Search results for: seasonal forecast models.

Authors: Abdelghafour Galadi

Abstract:

In this paper, accurate power MOSFET models including quasi-saturation effect are presented. These models have no internal node voltages determined by the circuit simulator and use one JFET or one depletion mode MOSFET transistors controlled by an “effective” gate voltage taking into account the quasi-saturation effect. The proposed models achieve accurate simulation results with an average error percentage less than 9%, which is an improvement of 21 percentage points compared to the commonly used standard power MOSFET model. In addition, the models can be integrated in any available commercial circuit simulators by using their analytical equations. A description of the models will be provided along with the parameter extraction procedure.

Keywords: Power MOSFET, drift layer, quasi-saturation effect, SPICE model, circuit simulation.

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Authors: Swathi Ganesan, Nalinda Somasiri, Rebecca Jeyavadhanam, Gayathri Karthick

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The COVID-19 crisis presents a substantial and critical hazard to worldwide health. Since the occurrence of the disease in late January 2020 in the UK, the number of infected people confirmed to acquire the illness has increased tremendously across the country, and the number of individuals affected is undoubtedly considerably high. The purpose of this research is to figure out a predictive machine learning (ML) archetypal that could forecast the COVID-19 cases within the UK. This study concentrates on the statistical data collected from 31st January 2020 to 31st March 2021 in the United Kingdom. Information on total COVID-19 cases registered, new cases encountered on a daily basis, total death registered, and patients’ death per day due to Coronavirus is collected from World Health Organization (WHO). Data preprocessing is carried out to identify any missing values, outliers, or anomalies in the dataset. The data are split into 8:2 ratio for training and testing purposes to forecast future new COVID-19 cases. Support Vector Machine (SVM), Random Forest (RF), and linear regression (LR) algorithms are chosen to study the model performance in the prediction of new COVID-19 cases. From the evaluation metrics such as r-squared value and mean squared error, the statistical performance of the model in predicting the new COVID-19 cases is evaluated. RF outperformed the other two ML algorithms with a training accuracy of 99.47% and testing accuracy of 98.26% when n = 30. The mean square error obtained for RF is 4.05e11, which is lesser compared to the other predictive models used for this study. From the experimental analysis, RF algorithm can perform more effectively and efficiently in predicting the new COVID-19 cases, which could help the health sector to take relevant control measures for the spread of the virus.

Keywords: COVID-19, machine learning, supervised learning, unsupervised learning, linear regression, support vector machine, random forest.

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Authors: Ksenija Dumičić, Anita Čeh Časni, Berislav Žmuk

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The aim of this paper is to select the most accurate forecasting method for predicting the future values of the unemployment rate in selected European countries. In order to do so, several forecasting techniques adequate for forecasting time series with trend component, were selected, namely: double exponential smoothing (also known as Holt`s method) and Holt-Winters` method which accounts for trend and seasonality. The results of the empirical analysis showed that the optimal model for forecasting unemployment rate in Greece was Holt-Winters` additive method. In the case of Spain, according to MAPE, the optimal model was double exponential smoothing model. Furthermore, for Croatia and Italy the best forecasting model for unemployment rate was Holt-Winters` multiplicative model, whereas in the case of Portugal the best model to forecast unemployment rate was Double exponential smoothing model. Our findings are in line with European Commission unemployment rate estimates.

Keywords: European Union countries, exponential smoothing methods, forecast accuracy unemployment rate.

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Authors: Ali Mamizadeh, Ires Iskender

Abstract:

The most important parameter in transformers life expectancy is the hot-spot temperature level which accelerates the rate of aging of the insulation. The aim of this paper is to present thermal models for transformers loaded at prefabricated MV/LV transformer substations and outdoor situations. The hot-spot temperature of transformers is studied using their top-oil temperature rise models. The thermal models proposed for hot-spot and top-oil temperatures of different operating situations are compared. Since the thermal transfer is different for indoor and outdoor transformers considering their operating conditions, their hot-spot thermal models differ from each other. The proposed thermal models are verified by the results obtained from the experiments carried out on a typical 1600 kVA, 30 /0.4 kV, ONAN transformer for both indoor and outdoor situations.

