The Experts below are selected from a list of 279 Experts worldwide ranked by ideXlab platform

Pradeep K Behera - One of the best experts on this subject based on the ideXlab platform.

  • solar radiation prediction based on particle swarm optimization and evolutionary algorithm using recurrent neural networks
    IEEE Systems Conference, 2013
    Co-Authors: Nian Zhang, Pradeep K Behera, C Williams
    Abstract:

    Over the last decade, there has been emphasis on the reduction of the dependency of fossil fuels that resulting in the growth of renewable energy industries. These industries have been significant economic drivers in many parts of the United States supported by both government and private sectors. As a part of renewable energy industries, there is a strong growth in solar power generation industries that often requires prediction of solar energy to develop highly efficient Stand-Alone Photovoltaic Systems as well as hybrid power Systems. Specifically solar radiation prediction is a important component in the solar energy production. However, some computational intelligence methods that have most successful applications on time series prediction have not yet been investigated on solar radiation prediction. Only a limited number of neural networks models were applied to the solar radiation monitoring. Therefore, we propose an Elman style based recurrent neural network to predict solar radiation from the past solar radiation and solar energy in this research. A hybrid learning algorithm incorporating particle swarm optimization and evolutional algorithm was presented, which takes the complementary advantages of the two global optimization algorithms. The neural networks model was trained by particle swarm optimization and evolutional algorithm to forecast the solar radiation. The excellent experimental results demonstrated that the proposed hybrid learning algorithm can be successfully used for the recurrent neural networks based prediction model for the solar radiation monitoring.

  • solar radiation prediction based on recurrent neural networks trained by levenberg marquardt backpropagation learning algorithm
    IEEE PES Innovative Smart Grid Technologies Conference, 2012
    Co-Authors: Nian Zhang, Pradeep K Behera
    Abstract:

    In response to the growing concern over the use of fossil fuels, renewable energy industries have been significant economic drivers in many parts of the United States. In the recent years there is a strong growth in solar power generation industries that requires prediction of solar energy to develop highly efficient Stand-Alone Photovoltaic Systems as well as hybrid power Systems. In order to accomplish the goal, we propose a predictive model that is based on recurrent neural networks trained with the Levenberg-Marquardt backpropagation learning algorithm to forecast the solar radiation using the past solar radiation and solar energy. This computational intelligence modeling tool explored the impact of solar radiation and solar energy in forecasting reliable long-run solar energy. Based on the excellent experimental results including the mean squared error analysis, error autocorrelation function analysis, regression analysis, and time series response, it demonstrated that the proposed neural network structure and the learning algorithm could be very useful in training the recurrent neural network for the solar radiation prediction.

  • ISGT - Solar radiation prediction based on recurrent neural networks trained by Levenberg-Marquardt backpropagation learning algorithm
    2012 IEEE PES Innovative Smart Grid Technologies (ISGT), 2012
    Co-Authors: Nian Zhang, Pradeep K Behera
    Abstract:

    In response to the growing concern over the use of fossil fuels, renewable energy industries have been significant economic drivers in many parts of the United States. In the recent years there is a strong growth in solar power generation industries that requires prediction of solar energy to develop highly efficient Stand-Alone Photovoltaic Systems as well as hybrid power Systems. In order to accomplish the goal, we propose a predictive model that is based on recurrent neural networks trained with the Levenberg-Marquardt backpropagation learning algorithm to forecast the solar radiation using the past solar radiation and solar energy. This computational intelligence modeling tool explored the impact of solar radiation and solar energy in forecasting reliable long-run solar energy. Based on the excellent experimental results including the mean squared error analysis, error autocorrelation function analysis, regression analysis, and time series response, it demonstrated that the proposed neural network structure and the learning algorithm could be very useful in training the recurrent neural network for the solar radiation prediction.

Nian Zhang - One of the best experts on this subject based on the ideXlab platform.

