation

Aboueldahab¹

2012

IJCT

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Short term wind speed predicting is essential in using wind energy as an alternative source of electrical power generation, thus the improvement of wind speed prediction accuracy becomes an important issue. Although many prediction models have been developed during the last few years, they suffer a poor performance becausetheir dependency on performing only the local search without the capability in performing the global search in the whole search space.To overcome this problem, we propose a new passive congregation term to the standard hybrid Genetic Algorithm / Particle Swarm Optimization (GA/ PSO) model in training Neural Network (NN) wind speed predictor. This term is based on the mutual cooperation between different particles in determining new positions rather than their selfish thinking. Experiment study shows significantly the influence of the passive congregation term in improving the performance accuracy compared to the standard model. General TermsWind Speed Prediction, Key wordsParticle Swarm Optimization, Genetic Algorithm, Neural Networks, Passive Congregation. INTRODUCTIONWind energy is one of the lowest costs of electricity production among renewable energy sources, but is visible only as long as weather conditions allow, so to maintain economical dispatch of wind generation electricity, it is important to make short term predictions of future wind speed which directly affects generation capacity [1]. Without this ability, a wind farm operator is prone to allocate more generation units or supplemental energy reserves than necessary in order to ensure budgeted electricity outputs are met, with an end result of increased operating costs [2]. Thus, the further prediction accuracy improvement of the wind speed predication becomes a fundamental issue in the wind industry [2-3].In short term wind speed prediction, Feedforward Neural Networks (FNN) trained using tabu search [4] and Recurrent Neural Networks (RNN) [5] have been used.Particle Swarm Optimization (PSO) which is a population based stochastic algorithm was used in training both FNN and RNN wind speed predictors to enhance their prediction accuracy [2]. Also, Genetic Algorithm (GA) operators was introduced to increase the training accuracy of PSO learning algorithm, to form GA/ PSO hybrid model to enable searching new regions in the search space [6][7].However, in GA/ PSO model, the PSO algorithm is restricted in doing the local search around its best position and the global best position without any cooperation with other particles in the swarm while the GA operators are responsible in doing the global search by making particles fly to new regions in the search space leading to poor model performance in terms of accuracy, convergence speed and robustness [8].To overcome this problem and improve the model performance, each particle in the swarm should be capable of performing the global search and not to be only restricted in local search [9]. Thus, we introduce our new proposed hybrid (GA/PSO) model with passive congregation inspired ...

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“…at al [34] compares three types of neural networks (namely MLP, simultaneous recurrent neural network 24 (SRN) and Elman recurrent neural network) trained using particle swarm optimization (PSO) for short …”

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confidence: 99%