Keywords: Hot-spot Temperature, Dynamic Thermal Model, MV/LV Prefabricated, Oil Immersed Transformers

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Authors: M. A. Basunia

Abstract:

Thirty three re-wetting tests were conducted at different combinations of temperatures (5.7- 46.30C) and relative humidites (48.2-88.6%) with barley. Two most commonly used thinlayer drying and rewetting models i.e. Page and Diffusion were compared for their ability to the fit the experimental re-wetting data based on the standard error of estimate (SEE) of the measured and simulated moisture contents. The comparison shows both the Page and Diffusion models fit the re-wetting experimental data of barley well. The average SEE values for the Page and Diffusion models were 0.176 % d.b. and 0.199 % d.b., respectively. The Page and Diffusion models were found to be most suitable equations, to describe the thin-layer re-wetting characteristics of barley over a typically five day re-wetting. These two models can be used for the simulation of deep-bed re-wetting of barley occurring during ventilated storage and deep bed drying.

Keywords: Thin-layer, barley, re-wetting parameters, temperature, relative humidity.

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Authors: Tatyana Aleksandrovna Barbasova, Lev Sergeevich Kazarinov, Olga Valerevna Kolesnikova, Aleksandra Aleksandrovna Filimonova

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We regard forecasting of energy consumption by private production areas of a large industrial facility as well as by the facility itself. As for production areas, the forecast is made based on empirical dependencies of the specific energy consumption and the production output. As for the facility itself, implementation of the task to minimize the energy consumption forecasting error is based on adjustment of the facility’s actual energy consumption values evaluated with the metering device and the total design energy consumption of separate production areas of the facility. The suggested procedure of optimal energy consumption was tested based on the actual data of core product output and energy consumption by a group of workshops and power plants of the large iron and steel facility. Test results show that implementation of this procedure gives the mean accuracy of energy consumption forecasting for winter 2014 of 0.11% for the group of workshops and 0.137% for the power plants.

Keywords: Energy consumption, energy consumption forecasting error, energy efficiency, forecasting accuracy, forecasting.

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Authors: Dimitra Alexiou

Abstract:

During the last 10 years, in spite of the economic crisis, the number of tourists arriving in Greece has increased, particularly during the tourist season from April to October. In this paper, the number of annual tourist arrivals is studied to explore their preferences with regard to the month of travel, the selected destinations, as well the amount of money spent. The collected data are processed with statistical methods, yielding numerical and graphical results. From the computation of statistical parameters and the forecasting with exponential smoothing, useful conclusions are arrived at that can be used by the Greek tourism authorities, as well as by tourist organizations, for planning purposes for the coming years. The results of this paper and the computed forecast can also be used for decision making by private tourist enterprises that are investing in Greece. With regard to the statistical methods, the method of Simple Exponential Smoothing of time series of data is employed. The search for a best forecast for 2017 and 2018 provides the value of the smoothing coefficient. For all statistical computations and graphics Microsoft Excel is used.

Keywords: Tourism, statistical methods, exponential smoothing, land spatial planning, economy, Microsoft Excel.

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Authors: Zahra Gharineiat, Malik Khalfan

Abstract:

The significance of emissions from the road transport sector (such as air pollution, noise, etc) has grown considerably in recent years. In Australia, 14.3% of national greenhouse gas emissions in 2000 were the transport sector-s share which 12.9% of net national emissions were related to a road transport alone. Considering the growing attention to the green house gas(GHG) emissions, this paper attempts to provide air pollution modeling aspects of environmental consequences of the road transport by using one of the best computer based tools including the Geographic Information System (GIS). In other word, in this study, GIS and its applications is explained, models which are used to model air pollution and GHG emissions from vehicles are described and GIS is applied in real case study that attempts to forecast GHG emission from people who travel to work by car in 2031 in Melbourne for analysing results as thematic maps.

