The Experts below are selected from a list of 1551 Experts worldwide ranked by ideXlab platform
Malcolm Mcculloch - One of the best experts on this subject based on the ideXlab platform.
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Daily Clearness Index profiles and weather conditions studies for photovoltaic systems
Energy Procedia, 2017Co-Authors: Chun Sing Lai, Loi Lei Lai, Malcolm MccullochAbstract:Abstract The increasing number of distributed photovoltaic (PV) systems connected to the power grid has made system planning and performance evaluation a challenging task. This is mainly due to the computational complexity, such as load flow analysis with large irradiance datasets collected from various locations of the installed PV farms. Solar irradiance data are known to possess the characteristic of high uncertainty, due to the random nature of cloud cover and atmospheric conditions. This paper presents the studies on the relationships of clustered Clearness Index profiles and the weather conditions obtained from the weather forecasting stations. Four years of solar irradiance and weather conditions data from two locations (Johannesburg and Kenya) were obtained and are used for the analysis. The preliminary study shows that the weather condition is related to the daily Clearness Index profiles. This work will form the basis for estimating the daily Clearness Index profile with weather conditions.
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Daily Clearness Index Profiles Cluster Analysis for Photovoltaic System
IEEE Transactions on Industrial Informatics, 2017Co-Authors: Chun Sing Lai, Youwei Jia, Malcolm MccullochAbstract:Due to various weather perturbation effects, the stochastic nature of real-life solar irradiance has been a major issue for solar photovoltaic (PV) system planning and performance evaluation. This paper aims to discover Clearness Index (CI) patterns and to construct centroids for the daily CI profiles. This will be useful in being able to provide a standardized methodology for PV system design and analysis. Four years of solar irradiance data collected from Johannesburg (26.21 S, 28.05 E), South Africa are used for the case study. The variation in CI could be significant in different seasons. In this paper, cluster analysis with Gaussian mixture models (GMM), K -Means with Euclidean distance (ED), K -Means with Manhattan distance, Fuzzy C -Means (FCM) with ED, and FCM with dynamic time warping (FCM DTW) are performed for the four seasons. A case study based on sizing a stand-alone solar PV and storage system with anaerobic digestion biogas power plants is used to examine the usefulness of the clustering results. It concludes that FCM DTW and GMM can determine the correct PV farm rated capacity with an acceptable energy storage capacity, with 36 and 46 rather than 1457 solar irradiance profiles, respectively.
Chun Sing Lai - One of the best experts on this subject based on the ideXlab platform.
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Daily Clearness Index profiles and weather conditions studies for photovoltaic systems
Energy Procedia, 2017Co-Authors: Chun Sing Lai, Loi Lei Lai, Malcolm MccullochAbstract:Abstract The increasing number of distributed photovoltaic (PV) systems connected to the power grid has made system planning and performance evaluation a challenging task. This is mainly due to the computational complexity, such as load flow analysis with large irradiance datasets collected from various locations of the installed PV farms. Solar irradiance data are known to possess the characteristic of high uncertainty, due to the random nature of cloud cover and atmospheric conditions. This paper presents the studies on the relationships of clustered Clearness Index profiles and the weather conditions obtained from the weather forecasting stations. Four years of solar irradiance and weather conditions data from two locations (Johannesburg and Kenya) were obtained and are used for the analysis. The preliminary study shows that the weather condition is related to the daily Clearness Index profiles. This work will form the basis for estimating the daily Clearness Index profile with weather conditions.
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Daily Clearness Index Profiles Cluster Analysis for Photovoltaic System
IEEE Transactions on Industrial Informatics, 2017Co-Authors: Chun Sing Lai, Youwei Jia, Malcolm MccullochAbstract:Due to various weather perturbation effects, the stochastic nature of real-life solar irradiance has been a major issue for solar photovoltaic (PV) system planning and performance evaluation. This paper aims to discover Clearness Index (CI) patterns and to construct centroids for the daily CI profiles. This will be useful in being able to provide a standardized methodology for PV system design and analysis. Four years of solar irradiance data collected from Johannesburg (26.21 S, 28.05 E), South Africa are used for the case study. The variation in CI could be significant in different seasons. In this paper, cluster analysis with Gaussian mixture models (GMM), K -Means with Euclidean distance (ED), K -Means with Manhattan distance, Fuzzy C -Means (FCM) with ED, and FCM with dynamic time warping (FCM DTW) are performed for the four seasons. A case study based on sizing a stand-alone solar PV and storage system with anaerobic digestion biogas power plants is used to examine the usefulness of the clustering results. It concludes that FCM DTW and GMM can determine the correct PV farm rated capacity with an acceptable energy storage capacity, with 36 and 46 rather than 1457 solar irradiance profiles, respectively.