  • solar radiation prediction based on particle swarm optimization and evolutionary algorithm using recurrent neural networks
    IEEE Systems Conference, 2013
    Co-Authors: Nian Zhang, Pradeep K Behera, C Williams
    Abstract:

    Over the last decade, there has been emphasis on the reduction of the dependency of fossil fuels that resulting in the growth of renewable energy industries. These industries have been significant economic drivers in many parts of the United States supported by both government and private sectors. As a part of renewable energy industries, there is a strong growth in solar power generation industries that often requires prediction of solar energy to develop highly efficient Stand-Alone Photovoltaic Systems as well as hybrid power Systems. Specifically solar radiation prediction is a important component in the solar energy production. However, some computational intelligence methods that have most successful applications on time series prediction have not yet been investigated on solar radiation prediction. Only a limited number of neural networks models were applied to the solar radiation monitoring. Therefore, we propose an Elman style based recurrent neural network to predict solar radiation from the past solar radiation and solar energy in this research. A hybrid learning algorithm incorporating particle swarm optimization and evolutional algorithm was presented, which takes the complementary advantages of the two global optimization algorithms. The neural networks model was trained by particle swarm optimization and evolutional algorithm to forecast the solar radiation. The excellent experimental results demonstrated that the proposed hybrid learning algorithm can be successfully used for the recurrent neural networks based prediction model for the solar radiation monitoring.

  • solar radiation prediction based on recurrent neural networks trained by levenberg marquardt backpropagation learning algorithm
    IEEE PES Innovative Smart Grid Technologies Conference, 2012
    Co-Authors: Nian Zhang, Pradeep K Behera
    Abstract:

    In response to the growing concern over the use of fossil fuels, renewable energy industries have been significant economic drivers in many parts of the United States. In the recent years there is a strong growth in solar power generation industries that requires prediction of solar energy to develop highly efficient Stand-Alone Photovoltaic Systems as well as hybrid power Systems. In order to accomplish the goal, we propose a predictive model that is based on recurrent neural networks trained with the Levenberg-Marquardt backpropagation learning algorithm to forecast the solar radiation using the past solar radiation and solar energy. This computational intelligence modeling tool explored the impact of solar radiation and solar energy in forecasting reliable long-run solar energy. Based on the excellent experimental results including the mean squared error analysis, error autocorrelation function analysis, regression analysis, and time series response, it demonstrated that the proposed neural network structure and the learning algorithm could be very useful in training the recurrent neural network for the solar radiation prediction.

  • ISGT - Solar radiation prediction based on recurrent neural networks trained by Levenberg-Marquardt backpropagation learning algorithm
    2012 IEEE PES Innovative Smart Grid Technologies (ISGT), 2012
    Co-Authors: Nian Zhang, Pradeep K Behera
    Abstract:

    In response to the growing concern over the use of fossil fuels, renewable energy industries have been significant economic drivers in many parts of the United States. In the recent years there is a strong growth in solar power generation industries that requires prediction of solar energy to develop highly efficient Stand-Alone Photovoltaic Systems as well as hybrid power Systems. In order to accomplish the goal, we propose a predictive model that is based on recurrent neural networks trained with the Levenberg-Marquardt backpropagation learning algorithm to forecast the solar radiation using the past solar radiation and solar energy. This computational intelligence modeling tool explored the impact of solar radiation and solar energy in forecasting reliable long-run solar energy. Based on the excellent experimental results including the mean squared error analysis, error autocorrelation function analysis, regression analysis, and time series response, it demonstrated that the proposed neural network structure and the learning algorithm could be very useful in training the recurrent neural network for the solar radiation prediction.

Ma Egido - One of the best experts on this subject based on the ideXlab platform.

  • dependability analysis of stand alone Photovoltaic Systems
    Progress in Photovoltaics, 2007
    Co-Authors: P. Díaz, Ma Egido, Frans Nieuwenhout
    Abstract:

    Long-term performance of PV Stand-Alone Systems is analysed in this work in terms of dependability. On one side, the quality of a PV system, the energy service supplied to the users, depends on the initial design and sizing and on the component ageing that progressively decreases the availability of supply on demand (energy reliability). On the other side, technical failures lead to system stoppage until repairing is performed (technical reliability), which is crucial in real rural electrification applications. All those factors are analysed together with the basis of an extended field, laboratory and bibliographic review work.