Keywords: Geographic Information System (GIS), Green HouseGas(GHG) emission, sustainable development, transportation

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Authors: Chiung-ying Lee, Chia-hua Chang

Abstract:

In July 1, 2007, Taiwan Stock Exchange (TWSE) on market observation post system (MOPS) adds a new "Financial reference database" for investors to do investment reference. This database as a warning to public offering companies listed on the public financial information and it original within eight targets. In this paper, this database provided by the indicators for the application of company financial crisis early warning model verify that the database provided by the indicator forecast for the financial crisis, whether or not companies have a high accuracy rate as opposed to domestic and foreign scholars have positive results. There is use of Logistic Regression Model application of the financial early warning model, in which no joined back-conditions is the first model, joined it in is the second model, has been taken occurred in the financial crisis of companies to research samples and then business took place before the financial crisis point with T-1 and T-2 sample data to do positive analysis. The results show that this database provided the debt ratio and net per share for the best forecast variables.

Keywords: Financial reference database, Financial early warning model, Logistic Regression.

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Authors: R. Kongnuy., P. Pongsumpun

Abstract:

Mathematical models can be used to describe the transmission of disease. Dengue disease is the most significant mosquito-borne viral disease of human. It now a leading cause of childhood deaths and hospitalizations in many countries. Variations in environmental conditions, especially seasonal climatic parameters, effect to the transmission of dengue viruses the dengue viruses and their principal mosquito vector, Aedes aegypti. A transmission model for dengue disease is discussed in this paper. We assume that the human and vector populations are constant. We showed that the local stability is completely determined by the threshold parameter, 0 B . If 0 B is less than one, the disease free equilibrium state is stable. If 0 B is more than one, a unique endemic equilibrium state exists and is stable. The numerical results are shown for the different values of the transmission probability from vector to human populations.

Keywords: Dengue disease, mathematical model, season, threshold parameters.

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Authors: Mikel Alonso López

Abstract:

In order to begin the process of studying the task of making consumer decisions, the main decision models must be analyzed. The objective of this task is to see if there is a presence of emotions in those models, and analyze how authors that have created them consider their impact in consumer choices. In this paper, the most important models of consumer behavior are analysed. This review is useful to consider an unproblematic background knowledge in the literature. The order that has been established for this study is chronological.

Keywords: Consumer behaviour, emotions, decision making, consumer psychology.

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Authors: Abdel Hamid Ajbar, Emad Ali

Abstract:

Saudi Arabia is an arid country which depends on costly desalination plants to satisfy the growing residential water demand. Prediction of water demand is usually a challenging task because the forecast model should consider variations in economic progress, climate conditions and population growth. The task is further complicated knowing that Mecca city is visited regularly by large numbers during specific months in the year due to religious occasions. In this paper, a neural networks model is proposed to handle the prediction of the monthly and yearly water demand for Mecca city, Saudi Arabia. The proposed model will be developed based on historic records of water production and estimated visitors- distribution. The driving variables for the model include annuallyvarying variables such as household income, household density, and city population, and monthly-varying variables such as expected number of visitors each month and maximum monthly temperature.

Keywords: Water demand forecast; Neural Networks model; water resources management; Saudi Arabia.

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Authors: Mak Kaboudan

Abstract:

A multi-agent system is developed here to predict monthly details of the upcoming peak of the 24th solar magnetic cycle. While studies typically predict the timing and magnitude of cycle peaks using annual data, this one utilizes the unsmoothed monthly sunspot number instead. Monthly numbers display more pronounced fluctuations during periods of strong solar magnetic activity than the annual sunspot numbers. Because strong magnetic activities may cause significant economic damages, predicting monthly variations should provide different and perhaps helpful information for decision-making purposes. The multi-agent system developed here operates in two stages. In the first, it produces twelve predictions of the monthly numbers. In the second, it uses those predictions to deliver a final forecast. Acting as expert agents, genetic programming and neural networks produce the twelve fits and forecasts as well as the final forecast. According to the results obtained, the next peak is predicted to be 156 and is expected to occur in October 2011- with an average of 136 for that year.