Lucien Wald - One of the best experts on this subject based on the ideXlab platform.
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comparison between meteorological re analyses from era interim and merra and measurements of daily solar irradiation at surface
Renewable Energy, 2015Co-Authors: Alexandre Boilley, Lucien WaldAbstract:This paper compares the daily solar irradiation available at surface estimated by the MERRA (Modern-Era Retrospective Analysis for Research and Applications) re-analysis of the NASA and the ERA-Interim re-analysis of the European Center for Medium-range Weather Forecasts (ECMWF) against qualified ground measurements made in stations located in Europe, Africa and Atlantic Ocean. Using the Clearness Index, also known as atmospheric transmissivity or transmittance, this study evidences that the re-analyses often predict clear sky conditions while actual conditions are cloudy. The opposite is also true though less pronounced: actual clear sky conditions are predicted as cloudy. This overestimation of occurrence of clear sky conditions leads to an overestimation of the irradiation and Clearness Index by MERRA. The overall overestimation is less pronounced for ERA-Interim because the overestimation observed in clear sky conditions is counter-balanced by underestimation in cloudy conditions. The squared correlation coefficient for Clearness Index ranges between 0.38 and 0.53, showing that a very large part of the variability in irradiation is not captured by the re-analyses. Within an irradiation homogeneous area, the variability of the bias, root mean square error and correlation coefficient are surprisingly large. MERRA and ERA-Interim should only be used in solar energy with proper understanding of the limitations and uncertainties. In regions where clouds are rare, e.g. North Africa, MERRA or ERA-Interim may be used to provide a gross estimate of monthly or yearly irradiation. Satellite-derived data sets offer less uncertainty and should be preferred.
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comparison between meteorological re analyses from era interim and merra and measurements of daily solar irradiation at surface
Renewable Energy, 2015Co-Authors: Alexandre Boilley, Lucien WaldAbstract:This paper compares the daily solar irradiation available at surface estimated by the MERRA (Modern-Era Retrospective Analysis for Research and Applications) re-analysis of the NASA and the ERA-Interim reanalysis of the European Center for Medium-range Weather Forecasts (ECMWF) against qualified ground measurements made in stations located in Europe, Africa and Atlantic Ocean. Using the Clearness Index, also known as atmospheric transmissivity or transmittance, this study evidences that the re-analyses often predict clear sky conditions while actual conditions are cloudy. The opposite is also true though less pronounced: actual clear sky conditions are predicted as cloudy. This overestimation of occurrence of clear sky conditions leads to an overestimation of the irradiation and Clearness Index by MERRA. The overall overestimation is less pronounced for ERA-Interim because the overestimation observed in clear sky conditions is counter-balanced by underestimation in cloudy conditions. The squared correlation coefficient for Clearness Index ranges between 0.38 and 0.53, showing that a very large part of the variability in irradiation is not captured by the re-analyses. Within an irradiation homogeneous area, the variability of the bias, root mean square error and correlation coefficient are surprisingly large. MERRA and ERA-Interim should only be used in solar energy with proper understanding of the limitations and uncertainties. In regions where clouds are rare, e.g. North Africa, MERRA or ERA-Interim may be used to provide a gross estimate of monthly or yearly irradiation. Satellite-derived data sets offer less uncertainty and should be preferred. © 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license
Alfonso Soler - One of the best experts on this subject based on the ideXlab platform.
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Effect of sunshine and solar declination on the computation of monthly mean daily diffuse solar radiation
Renewable Energy, 1999Co-Authors: K.k. Gopinathan, Alfonso SolerAbstract:Several years of measured data for 17 European locations have been used to develop models for estimating monthly mean daily values of diffuse radiation (Hd) from combinations of the following: Clearness Index, sunshine fraction, and solar declination. Two models giving the highest correlation coefficients and the lowest standard errors of estimation are tested with data for 10 European locations not used in their development. From consideration of the MBE and RMSE values, a model which estimates Hd values from Clearness Index, relative sunshine duration and solar declination is found to be the most accurate. Comparison with Hd values predicted with the European Community solar radiation model (ECM) confirms this conclusion.
