Design Of Solar Energy Systems Research Articles

National-scale development and calibration of empirical models for predicting daily global solar radiation in China

Accurate global solar radiation (Rs) information is pivotal to the design and management of solar energy systems. Nevertheless, the expensive devices for Rs measurements make Rs data always unavailable in many regions around the world. The empirical models that predict Rs using other widely available climatic variables are feasible alternatives when Rs measurements are unavailable. However, the parameters of empirical models are site-specific and always need local calibration. In this context, the present study firstly developed a novel empirical model for accurately predicting Rs at the national scale in China, and then compared the new model with nineteen locally calibrated empirical models that have been largely reported in prior studies, including seven sunshine-based, nine temperature-based, and three complex empirical models. Daily Rs and other meteorological data during 1994–2016 from 96 weather stations in China were used to calibrate/develop and assess the models. The results showed that the newly proposed C4 model generally offered the best prediction accuracy among the models, with average MAE of 1.69 MJ m−2 d−1, RRMSE of 16.2% and NS of 0.895, which can be recommended as the optimal model for predicting Rs in China. The models reviewed and developed in this study improved the prediction accuracy of Rs, which can provide crucial information for the design and implementation of solar photovoltaic and thermal systems.

Empirical model development for the estimation of clearness index usingmeteorological parameters

The clearness index is an indispensable parameter required for the design and analysis of solar energy systems. In the absence of measured values for a specific location, the clearness index can be estimated from other measured meteorological variables. In this study three meteorological parameters, sunshine hours, monthly mean values of the temperature difference ($\Delta$T), and cloudiness, are used to develop empirical models for the estimation of clearness index. The empirical models are developed for five major cities in Pakistan (Karachi, Multan, Lahore, Islamabad, and Quetta). For empirical model development, long-term data (1991 to 2010) of monthly average clearness index, sunshine hours, average daily minimum and maximum temperatures, and cloudiness have been used. The accuracy of the models has been tested by statistical indicators that include mean percentage error (MPE), coefficient of determination (R$^2$), mean absolute relative error (MARE), mean bias error (MBE), and root mean square error (RMSE). The error analysis revealed that the proposed models are suitable for the estimation of the clearness index. It is also concluded that multiple regression models give better estimates of clearness index for all the stations (0.80 $\leq$ R$^{2}$ $\leq$ 0.86) compared to single parameter model and therefore are recommended. The study indicated that clear sky conditions prevail throughout the months at all the investigated sites (0.58 $\leq$ K$_T$ $\leq$ 0.68), which is a good indicator for solar energy utilization. The statistical indicators also suggest that multilinear regression model M-3 gives a better representation of the climate system and using three parameters reduces the uncertainties in the developed model.

Developing of the new models in solar radiation estimation with curve fitting based on moving least-squares approximation

Recently, serious progress has been made in solar energy applications in developed and developing countries. Solar radiation on a horizontal surface is the basic parameter required for the design of solar energy systems and for evaluating the system performance. Therefore, solar radiation the exact determination of the amount of land in different latitudes on the earth's surface is of great importance in many solar energy applications. In this study, Angstorm coefficients were determined by Moving Least Squares Approximation (MLSA). Three different models were obtained by using the moving least squares method. In this study, new empirical models were developed for determining the monthly average daily global solar radiation on a horizontal surface for Antalya. The developed models were compared with the models in the literature by using different error analysis methods. The statistical compatibility of the investigated models was tested and the model closest to the measurements was determined. Although, this study concluded that the suggested methods are applicable to estimate the monthly average daily diffuse radiation on a horizontal surface for selected region, it has been observed that the performance of these models varies according to years and the error analysis test used. If the results are generally evaluated the developed linear model showed the best performance to estimate the global solar radiation on a horizontal surface for Antalya. Also, among the models used in the literature, the Togrul model showed the best performance for selected region.

Evaluation of temperature-based machine learning and empirical models for predicting daily global solar radiation

Accurate global solar radiation data are fundamental information for the allocation and design of solar energy systems. The current study compared different machine learning and empirical models for global solar radiation prediction only using air temperature as inputs. Four machine learning models, e.g., hybrid mind evolutionary algorithm and artificial neural network model, original artificial neural network, random forests and wavelet neural network, as well as four empirical temperature-based models (Hargreaves-Samani model, Bristow-Campbell model, Jahani model, and Fan model) were applied for prediction of daily global solar radiation in temperate continental regions of China. The results indicated the hybrid mind evolutionary algorithm and artificial neural network model provided better estimations, compared with the existing machine learning and empirical models. Thus, the temperature-based hybrid model is highly recommended to predict global solar radiation in temperate continental regions of China when only air temperature data are available. Combining the hybrid model with future air temperature forecasts, we can get the accurate information of future solar radiation, which is of great importance to management and operation of solar energy systems.