Keywords: Computational techniques, discrete wavelet transformations, solar cycle prediction, sunspot numbers.

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Authors: W. Preechaporn, M. Jaroensutasinee, K. Jaroensutasinee

Abstract:

This study investigated the seasonal prevalence of Aedes aegypti and Ae. albopictus larvae in three topographical areas (i.e. mangrove, rice paddy and mountainous areas). Samples were collected from 300 households in both wet and dry seasons in nine districts in Nakhon Si Thammarat province. Ae. aegypti and Ae. albopictus were found in 21 out of 29 types of water containers in mangrove, rice paddy and mountainous areas. Ae. aegypti and Ae. albopictus laid eggs in different container types depending on season and topographical areas. Ae. aegypti larvae were found most in metal box in mangrove and mountainous areas in wet season. Ae. albopictus larvae were also found most in metal box in mangrove and mountainous areas in both wet and dry seasons. All Ae. albopictus larval indices were higher than Ae. aegypti larval indices in all three topographical areas and both seasons. HI and BI did not differ in three topographical areas but differed between Aedes sp. HI for both Ae. aegypti and Ae. albopictus in all three topographical areas in both seasons were greater than 10 %, except Aedes aegypti in rice paddy area in wet season. This indicated high risks of DHF transmission in these areas.

Keywords: Aedes aegypti, Aedes albopictus, Season, Topography.

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Authors: Jaroslav Masek, Juraj Camaj, Eva Nedeliakova

Abstract:

The paper is focused on the operational model for transport the single wagon consignments on railway network by using two different models of train formation. The paper gives an overview of possibilities of improving the quality of transport services. Paper deals with two models used in problematic of train formatting - time continuously and time discrete. By applying these models in practice, the transport company can guarantee a higher quality of service and expect increasing of transport performance. The models are also applicable into others transport networks. The models supplement a theoretical problem of train formation by new ways of looking to affecting the organization of wagon flows.

Keywords: Train formation, wagon flows, marshalling yard, railway technology.

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Authors: Do Hoai Nam, Keiko Udo, Akira Mano

Abstract:

This paper presents the combination of different precipitation data sets and the distributed hydrological model, in order to examine the flood runoff reproductivity of scattered observation catchments. The precipitation data sets were obtained from observation using rain-gages, satellite based estimate (TRMM), and numerical weather prediction model (NWP), then were coupled with the super tank model. The case study was conducted in three basins (small, medium, and large size) located in Central Vietnam. Calculated hydrographs based on ground observation rainfall showed best fit to measured stream flow, while those obtained from TRMM and NWP showed high uncertainty of peak discharges. However, calculated hydrographs using the adjusted rainfield depicted a promising alternative for the application of TRMM and NWP in flood modeling for scattered observation catchments, especially for the extension of forecast lead time.

Keywords: Flood forecast, rainfall-runoff model, satellite rainfall estimate, numerical weather prediction, quantitative precipitation forecasting.

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Authors: Neerja Bhatnagar, Benjoe A. Juliano, Renee S. Renner

Abstract:

This paper presents data annotation models at five levels of granularity (database, relation, column, tuple, and cell) of relational data to address the problem of unsuitability of most relational databases to express annotations. These models do not require any structural and schematic changes to the underlying database. These models are also flexible, extensible, customizable, database-neutral, and platform-independent. This paper also presents an SQL-like query language, named Annotation Query Language (AnQL), to query annotation documents. AnQL is simple to understand and exploits the already-existent wide knowledge and skill set of SQL.