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the determination of monthly mean hourly diffuse radiation on horizontal surfaces using equations based on hourly Clearness Index sunshine fraction and solar elevation
International Journal of Solar Energy, 1996Co-Authors: K.k. Gopinathan, Alfonso SolerAbstract:Several years of measured hourly diffuse and global data for four locations in Spain are used to establish correlations for estimating monthly mean hourly diffuse radiation Dh for locations in Spain and to test the influence of various climatic and geographic parameters on the diffuse to global fraction. A correlation connecting the monthly mean hourly diffuse to global fraction Dh/Gh with mean hourly Clearness Index KT, mean monthly hourly sunshine fraction S/So, and solar elevation at mid hour y, is found most suitable, and an equation of the form Dh/Gh=i+jkT+lsin γ+m s/sO is recommended for estimation purposes for any location in Spain. When the solar altitude and sunshine fraction are added to the basic equation Dh/Gh= a+bKT the accuracy of the estimated data improves to a good extent and this improvement is more significant when correlations are developed from a group of stations in a region rather than for a single isolated location.
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A MULTIPLE LINEAR CORRELATION FOR DIFFUSE RADIATION FROM GLOBAL AND SUNSHINE DATA
International Journal of Solar Energy, 1994Co-Authors: K.k. Gopinathan, Alfonso SolerAbstract:Several years of measured data of global and diffuse radiation together with sunshine duration, for five locations in Spain are used to establish empirical relationships to connect monthly mean daily diffuse irradiation with Clearness Index and relative sunshine duration. A correlation connecting sky radiation with both Clearness Index and percent possible sunshine together is found to be most accurate for locations in Spain and Portugal. When Clearness Index and relative sunshine duration are combined together, it is observed that the accuracy of the estimated diffuse radiation data is better than when they are used separately.
Hadj A Arab - One of the best experts on this subject based on the ideXlab platform.
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methodology for predicting sequences of mean monthly Clearness Index and daily solar radiation data in remote areas application for sizing a stand alone pv system
Renewable Energy, 2008Co-Authors: A Mellit, Soteris A. Kalogirou, Sulaiman Shaari, H Salhi, Hadj A ArabAbstract:Abstract In this paper, a suitable adaptive neuro-fuzzy inference system (ANFIS) model is presented for estimating sequences of mean monthly Clearness Index ( K ¯ t ) and total solar radiation data in isolated sites based on geographical coordinates. The magnitude of solar radiation is the most important parameter for sizing photovoltaic (PV) systems. The ANFIS model is trained by using a multi-layer perceptron (MLP) based on fuzzy logic (FL) rules. The inputs of the ANFIS are the latitude, longitude, and altitude, while the outputs are the 12-values of mean monthly Clearness Index K ¯ t . These data have been collected from 60 locations in Algeria. The results show that the performance of the proposed approach in the prediction of mean monthly Clearness Index K ¯ t is favorably compared to the measured values. The root mean square error (RMSE) between measured and estimated values varies between 0.0215 and 0.0235 and the mean absolute percentage error (MAPE) is less than 2.2%. In addition, a comparison between the results obtained by the ANFIS model and artificial neural network (ANN) models, is presented in order to show the advantage of the proposed method. An example for sizing a stand-alone PV system is also presented. This technique has been applied to Algerian locations, but it can be generalized for any geographical position. It can also be used for estimating other meteorological parameters such as temperature, humidity and wind speed.
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an anfis based prediction for monthly Clearness Index and daily solar radiation application for sizing of a stand alone photovoltaic system
2007Co-Authors: A Mellit, Sulaiman Shaari, Hadj A Arab, Arda ComplutenseAbstract:A suitable Neuro-Fuzzy model is presented for estimating sequences of monthly Clearness Index () K t in isolated sites based only on geographical coordinates. The Clearness Index () K t corresponds to the solar radiation data (H) divided by the corresponding extraterrestrial data (H0). Solar radiation data is the most important parameters for sizing photovoltaic (PV) system. The Adaptive Neuro-Fuzzy Inference System (ANFIS) model is trained by using the Multilayer Perceptron (MLP) based on the Fuzzy Logic (FL) rule. The inputs of the network are the latitude, longitude, and altitude, while the outputs are the 12-values of K t , where these data have been collected over 60 locations in Algeria. The K t corresponding of 56 sites have been used for training the proposed ANFIS. However, the K t relative to 4-sites have been selected randomly from the database in order to test and validate the proposed ANFIS model. The performance of the approach in the prediction K t is favorably compared to the measured values, with a Root Mean Square Error (RMSE) between 0.0215 and 0.0235, and the Mean Relative Error (MRE) not exceeding 2.2%. In addition, a comparison between the results obtained by the ANFIS model and other Artificial Neural Networks (ANN) is presented in order to show the performance of the model. An example of sizing PV system is presented. Although this technique has been applied for Algerian locations, but can be generalized in any geographical location in the world.