Photovoltaic-Thermal solar energy system design for dairy industry

In 21th century demand for energy is rapidly increasing as the population is increased. To provide desirable living conditions and satisfy the food needs of overpopulated humanity getting harder and harder due to the limited energy and nutrient resources. Nutrient sources pass through many stages such as heating, cooling, drying and freezing which includes thermal and electrical energy needs before being presented to the consumer in the food industry. In the current situation, these two main energy requirements are largely met by fossil fuels which are not efficient, non-sustainable and not user friendly in terms of resource management. In this context, the possibilities of usage Photovoltaic-Thermal (PV/T) solar energy systems which will be able to eliminate these disadvantages, use solar energy as a source and can meet the thermal and electrical requirements of the facility continuously has been investigated and, a system was designed which can meet the energy needs according to different scenarios for different processes. It is aimed to produce food in low carbon-emission facilities by meeting both electrical and thermal energy from the solar energy which is a renewable and clean energy source.

Multi-region Modeling of Daily Global Solar Radiation with Artificial Intelligence Ensemble

Solar radiation data are crucial for the design and evaluation of solar energy systems, climatic studies, water resources management, estimating crop productivity, etc. As so, for locations where direct measurements are not available, reliable models may be developed to estimate solar radiation from more readily available data. In this study, two artificial intelligence (AI)-based models including artificial neural network and adaptive neuro-fuzzy inference systems, three temperature-based empirical models including Meza–Varas, Hargreaves–Samani, and Chen, and a conventional multi-linear regression (MLR) model were employed for multi-region daily global solar radiation estimation for Iraq. To ensure appropriate selection of input variables, sensitivity analysis was conducted to determine the dominant parameters. Finally, two ensemble approaches, neural average ensemble and simple average ensemble, were applied to improve the performance of the single models. For this purpose, daily meteorological data of maximum temperature $$ \left({T_{\hbox{max} } } \right) $$ , minimum temperature $$ \left({T_{\hbox{min} } } \right) $$ , mean temperature $$ \left({T_{\text{mean}} } \right) $$ , relative humidity $$ \left({R_{\text{H}} } \right) $$ , and wind speed $$ \left({U_{2} } \right) $$ were obtained from January 2006 to December 2016 from four major cities in Iraq representing, north, west, south, and east regions. The results revealed that temperatures $$ \left({T_{\hbox{max} } , \;T_{\text{mean}} ,\; T_{\hbox{min} } } \right) $$ and relative humidity are the dominant parameters. While temperature-based empirical models and MLR model could be employed to achieve reliable results, AI-based models are superior in performance to other models. Also promising improvement in daily global solar radiation modeling could be achieved by model ensemble. The results of this study affirmed that the provided ensemble approaches can increase the performance of single models up to 19.19%, 7.59%, and 16.81% in training, validation, and testing steps, respectively.

Potential of kernel-based nonlinear extension of Arps decline model and gradient boosting with categorical features support for predicting daily global solar radiation in humid regions

The knowledge of global solar radiation is of vital importance for the design and use of solar energy systems. This study evaluated the potential of two new powerful machine learning models, i.e., kernel-based nonlinear extension of Arps decline model and gradient boosting with categorical features support, for accurately estimating daily global solar radiation in humid regions. These two models were also compared with the multilayer perceptron, M5 model tree, random forest and multivariate adaptive regression spline models, using five input combinations of daily meteorological data during 2001–2015 from four weather stations in the (sub)tropical humid regions of South China. The results showed that, when lack of complete meteorological data, machine learning models using the ratio of actual and theoretical sunshine duration, maximum and minimum temperatures obtained satisfactory daily global solar radiation estimates. Generally, the kernel-based nonlinear extension of Arps decline model offered the best prediction accuracy among the studied models, followed by the gradient boosting with categorical features support. The multilayer perceptron model exhibited the smallest average percentage increase in the root mean square error during testing over the training values, followed by the kernel-based nonlinear extension of Arps decline model. Both the kernel-based nonlinear extension of Arps decline model and gradient boosting with categorical features support were successfully applied to develop general models for daily global solar radiation prediction (differences in root mean square error <5% compared with local models). The gradient boosting with categorical features support and multilayer perceptron exhibited much smaller computational time for both local and general models. Comprehensively considering the accuracy, stability and computational time, both the kernel-based nonlinear extension of Arps decline model and gradient boosting with categorical features support are recommended for predicting daily global solar radiation in the humid regions of China.