Keywords: annotation query language, data annotations, data annotation models, semantic data annotations

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Authors: Maryam Jamela Ismail, Rosdiazli Ibrahim, Idris Ismail

Abstract:

In the oil and gas industry, energy prediction can help the distributor and customer to forecast the outgoing and incoming gas through the pipeline. It will also help to eliminate any uncertainties in gas metering for billing purposes. The objective of this paper is to develop Neural Network Model for energy consumption and analyze the performance model. This paper provides a comprehensive review on published research on the energy consumption prediction which focuses on structures and the parameters used in developing Neural Network models. This paper is then focused on the parameter selection of the neural network prediction model development for energy consumption and analysis on the result. The most reliable model that gives the most accurate result is proposed for the prediction. The result shows that the proposed neural network energy prediction model is able to demonstrate an adequate performance with least Root Mean Square Error.

Keywords: Energy Prediction, Multilayer Feedforward, Levenberg-Marquardt, Root Mean Square Error (RMSE)

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Authors: Moustafa S. El-Sammany

Abstract:

Flash floods are considered natural disasters that can cause casualties and demolishing of infra structures. The problem is that flash floods, particularly in arid and semi arid zones, take place in very short time. So, it is important to forecast flash floods earlier to its events with a lead time up to 48 hours to give early warning alert to avoid or minimize disasters. The flash flood took place over Wadi Watier - Sinai Peninsula, in October 24th, 2008, has been simulated, investigated and analyzed using the state of the art regional weather model. The Weather Research and Forecast (WRF) model, which is a reliable short term forecasting tool for precipitation events, has been utilized over the study area. The model results have been calibrated with the real data, for the same date and time, of the rainfall measurements recorded at Sorah gauging station. The WRF model forecasted total rainfall of 11.6 mm while the real measured one was 10.8 mm. The calibration shows significant consistency between WRF model and real measurements results.

Keywords: Early warning system, Flash floods forecasting, WadiWatier, WRF model.

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Authors: Siham G. Farag

Abstract:

The main objective of this paper is to provide a new methodology for road safety assessment in Oman through the development of suitable accident prediction models. GLM technique with Poisson or NBR using SAS package was carried out to develop these models. The paper utilized the accidents data of 31 un-signalized T-intersections during three years. Five goodness-of-fit measures were used to assess the overall quality of the developed models. Two types of models were developed separately; the flow-based models including only traffic exposure functions, and the full models containing both exposure functions and other significant geometry and traffic variables. The results show that, traffic exposure functions produced much better fit to the accident data. The most effective geometric variables were major-road mean speed, minor-road 85th percentile speed, major-road lane width, distance to the nearest junction, and right-turn curb radius. The developed models can be used for intersection treatment or upgrading and specify the appropriate design parameters of T-intersections. Finally, the models presented in this thesis reflect the intersection conditions in Oman and could represent the typical conditions in several countries in the middle east area, especially gulf countries.

Keywords: Accidents Prediction Models (APMs), Generalized Linear Model (GLM), T-intersections, Oman.

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Authors: L. Parvizsedghy, I. Gkountis, A. Senouci, T. Zayed, M. Alsharqawi, H. El Chanati, M. El-Abbasy, F. Mosleh

Abstract:

The aging and deterioration of water pipelines in cities worldwide result in more frequent water main breaks, water service disruptions, and flooding damage. Therefore, there is an urgent need for undertaking proper maintenance procedures to avoid breaks and disastrous failures. However, due to budget limitations, the maintenance of water pipeline networks needs to be prioritized through efficient deterioration assessment models. Previous studies focused on the development of structural or physical deterioration assessment models, which require expensive inspection data. But, this paper aims at developing deterioration assessment models for water pipelines using statistical techniques. Several deterioration models were developed based on pipeline size, material type, and soil type using linear regression analysis. The categorical nature of some variables affecting pipeline deterioration was considered through developing several categorical models. The developed models were validated with an average validity percentage greater than 95%. Moreover, sensitivity analysis was carried out against different classifications and it displayed higher importance of age of pipes compared to other factors. The developed models will be helpful for the water municipalities and asset managers to assess the condition of their pipes and prioritize them for maintenance and inspection purposes.

