Neural Network Ensemble-Based Solar Power Generation Short-Term Forecasting

Chaouachi, Anis; Kamel, Rashad M.; Nagasaka, Ken

doi:10.20965/jaciii.2010.p0069

Cited by 60 publications

(41 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The feed forward neural network [4] was the first and arguably simplest type of artificial neural network devised. Feed-forward ANNs allow signals to travel one way only, from input to output.…”

Section: A Feedforward Networkmentioning

confidence: 99%

Evaluate the Performance of Power Energy Output Forecasting in Photovoltaic Cell

2017

IJLTET

View full text Add to dashboard Cite

Section: A Feedforward Networkmentioning

confidence: 99%

Evaluate the Performance of Power Energy Output Forecasting in Photovoltaic Cell

2017

IJLTET

View full text Add to dashboard Cite

“…The constriction coefficient, K, implements a velocity control, effectively eliminating the tendency of some particles to spiral into ever increasing velocity oscillations. The formulation of K is expressed as follows: (6) Where  = c 1 + c 2 and  >4, then the Kennedy and Eberhart's original PSO for velocity updating become:…”

Section: Pso Via Constriction Coefficient Of Clercmentioning

confidence: 99%

“…Polynomial Neural Network based Genetic Algorithm (PNN-GA) was used to search between all possible input variables and to select the order of polynomial and Local Linear Wavelet Neural Network (LLWNN) optimized by Estimation of Distribution Algorithm (EDA) was proposed to train the network parameters (Chen et al, 2005).On the other hand, researchers proved that ensemble neural networks and their training for the same task can produce more accurate results than using individual neural network (Chen et al, 2006). Thus Particle Swarm Optimization (PSO) was used in training neural networks and was applied successfully in time series forecasting (Chaouachi et al, 2009), moreover it was shown that it is better suited for real time series prediction applications than GA because it has fewer parameters to tune and will not follow the rule of the survival of the fittest (Sivanagaraju and Viswanatha, 2008). Based on this recognition, (PSO) algorithm was used to train the selective neural network ensemble(PSOSEN) (Zhang et al, 2007) and Flexible Neural Tree (FNT) with its structure and parameters optimized using (PSO)incorporated with (GA) were applied in both Nasdaq100 and S&P 500 indices .…”

Section: Introductionmentioning

confidence: 99%

Stock Market Index Prediction via Hybrid Inertia Factor PSO and Constriction Coefficient PSO

Ashhar¹,

Ghanbari²,

Nasab³

2014

IJARAFMS

View full text Add to dashboard Cite

“…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%

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

Aboueldahab¹

2012

IJCT

View full text Add to dashboard Cite

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 ...

show abstract

Neural Network Ensemble-Based Solar Power Generation Short-Term Forecasting

Cited by 60 publications

References 18 publications

Evaluate the Performance of Power Energy Output Forecasting in Photovoltaic Cell

Evaluate the Performance of Power Energy Output Forecasting in Photovoltaic Cell

Stock Market Index Prediction via Hybrid Inertia Factor PSO and Constriction Coefficient PSO

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

Contact Info

Product

Resources

About