Thermal performance of high-altitude solar powered scientific balloon

High-altitude solar powered scientific balloon can be powered by thin-film solar panel mounted on the balloon. The temperature change of solar panel might have significant influence on the thermal performance of the balloon, which is closely related to the superheat and overpressure of balloon. The thermal model of solar powered scientific balloon was presented to investigate the thermal performance and compare with unpowered balloon. A user define function program in computational fluid dynamic software was developed based on the model. The effects of layout parameter and area of solar panel on the thermal performance of solar powered balloon were also analyzed. The results show that the temperature of envelope and internal helium of solar powered balloon is much higher than that of unpowered balloon during the daylight, and the maximum velocity of internal helium is decreased with the existence of solar panel. Moreover, the increase of the height and area of solar panel would result the raise of temperature and pressure of internal Helium, but the helium velocity and the flow distribution were hardly changed. The present work may be used to guide the design of solar energy system and the thermal control of scientific balloon.

English

In this study, working parameters of the system consisting of air heaters and dryer with solar energy of 0.20 m2 with a space of 0.72 m2 and 10 pieces in the cabin were investigated experimentally. Average atmospheric air temperature and relative humidity values during the dry daylight hours were measured at 49°C and 35%, respectively. As a result of the examination of the test results, the atmospheric air temperature of 56°C was reached in the dryer depending on the solar radiation and environmental conditions. In addition, the accuracy of solar radiation data in the design of solar energy systems was extremely important. Six different models and MBE, RMSE and t-stat comparison methods were used to calculate the amount of global solar radiation on the horizontal surface. The best results were given by Model-3 with values of MBE=0.130 and RMSE=1.401, while the best result from t-statistics was with t-ist=0.282 with Model-5. When the equations are evaluated statistically, it has been shown that the solar-powered family type shelf dryer and tomato drying conditions can be used for Aksaray province. Key words: Solar energy, shelf dryer, tomato, solar radiation, relative humidity.

Design of Hybrid Solar Wind Energy System in a Microgrid with MPPT Techiques

&lt;p&gt;DC Microgrid is one feasible and effective solution to integrate renewable energy sources as well as to supply electricity. This paper proposes a DC microgrid with enhanced Maximum Power Point Tracking (MPPT) techniques for wind and solar energy systems. In this paper, the PV system power generation is enhanced by introducing a two-model MPPT technique that combines incremental conductance and constant voltage MPPT algorithms. Also, for the Wind Energy Conversion System (WECS) with pitch angle controlling technique, an Optimal Power Control MPPT technique is added. The Space Vector Pulse Width Modulation technique is introduced on grid side converter to improve the supply to the grid. The performance of proposed system is analyzed and the efficiency obtained with these methods is enhanced as compared with the previous methods.&lt;/p&gt;

Resolution of the cloud enhancement problem for one-minute diffuse radiation prediction

For design and simulation of solar energy systems, quality information about all components of solar irradiance is crucial. In cases when only global irradiance measurements are available, separation models are a useful method to estimate DNI and diffuse irradiation. Several of such models have been developed since the 1960s, most of them aiming to deliver estimates in hourly resolution. For higher data resolution, such as in minute data, those models are not able to describe fast transient and cloud enhancement phenomena commonly observed in data with smaller time-steps. This paper proposes an adaptation of the BRL separation model, making it capable of delivering more precise irradiance estimates for higher resolution data. Two models result from this adaptation: one for Brazil and other for Australia. The proposed models yield a more precise DNI and diffuse fraction estimates to their respective countries, compared to other separation models commonly used in the technical literature. For example, using the recommended Combined Performance Index (CPI) as a single statistical indicator, the proposed model yields DNI estimates with CPI from 230 to 350% for Australia, and from 270 to 800% for Brazil, while the Engerer model, recently recommended as a ‘‘quasi-universal” 1-min separation model, yields DNI estimates with CPI from 500 to 700% for Australia, and from 600 to 1800% for Brazil.