Keywords: Water pipelines, deterioration assessment models, regression analysis.

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Authors: S. Ghorbani, N. I. Polushin

Abstract:

In this study, an approach to identify factors affecting on surface roughness in a machining process is presented. This study is based on 81 data about surface roughness over a wide range of cutting tools (conventional, cutting tool with holes, cutting tool with composite material), workpiece materials (AISI 1045 Steel, AA2024 aluminum alloy, A48-class30 gray cast iron), spindle speed (630-1000 rpm), feed rate (0.05-0.075 mm/rev), depth of cut (0.05-0.15 mm) and tool overhang (41-65 mm). A single decision tree (SDT) analysis was done to identify factors for predicting a model of surface roughness, and the CART algorithm was employed for building and evaluating regression tree. Results show that a single decision tree is better than traditional regression models with higher rate and forecast accuracy and strong value.

Keywords: Cutting condition, surface roughness, decision tree, CART algorithm.

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Authors: Mustapha Kamel Mihoubi, Hocine Dahmani

Abstract:

Over recent years much progress has been achieved in the fields of numerical modeling shoreline processes: waves, currents, waves and current. However, there are still some problems in the existing models to link the on the first, the hydrodynamics of waves and currents and secondly, the sediment transport processes and due to the variability in time, space and interaction and the simultaneous action of wave-current near the shore. This paper is the establishment of a numerical modeling to forecast the sediment transport from development scenarios of harbor structure. It is established on the basis of a numerical simulation of a water-sediment model via a 2D model using a set of codes calculation MIKE 21-DHI software. This is to examine the effect of the sediment transport drivers following the dominant incident wave in the direction to pass input harbor work under different variants planning studies to find the technical and economic limitations to the sediment transport and protection of the harbor structure optimum solution.

Keywords: Swell, current, radiation, stress, mesh, MIKE21, sediment.

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Authors: Dursun Aydin

Abstract:

This paper study about using of nonparametric models for Gross National Product data in Turkey and Stanford heart transplant data. It is discussed two nonparametric techniques called smoothing spline and kernel regression. The main goal is to compare the techniques used for prediction of the nonparametric regression models. According to the results of numerical studies, it is concluded that smoothing spline regression estimators are better than those of the kernel regression.

Keywords: Kernel regression, Nonparametric models, Prediction, Smoothing spline.

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Authors: I. Erins, J. Erina

Abstract:

The aim of this article is to assess the existing business models used by the banks operating in the CEE countries in the time period from 2006 till 2011. In order to obtain research results, the authors performed qualitative analysis of the scientific literature on bank business models, which have been grouped into clusters that consist of such components as: 1) capital and reserves; 2) assets; 3) deposits, and 4) loans. In their turn, bank business models have been developed based on the types of core activities of the banks, and have been divided into four groups: Wholesale, Investment, Retail and Universal Banks. Descriptive statistics have been used to analyse the models, determining mean, minimal and maximal values of constituent cluster components, as well as standard deviation. The analysis of the data is based on such bank variable indices as Return on Assets (ROA) and Return on Equity (ROE).

Keywords: Banks, Business model, CEE, ROA, ROE.

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Authors: Abdoullah Fath-Allah, Laila Cheikhi, Rafa E. Al-Qutaish, Ali Idri

Abstract:

Many quality models have been used to measure egovernment portals quality. However, the absence of an international consensus for e-government portals quality models results in many differences in terms of quality attributes and measures. The aim of this paper is to compare and analyze the existing e-government quality models proposed in literature (those that are based on ISO standards and those that are not) in order to propose guidelines to build a good and useful e-government portals quality model. Our findings show that, there is no e-government portal quality model based on the new international standard ISO 25010. Besides that, the quality models are not based on a best practice model to allow agencies to both; measure e-government portals quality and identify missing best practices for those portals.

