The Experts below are selected from a list of 160413 Experts worldwide ranked by ideXlab platform
Waleed Ahmed Khan - One of the best experts on this subject based on the ideXlab platform.
-
The Artificial Neural Network for solar radiation prediction and designing solar systems: A systematic literature review
Journal of Cleaner Production, 2015Co-Authors: Atika Qazi, A. Wadi, Ram Gopal Raj, Hina Fayaz, Nasrudin Abd Rahim, Waleed Ahmed KhanAbstract:Solar energy generated by sunlight has a non-schedulable nature due to the stochastic environment of meteorological conditions. Hence, power system control and the energy business require the prediction of solar energy (radiation) from a few seconds up to one week in advance. To deal with prediction shortcomings, various solar radiation prediction methods have been used. Predictive data mining offers variety of methods for solar radiation predictions where Artificial Neural Network is one of the reliable and accurate methods. A systematic review of literature was conducted and identified 24 papers that discuss Artificial Neural Network for solar systems design and solar radiation prediction. The Artificial Neural Network techniques were employed for designing solar systems and predicting solar radiations to assess current literature on the basis of prediction accuracy and inadequacies. Specific inclusion and exclusion criteria in two distinct rounds were applied to determine the most relevant studies for our research goal. Further, it is observed from the result of this study that Artificial Neural Network gives good accuracy in terms of prediction error less than 20%. The accuracy of solar radiation prediction models is found to be dependent on input parameters and architecture type algorithms utilized. Therefore, Artificial Neural Network as compared to other empirical models is capable to deal with many input meteorological parameters, which make it more accurate and reliable.
S S Chandel - One of the best experts on this subject based on the ideXlab platform.
-
solar radiation prediction using Artificial Neural Network techniques a review
Renewable & Sustainable Energy Reviews, 2014Co-Authors: Amit Kumar Yadav, S S ChandelAbstract:Abstract Solar radiation data plays an important role in solar energy research. These data are not available for location of interest due to absence of a meteorological station. Therefore, the solar radiation has to be predicted accurately for these locations using various solar radiation estimation models. The main objective of this study is to review Artificial Neural Network (ANN) based techniques in order to identify suitable methods available in the literature for solar radiation prediction and to identify research gaps. The study shows that Artificial Neural Network techniques predict solar radiation more accurately in comparison to conventional methods. The prediction accuracy of ANN models is found to be dependent on input parameter combinations, training algorithm and architecture configurations. Further research areas in ANN technique based methodologies are also identified in the present study.
Xiaoqing Zhang - One of the best experts on this subject based on the ideXlab platform.
-
prediction of output power with Artificial Neural Network using extended datasets for stirling engines
Applied Energy, 2020Co-Authors: Han Jiang, Anas A Rahman, Xiaoqing ZhangAbstract:Abstract A Stirling engine is inherently complex in structure and manufacturing process, and its operating mechanism involves thermal-mechanic-electronic (electromagnetic) coupling and complicated nonlinear losses. Therefore, it is difficult to accurately predict the performances by theoretical analysis during the design of a Stirling engine. In the present study, the Artificial Neural Network is used to predict the output power of Stirling engines. Using extended datasets including the isothermal analytical data and the experimental data, two accuracy-improved Artificial Neural Network models that are able to predict the output power for two typical Stirling engine prototypes are developed using Matlab to improve the prediction ability of normal Artificial Neural Network models only based on experimental data. Compared to the normal Artificial Neural Network model, the two improved Artificial Neural Network models achieve maximum improvements of over 50% and 20% in average prediction error for Ford 4-215 engine and General Motors 4L23 engine, respectively. The results also demonstrate that the two improved Artificial Neural Network models have better robustness to the quality of experimental data samples. This research provides an effective approach based on the Artificial Neural Network methodology to predict the performances of Stirling engines.
Atika Qazi - One of the best experts on this subject based on the ideXlab platform.
-
The Artificial Neural Network for solar radiation prediction and designing solar systems: A systematic literature review
Journal of Cleaner Production, 2015Co-Authors: Atika Qazi, A. Wadi, Ram Gopal Raj, Hina Fayaz, Nasrudin Abd Rahim, Waleed Ahmed KhanAbstract:Solar energy generated by sunlight has a non-schedulable nature due to the stochastic environment of meteorological conditions. Hence, power system control and the energy business require the prediction of solar energy (radiation) from a few seconds up to one week in advance. To deal with prediction shortcomings, various solar radiation prediction methods have been used. Predictive data mining offers variety of methods for solar radiation predictions where Artificial Neural Network is one of the reliable and accurate methods. A systematic review of literature was conducted and identified 24 papers that discuss Artificial Neural Network for solar systems design and solar radiation prediction. The Artificial Neural Network techniques were employed for designing solar systems and predicting solar radiations to assess current literature on the basis of prediction accuracy and inadequacies. Specific inclusion and exclusion criteria in two distinct rounds were applied to determine the most relevant studies for our research goal. Further, it is observed from the result of this study that Artificial Neural Network gives good accuracy in terms of prediction error less than 20%. The accuracy of solar radiation prediction models is found to be dependent on input parameters and architecture type algorithms utilized. Therefore, Artificial Neural Network as compared to other empirical models is capable to deal with many input meteorological parameters, which make it more accurate and reliable.
Amit Kumar Yadav - One of the best experts on this subject based on the ideXlab platform.
-
solar radiation prediction using Artificial Neural Network techniques a review
Renewable & Sustainable Energy Reviews, 2014Co-Authors: Amit Kumar Yadav, S S ChandelAbstract:Abstract Solar radiation data plays an important role in solar energy research. These data are not available for location of interest due to absence of a meteorological station. Therefore, the solar radiation has to be predicted accurately for these locations using various solar radiation estimation models. The main objective of this study is to review Artificial Neural Network (ANN) based techniques in order to identify suitable methods available in the literature for solar radiation prediction and to identify research gaps. The study shows that Artificial Neural Network techniques predict solar radiation more accurately in comparison to conventional methods. The prediction accuracy of ANN models is found to be dependent on input parameter combinations, training algorithm and architecture configurations. Further research areas in ANN technique based methodologies are also identified in the present study.
