An Empiric-Stochastic Approach, Based on Normalization Parameters, to Simulate Solar Irradiance

This paper presents a methodology to estimate solar irradiance using an empiric-stochastic approach, which is based on the computation of normalization parameters from the solar irradiance data. For this study, the solar irradiance data were collected in a weather station during a year. Posttreatment included a trimmed moving average to smooth the data, the performance of a fitting procedure using a simple model to recover normalization parameters, and the estimation of a probability density, which evolves along the daytime, by means of a kernel density estimation method. The normalization parameters correspond to characteristic physical variables that allow us to decouple the short- and long-term behaviors of solar irradiance and to describe their average trends with simple equations. The normalization parameters and the probability densities allowed us to build an empiric-stochastic methodology that generates an estimate of the solar irradiance. Finally, in order to validate our method, we had run simulations of solar irradiance and afterward computed the theoretical generation of solar power, which in turn had been compared with the experimental data retrieved from a commercial photovoltaic system. Since the simulation results show a good agreement with the experimental data, this simple methodology can generate the synthetic data of solar power production and may help to design and test a photovoltaic system before installation.

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A Transportable Solar Power Generator

International Journal of Research in Science ◽

10.24178/ijrs.2017.3.2.06 ◽

2017 ◽

Vol 3 (2) ◽

pp. 6

Author(s):

Azzeddine Ferrah ◽

Fatima Yaqoob ◽

Mariam Ismaeil ◽

Dana Khalil ◽

Amal Abdelrahman ◽

...

Keyword(s):

Solar Energy ◽

Solar Irradiance ◽

Engineering Students ◽

Solar Power ◽

Geographic Location ◽

Photovoltaic System ◽

Project Work ◽

Energy Output ◽

Rural Villages ◽

Pv Generator

Abstract—This paper presents the design of a 2kW portable photovoltaic (PV) stand-alone facility (PV generator) that converts directly solar irradiance into electricity for immediate use or storage. The project aims to build a stand-alone solar power source for use in rural villages, mountainous and remote areas that are distant from the national grid. It can also be very useful for powering camping tents, fishing boats, small farms, and greenhouses. Equally, it could be used for disaster stricken areas and during power outages. However, the proposed generator will be more suited for camping trips that Emiratis take almost in weekly basis. The paper will focus on presenting the main features of the designed prototype. It will also investigate the performance of the proposed stand-alone PV generator. Parameters investigated include geographic location, climate condition, solar irradiance, load consumption, ambient temperature, array voltage, battery voltage, and energy output from the array. The work presented is based entirely on the work carried out by final year electrical engineering students, during their capstone design project. The project work, presented, is a manifestation of the students learning during earlier semesters. It puts into practice the application of solar energy technology, that the student learned in his course on renewable energy systems. Index Terms—Solar energy,transportable solar system,stand-alone system,PV,photovoltaic system.

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Statistical Moments Approach in Grid-Connected Photovoltaic System Performance Evaluation

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.785.616 ◽

2015 ◽

Vol 785 ◽

pp. 616-620

Author(s):

M.B.M. Juhari ◽

A. Samizee ◽

E.A. Azrulhisham ◽

S. Nizam

Keyword(s):

Power Distribution ◽

Solar Irradiance ◽

Solar Power ◽

Photovoltaic System ◽

Statistical Moments ◽

Empirical Distributions ◽

Photovoltaic Power ◽

Density Function Approximation ◽

First Four Moments ◽

The Relationship

Solar energy is considered as one of the solution to the worldwide depletion of fossil fuel resources as well as the economic alternatives in protecting the atmosphere from the adverse consequences of global warming. Nevertheless solar power is often criticized because the output power generated is variable and virtually uncontrollable. Potential analysis on introduction of photovoltaic system at particular site however requires the knowledge of solar irradiance and photovoltaic power distributions. This paper will focus on the possibility in applying statistical moments approach in solar irradiance and photovoltaic power distribution evaluation. Applying the first to forth statistical moments, the density function approximation of the parameters from 5MW grid connected Photovoltaic system were evaluated using the Pearson system. This method is based on the relationship between the first four moments of the distribution where the probability distribution is estimated by equating their theoretical moments with the moments of empirical distributions. Application of various statistical moments has the advantage in estimating the potential of photovoltaic system in view of dynamic changes of skewness and kurtosis coefficients of solar power irradiance distributions.