Keywords: E-government, portal, best practices, quality model, ISO, standard, ISO 25010, ISO 9126.

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Authors: Bashir M. Suleiman

Abstract:

Thermal load calculations have been performed for multi-layered walls that are composed of three different parts; a common (sand and cement) plaster, and two types of locally produced soft and hard bricks. The masonry construction of these layered walls was based on concrete-backed stone masonry made of limestone bricks joined by mortar. These multilayered walls are forming the outer walls of the building envelope of a typical Libyan house. Based on the periodic seasonal weather conditions, within the Libyan cost region during summer and winter, measured thermal conductivity values were used to implement such seasonal variation of heat flow and the temperature variations through the walls. The experimental measured thermal conductivity values were obtained using the Hot Disk technique. The estimation of the thermal resistance of the wall layers ( R-values) is based on measurements and calculations. The numerical calculations were done using a simplified analytical model that considers two different wall constructions which are characteristics of such houses. According to the obtained results, the R-values were quite low and therefore, several suggestions have been proposed to improve the thermal loading performance that will lead to a reasonable human comfort and reduce energy consumption.

Keywords: Thermal loading, multilayered walls, Libyan bricks, thermal resistance

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Authors: Mogari I. Rapoo, Diteboho Xaba

Abstract:

This paper examines the forecasting performance of Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN) models with the published exchange rate obtained from South African Reserve Bank (SARB). ARIMA is one of the popular linear models in time series forecasting for the past decades. ARIMA and ANN models are often compared and literature revealed mixed results in terms of forecasting performance. The study used the MSE and MAE to measure the forecasting performance of the models. The empirical results obtained reveal the superiority of ARIMA model over ANN model. The findings further resolve and clarify the contradiction reported in literature over the superiority of ARIMA and ANN models.

Keywords: ARIMA, artificial neural networks models, error metrics, exchange rates.

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Authors: Mohammad Seifi, Azura Bt. Che Soh, Noor Izzrib. Abd. Wahab, Mohd Khair B. Hassan

Abstract:

Solar energy has a major role in renewable energy resources. Solar Cell as a basement of solar system has attracted lots of research. To conduct a study about solar energy system, an authenticated model is required. Diode base PV models are widely used by researchers. These models are classified based on the number of diodes used in them. Single and two-diode models are well studied. Single-diode models may have two, three or four elements. In this study, these solar cell models are examined and the simulation results are compared to each other. All PV models are re-designed in the Matlab/Simulink software and they examined by certain test conditions and parameters. This paper provides comparative studies of these models and it tries to compare the simulation results with manufacturer-s data sheet to investigate model validity and accuracy. The results show a four- element single-diode model is accurate and has moderate complexity in contrast to the two-diode model with higher complexity and accuracy

Keywords: Fill Factor (FF), Matlab/Simulink, Maximum PowerPoint (MPP), Maximum Power Point Tracker (MPPT), Photo Voltaic(PV), Solar cell, Standard Test Condition (STC).

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Authors: Alexander Efremov

Abstract:

A system for market identification (SMI) is presented. The resulting representations are multivariable dynamic demand models. The market specifics are analyzed. Appropriate models and identification techniques are chosen. Multivariate static and dynamic models are used to represent the market behavior. The steps of the first stage of SMI, named data preprocessing, are mentioned. Next, the second stage, which is the model estimation, is considered in more details. Stepwise linear regression (SWR) is used to determine the significant cross-effects and the orders of the model polynomials. The estimates of the model parameters are obtained by a numerically stable estimator. Real market data is used to analyze SMI performance. The main conclusion is related to the applicability of multivariate dynamic models for representation of market systems.

Keywords: market identification, dynamic models, stepwise regression.

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