- 标题
- 摘要
- 关键词
- 实验方案
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Solar radiation exergy and quality performance for Iraq and Turkey
摘要： The present study is conducted with two primary objectives: First, a new formulation for the maximum efficiency of the solar radiation conversion is developed by considering the radiative energy transfer between two surfaces at different temperatures for a constant volume system. Second, a new methodology is introduced for estimating the exergy value of the monthly average daily horizontal global radiation, including many parameters, such as monthly average daily value of the horizontal extraterrestrial radiation, the number of sunny hours, the day length, the mean temperature and the mean wind velocity. Seven statistical parameters are used to validate the accuracy of all models. The results of the two new models are found to be more reliable than the results obtained from other models. This study, which was conducted for four locations in Iraq and Turkey. The findings would help in predicting the maximum availability of solar radiation based on weather parameters.
关键词： empirical models,solar radiation,solar radiation exergy
更新于2025-09-23 15:22:29
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Photovoltaic power forecast using empirical models and artificial intelligence approaches for water pumping systems
摘要： The solar water pumping system is one of the brightest applications of solar energy for its environmental and economic advantages. It consists of a photovoltaic panel which converts solar energy into electrical energy to operate a DC or AC motor and a battery bank. The photovoltaic power fluctuation can affect the water pumping system performances. Thus, the photovoltaic power prediction is very important to ensure a balance between the produced energy and the pump requirements. The prediction of the generated power depends on solar irradiation and ambient temperature forecasting. The purpose of this study was to evaluate various methodologies for weather data estimation namely: the empirical models, the feed forward neural network and the adaptive neuro-fuzzy inference system. The simulation results show that the ANFIS model can be successfully used to forecast the photovoltaic power. The predicted energy was used for the solar water pumping management algorithm.
关键词： water pumping system management,photovoltaic power,empirical models,forecast,artificial neural network,neuro fuzzy inference system
更新于2025-09-23 15:19:57
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Predictive Modeling Of Laser Assisted Hybrid Machining Parameters Of Inconel 718 Alloy Using Statistical And Artificial Neural Network
摘要： Laser assisted hybrid machining being researched in past decade on various difficult to machine materials to improve the machinability. Predictive modeling approaches such as response surface method (RSM) and artificial neural network (ANN) are widely applied for model development. However, no reported work using RSM and ANN approaches to predict the relationship between the experimental variables (speed, feed, laser power and beam apporach angle) on surface roughness Ra (μm). Furthermore, coefficient of correlation (R2), root mean square error (RMSE) and model predictive error (MPE) are considered as a performance measures for their effectiveness. The results show that the ANN model estimates the machinability indices with high accuracy with a limited number of experiments compared to the response surface model. From the comparative study, ANN model is found to be capable for better prediction of response than the RSM model. ANN model provides a maximum precision benefit of 10% for surface roughness Ra (μm) compared with RSM model. Also the calculated Pearson correlation coefficient showed a robust relationship between the laser beam angle and Ra, surface roughness followed by the speed.
关键词： response surface methodology,surface roughness,neural network,empirical models
更新于2025-09-16 10:30:52
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Estimation and validation of daily global solar radiation by day of the year-based models for different climates in China
摘要： Day of the year-based (DYB) models can achieve great accuracy in daily global solar radiation estimation without specific meteorological elements. Many empirical models (EMs) and machine learning (ML) methods have been proposed for DYB models. However, the number of their comparative studies based on diverse climates is limited. In this study, a grand total of 14 DYB models are established to estimate daily global solar radiation based on measured data from 1994 to 2015 at 35 meteorological stations in six climate zones of China. Detailed tasks are as follows: (1) Seven EMs and seven ML models are trained for solar radiation estimation. (2) A new EM and two novel ML models are proposed, i.e. hybrid 3th order polynomial and sine wave model, adaptive neuro-fuzzy inference system (ANFIS) optimized by chaotic firefly algorithm (CFA) and ANFIS optimized by whale optimization algorithm with simulated annealing and roulette wheel selection (WOASAR). (3) Four statistical indicators are utilized to compare those models, and the best model for each station is decided. (4) We discuss the model parameters and climate variances of six specific stations in different climate zones. The comparison results demonstrate superb estimation precision and climate adaptability of the newly proposed models.
关键词： Day of the year,Empirical models,Global solar radiation estimation,Machine learning
更新于2025-09-04 15:30:14