  • Dependability analysis of stand‐alone Photovoltaic Systems
    Progress in Photovoltaics: Research and Applications, 2007
    Co-Authors: P. Díaz, Ma Egido, Frans Nieuwenhout
    Abstract:

    Long-term performance of PV Stand-Alone Systems is analysed in this work in terms of dependability. On one side, the quality of a PV system, the energy service supplied to the users, depends on the initial design and sizing and on the component ageing that progressively decreases the availability of supply on demand (energy reliability). On the other side, technical failures lead to system stoppage until repairing is performed (technical reliability), which is crucial in real rural electrification applications. All those factors are analysed together with the basis of an extended field, laboratory and bibliographic review work.

  • Experimental analysis of battery charge regulation in Photovoltaic Systems
    Progress in Photovoltaics: Research and Applications, 2003
    Co-Authors: P. Díaz, Ma Egido
    Abstract:

    The influence of charge regulation on batteries for Stand-Alone Photovoltaic Systems is analysed in relation to two factors: battery lifetime and the daily energy service supplied. The regulation thresholds adjusted in the charge controller (for disconnection and reconnection), in overcharge and deep discharge conditions, determine the whole system operation. Laboratory testing procedures are proposed and applied to different components of the Photovoltaic rural electrification market. Finally, technical recommendations for charge regulation of lead‐acid batteries are presented. Copyright # 2003 John Wiley & Sons, Ltd.

  • the sizing of stand alone pv system a review and a proposed new method
    Solar Energy Materials and Solar Cells, 1992
    Co-Authors: Ma Egido, E Lorenzo
    Abstract:

    Abstract The reliability of stand alone Photovoltaic Systems is analyzed in terms of the loss of load probability, LLP. A variety of numerical and analytic models for calculating the LLP are described and evaluated using data for three Spanish locations. Madrid, Murcia and Santander, selected because they represent different climatic conditions. It is concluded that numerical models are accurate but complex to use, while analytic models exhibit significant lack of accuracy. A new analytic model, as accurate as the numerical models and as simple as analytic models, is proposed. For each location, the model requires as input 4 different coefficients.

Lauro De Vilhena Brandao Machado Neto - One of the best experts on this subject based on the ideXlab platform.

  • a stochastic method for stand alone Photovoltaic system sizing
    Solar Energy, 2010
    Co-Authors: C V T Cabral, Delly Oliveira Filho, Antonia Sonia A C Diniz, Jose Helvecio Martins, Olga Moraes Toledo, Lauro De Vilhena Brandao Machado Neto
    Abstract:

    Abstract Photovoltaic Systems utilize solar energy to generate electrical energy to meet load demands. Optimal sizing of these Systems includes the characterization of solar radiation. Solar radiation at the Earth’s surface has random characteristics and has been the focus of various academic studies. The objective of this study was to stochastically analyze parameters involved in the sizing of Photovoltaic generators and develop a methodology for sizing of Stand-Alone Photovoltaic Systems. Energy storage for isolated Systems and solar radiation were analyzed stochastically due to their random behavior. For the development of the methodology proposed stochastic analysis were studied including the Markov chain and beta probability density function. The obtained results were compared with those for sizing of stand–alone using from the Sandia method (deterministic), in which the stochastic model presented more reliable values. Both models present advantages and disadvantages, however, the stochastic one is more complex and provides more reliable and realistic results.

Pedro J. Zufiria - One of the best experts on this subject based on the ideXlab platform.

  • A new approach for sizing stand alone Photovoltaic Systems based in neural networks
    Solar Energy, 2005
    Co-Authors: L. Hontoria, Jorge Aguilera, Pedro J. Zufiria
    Abstract:

    Abstract Several methods for sizing stand alone Photovoltaic (pv) Systems has been developed. The more simplistic are called intuitive methods. They are a useful tool for a first approach in sizing stand alone Photovoltaic Systems. Nevertheless they are very inaccurate. Analytical methods use equations to describe the pv system size as a function of reliability. These ones are more accurate than the previous ones but they are also not accurate enough for sizing of high reliability. In a third group there are methods which use system simulations. These ones are called numerical methods. Many of the analytical methods employ the concept of reliability of the system or the complementary term: loss of load probability (LOLP). In this paper an improvement for obtaining LOLP curves based on the neural network called Multilayer Perceptron (MLP) is presented. A unique MLP for many locations of Spain has been trained and after the training, the MLP is able to generate LOLP curves for any value and location.