Design and Development of Hybrid Wind and Solar Energy System for Power Generation

People have harvested energy from the wind for quite a long time. Sails for boats, windmills for crushing grain or pumping water are a couple of illustrations. There is recharged intrigue now in energy harvesting from the wind, since it offers a contamination free method for producing power on a huge scale. The framework that fulfils the transformation of wind energy to power is known as a wind turbine. Presently a-days power is turning out to be rare. So in future, the renewable assets will be utilized to produce power. Indeed, even these days, 5% to 10% of the power is produced from wind and solar. In the meantime, every single work of the person is computerized by machines however the power generation is not up to the level. Above being the case, a hybrid wind and solar energy system was developed for the generation of power. The model is a combination of both horizontal axis wind turbine and solar panels where the blades of the wind turbine are being made by PVC pipes and the solar panel tiles are fitted along with the turbine blades. The project describes the modelling of two emerging electricity systems based on renewable energy: photovoltaic and wind power. The powers produced from both the sources were combined together using a Charge controller and then connected to an inverter. Finally, this power was fed to the residential load. The prototype exhibits an assessment of joined solar and wind system for house hold prerequisites, for example, lighting, fan, and so on. Moreover, wind turbine can be operated at lower wind speeds thus increasing the efficiency of the total system. The developed model of wind turbine was worked for the review to fulfill the vitality necessities on the houses. This model was tried alongside photovoltaic boards to check the power yield and the proficiency of the aggregate plan. A comparison between the suggested system and the proposed system proved that the proposed system had many advantages over the conversional system. The performance of the wind turbine was improved during the production process. At the point when the turbine is utilized with photovoltaic boards, the execution was enhanced fundamentally.

Solar energy utilization patterns for different district typologies using multi-objective optimization: A comparative study in China

Abstract Currently, solar energy technologies are in the stage of intensive development. With booming solar industry, there is a challenge to seek for appropriate solar energy solutions for different district typologies. This paper presents a comparative study on solar energy utilization patterns for different types of districts located in Kunming, China. Four district typologies are investigated: residential district (RD), official district (OD), commercial district (CD) and industrial district (ID). For each district, the objective is to identify such solar energy utilization patterns that result in an optimal design and operation of solar energy system. The optimum system is defined and obtained through minimizing life cycle CO 2 emissions and costs as well as maximizing exergy efficiency. To that end, a multi-objective optimization approach based on Genetic Algorithm is proposed. The results of the case study suggest that solar energy is to represent 3.7%, 5.9%, 7.9% and 21.4% of annual energy consumption for RD, OD, CD and ID, respectively. For each district, the portfolio of solar energy technologies is different. Solar power systems factually contribute to the energy supply of ID only. The final work aims at investigating the effects of different solar energy parameters on the solar utilization patterns for these districts.

Photurgen: The open source software for the analysis and design of hybrid solar wind energy systems in the Caribbean region: A brief introduction to its development policy

Abstract Hybrid Renewable Energy Systems (HRES) use multiple renewable resources such as hydro, solar and wind collaboratively to produce energy that can meet a defined load demand continuously. Their combination can lead to the improvement in the systems efficiency and overall reliability. However, the level of penetration of HRES in the Caribbean region is less than its expected potential. The constraints generated by their complexity and the costly access to useful energy planning tools is a limitation to their implementation. Therefore, in collaboration with the Alternative Energy Research Group, UWI Mona, we develop a free Linear Optimization software, Photurgen, for the design and analysis of hybrid solar-wind systems within the Caribbean region. Solar-wind hybrid systems are simulated based on historic climatological resources and instantaneous load consumption data, providing the user with graphics and advice for their optimal configuration. This paper introduces the first version of Photurgen and its associated development policies. This tool is one simple solution to be applied to increase the rate of autonomous and grid-tied households within the region, with Jamaica being its experimental location.

Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening

Predicting the performance of solar water heater (SWH) is challenging due to the complexity of the system. Fortunately, knowledge-based machine learning can provide a fast and precise prediction method for SWH performance. With the predictive power of machine learning models, we can further solve a more challenging question: how to cost-effectively design a high-performance SWH? Here, we summarize our recent studies and propose a general framework of SWH design using a machine learning-based high-throughput screening (HTS) method. Design of water-in-glass evacuated tube solar water heater (WGET-SWH) is selected as a case study to show the potential application of machine learning-based HTS to the design and optimization of solar energy systems.

Design of solar energy system in food manufacturing environment

Abstract Global climate change has driven many manufacturing organizations to consider renewable energy as an alternative source of power for their operations. However, the instability and unpredictability of renewable energy has made it difficult to design an optimal solution. This is particularly critical for food manufacturing which is constrained by timing and seasonable fluctuations. This paper describes a feasibility study of offsetting grid supplied power by local generation using a roof top photovoltaic system. The study shows that the company can significantly reduce their carbon emitting energy usage by a 100 kW photovoltaic solar system that helps to reduce the annual electricity consumption by 39%. The system sizing methodology has taken into account the load profile of the food manufacturing plant and seasonal solar energy availability with the constrain to consume all solar power immediately.