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Using The Fully Modified M Method In Estimating The Effect Of Cultivated Area On Barley Production In Iraq For The Period 1970-2018

Iraqi Journal of Economic Sciences ◽

10.31272/ijes2020.66.6 ◽

2020 ◽

Vol 2020 (66) ◽

pp. 101-110

Author(s):

. Azhar Kadhim Jbarah ◽

Prof Dr. Ahmed Shaker Mohammed

Keyword(s):

Regression Model ◽

Moving Average ◽

Estimation Method ◽

Estimation Methods ◽

Autoregressive Moving Average ◽

Moving Average Models ◽

Error Terms ◽

Relationship Of ◽

The Relationship ◽

Barley Crop

The research is concerned with estimating the effect of the cultivated area of barley crop on the production of that crop by estimating the regression model representing the relationship of these two variables. The results of the tests indicated that the time series of the response variable values is stationary and the series of values of the explanatory variable were nonstationary and that they were integrated of order one ( I(1) ), these tests also indicate that the random error terms are auto correlated and can be modeled according to the mixed autoregressive-moving average models ARMA(p,q), for these results we cannot use the classical estimation method to estimate our regression model, therefore, a fully modified M method was adopted, which is a robust estimation methods, The estimated results indicate a positive significant relation between the production of barley crop and cultivated area.

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Seeking the Good Fitting Procedure of PALS Polymer Spectra

Materials Science Forum ◽

10.4028/www.scientific.net/msf.607.64 ◽

2008 ◽

Vol 607 ◽

pp. 64-66

Author(s):

Nicolas Laforest ◽

Jérémie De Baerdemaeker ◽

Corine Bas ◽

Charles Dauwe

Keyword(s):

Experimental Data ◽

Low Temperature ◽

Positron Annihilation ◽

Physical Meaning ◽

Lifetime Measurements ◽

Positron Annihilation Lifetime ◽

Fitting Procedure ◽

Fitting Procedures ◽

Blob Model

Positron annihilation lifetime measurements on polymethylmethacrylate (PMMA) at low temperature were performed. Different discrete fitting procedures have been used to analyze the experimental data. It shows that the extracted parameters depend strongly on the fitting procedure. The physical meaning of the results is discussed. The blob model seems to give the best annihilation parameters.

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A physical downscaling algorithm for the generation of high-resolution spatiotemporal solar irradiance data

Solar Energy ◽

10.1016/j.solener.2021.01.036 ◽

2021 ◽

Vol 216 ◽

pp. 508-517

Author(s):

Grant Buster ◽

Michael Rossol ◽

Galen Maclaurin ◽

Yu Xie ◽

Manajit Sengupta

Keyword(s):

High Resolution ◽

Solar Irradiance ◽

Irradiance Data

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Building the Sun4Cast System: Improvements in Solar Power Forecasting

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-16-0221.1 ◽

2018 ◽

Vol 99 (1) ◽

pp. 121-136 ◽

Cited By ~ 23

Author(s):

Sue Ellen Haupt ◽

Branko Kosović ◽

Tara Jensen ◽

Jeffrey K. Lazo ◽

Jared A. Lee ◽

...

Keyword(s):