Mixed integer linear programming for the design of solar thermal energy systems with short-term storage

In this paper a mixed integer linear programming (MILP) model is developed to facilitate the design of a solar domestic hot water system. The MILP model does so by optimising the area of roof mounted flat plate solar thermal collectors and the volume of thermal energy storage that are required to minimise the annual capital and operational cost of the overall energy system. A new formulation of the MILP is presented in which the model is decomposed into two sub-modules that are iteratively solved. One module determines the optimal sizing of the solar thermal collectors and storage tank. The other module optimises the hourly energy production of the solar collectors based on the impact that the collector inlet stream temperature has on the collector efficiency. This iterative formulation is compared to both a conventional MILP formulation and a dynamic simulation model to determine if the iterative formulation results in a design tool that is able to accurately represent the dynamic behaviour of a solar domestic hot water system, while maintaining the computational tractability and scalability of the model. The novel iterative MILP formulation is applied to a case study of an apartment building in Rheinfelden, Switzerland. The results indicate that conventional MILP formulations overestimate the amount of energy that solar thermal collectors are able to produce, resulting in an oversizing of the required thermal storage tank. However, the iterative MILP model more accurately predicts the energy production of the solar collector, leading to the more realistic sizing of the component technologies. This result is important when evaluating the financial feasibility of solar thermal collector systems in comparison to other renewable generation technologies, as an overestimation of the generation potential of solar thermal collectors may result in an incorrect assessment of their investment attractiveness.

Evaluation of some mathematical models of solar radiation received by a ground collector

Purpose Using modeling approaches, this paper aims to propose different mathematical models for estimating the different components of the solar radiation as well as the received solar energy by a collector. Design/methodology/approach In this article, the authors consider three mathematical models to estimate the solar radiation captured at ground level by a solar collector. These models are Capderou model, Liu &amp; Jordan model and R.sun model. In the context of the design of experiments, we performed measurements of solar radiation received by a collector using a pyranometer. The obtained measurements were compared with the three mathematical models. Findings The comparison enabled the subsequent evaluation to determine the most appropriate model that best fit for our region. As a result, the Capderou model reveals to be the most suitable for our region. Originality/value Estimation of solar radiation at ground level (received by a collector) is of paramount importance for the design and optimization of solar energy systems. Nevertheless, many factors influence the amount of energy received by a collector situated at a ground, such as the longitude of the location, latitude, altitude, tilt collector orientation, temperature and humidity of the environment, wind speed, etc. Because of the complex influence of these parameters, the received solar radiation by the collector is a dynamical and a random process.

A novel free-form Cassegrain concentrator for PV/T combining utilization

The conventional imaging solar concentrators normally obtain a Gaussian distribution on the receiving surface. The aperture size of a thermal receiver (TR) is always limited primarily to the high concentration area, discarding the edge part to be lost. The current study proposes a novel free-form Cassegrain concentrator (FFCC) for PV/T combining utilization. The remaining heat flux by the edge of focal spot is directed by the secondary mirror for electric generation. The profile of secondary reflector is constructed by the geometric construction method (GCM), aiming at achieving high concentration for the TR and uniform flux distribution for the PV receiver (PVR). The energy transmission characteristics of FFCC, including the incident vectors and heat flux distribution on the TR/PVR are both investigated. The effects of changing the receiving locations, PV/T dividing ratio, etc. are analyzed. The sun size/solar conic angular is also considered for practical use. The GCM method and simulation results in this article can provide a reference for the PV/T solar energy system and free form surface design.

Prediction of solar radiation for solar systems by using ANN models with different back propagation algorithms

Global solar radiation (GSR) is an essential parameter for the design and operation of solar energy systems. Long-standing records of global solar radiation data are not available in many places because of the cost and maintenance of the measuring instruments. The major objective of this work is to develop an ANN model for accurately predicting solar radiation. Two ANN models with four different algorithms are considered in the present study. Meteorological data collected for the last 10 years from five different locations across India have been used to train the models. The best ANN algorithm and model are identified based on minimum mean absolute error (MAE) and root mean square error (RMSE) and maximum linear correlation coefficient (R). Further, the present study confirms that prediction accuracy of the ANN model depends on the complete set of data being used for training the network for the intended application. The developed ANN model has a low mean absolute percentage error (MAPE) which ascertains the accuracy and suitability of the model to predict the monthly average global radiation so as to design or evaluate solar energy installations, where the meteorological data measuring facilities are not in place in India.