Decision Making ◽

Value Chain ◽

Solar Irradiance ◽

Collaborative Design ◽

Solar Power ◽

Spatial Scales ◽

Weather Prediction ◽

Solar Array ◽

Solar Power Forecasting ◽

Power Forecasting

Abstract As integration of solar power into the national electric grid rapidly increases, it becomes imperative to improve forecasting of this highly variable renewable resource. Thus, a team of researchers from the public, private, and academic sectors partnered to develop and assess a new solar power forecasting system, Sun4Cast. The partnership focused on improving decision-making for utilities and independent system operators, ultimately resulting in improved grid stability and cost savings for consumers. The project followed a value chain approach to determine key research and technology needs to reach desired results. Sun4Cast integrates various forecasting technologies across a spectrum of temporal and spatial scales to predict surface solar irradiance. Anchoring the system is WRF-Solar, a version of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model optimized for solar irradiance prediction. Forecasts from multiple NWP models are blended via the Dynamic Integrated Forecast (DICast) System, which forms the basis of the system beyond about 6 h. For short-range (0–6 h) forecasts, Sun4Cast leverages several observation-based nowcasting technologies. These technologies are blended via the Nowcasting Expert System Integrator (NESI). The NESI and DICast systems are subsequently blended to produce short- to midterm irradiance forecasts for solar array locations. The irradiance forecasts are translated into power with uncertainties quantified using an analog ensemble approach and are provided to the industry partners for real-time decision-making. The Sun4Cast system ran operationally throughout 2015 and results were assessed. This paper analyzes the collaborative design process, discusses the project results, and provides recommendations for best-practice solar forecasting.

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Gradient Profile Estimation Using Exponential Cubic Spline Smoothing in a Bayesian Framework

Entropy ◽

10.3390/e23060674 ◽

2021 ◽

Vol 23 (6) ◽

pp. 674

Author(s):

Kushani De De Silva ◽

Carlo Cafaro ◽

Adom Giffin

Keyword(s):

State Of The Art ◽

Estimation Method ◽

Synthetic Data ◽

Noisy Data ◽

Bayesian Framework ◽

Physical Systems ◽

Gradient Profile ◽

Ill Posed ◽

Profile Estimation ◽

Different Levels

Attaining reliable gradient profiles is of utmost relevance for many physical systems. In many situations, the estimation of the gradient is inaccurate due to noise. It is common practice to first estimate the underlying system and then compute the gradient profile by taking the subsequent analytic derivative of the estimated system. The underlying system is often estimated by fitting or smoothing the data using other techniques. Taking the subsequent analytic derivative of an estimated function can be ill-posed. This becomes worse as the noise in the system increases. As a result, the uncertainty generated in the gradient estimate increases. In this paper, a theoretical framework for a method to estimate the gradient profile of discrete noisy data is presented. The method was developed within a Bayesian framework. Comprehensive numerical experiments were conducted on synthetic data at different levels of noise. The accuracy of the proposed method was quantified. Our findings suggest that the proposed gradient profile estimation method outperforms the state-of-the-art methods.

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Evaluation of energy generation in Iraqi territory by solar photovoltaic power plants with a capacity of 20 MW

Energy Harvesting and Systems ◽

10.1515/ehs-2021-0075 ◽

2022 ◽

Vol 0 (0) ◽

Author(s):

Qusay Hassan ◽

Saadoon Abdul Hafedh ◽

Ali Hasan ◽

Marek Jaszczur

Keyword(s):

Experimental Data ◽

Wind Speed ◽

Power Plant ◽

Ambient Temperature ◽

Solar Irradiance ◽

Electricity Generation ◽

Power Plants ◽

Solar Photovoltaic ◽

Photovoltaic Power ◽

Plant Capacity

Abstract The study evaluates the visibility of solar photovoltaic power plant construction for electricity generation based on a 20 MW capacity. The assessment was performed for four main cities in Iraq by using hourly experimental weather data (solar irradiance, wind speed, and ambient temperature). The experimental data was measured for the period from 1st January to 31st December of the year 2019, where the simulation process was performed at a 1 h time step resolution at the same resolution as the experimental data. There are two positionings considered for solar photovoltaic modules: (i) annual optimum tilt angle and (ii) two-axis tracking system. The effect of the ambient temperature and wind on the overall system energy generated was taken into consideration. The study is targeted at evaluating the potential solar energy in Iraq and the viability of electricity generation using a 20 MW solar photovoltaic power plant. The results showed that the overall performance of the suggested power plant capacity is highly dependent on the solar irradiance intensity and the ambient temperature with wind speed. The current 20 MW solar photovoltaic power plant capacity shows the highest energy that can be generated in the mid-western region and the lowest in the northeast regions. The greatest influence of the ambient temperature on the energy genrated by power plants is observed in the southern regions.

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