Integration of logistic regression and multilayer perceptron for intelligent single and dual axis solar tracking systems

2021 ◽  
Author(s):  
Nadia AL‐Rousan ◽  
Nor Ashidi Mat Isa ◽  
Mohammad Khairunaz Mat Desa ◽  
Hazem AL‐Najjar
Keyword(s):  
Logistic Regression ◽  
Multilayer Perceptron ◽  
Tracking Systems ◽  
Solar Tracking

scholarly journals Photovoltaic Prediction Software: Evaluation with Real Data from Northern Spain

2021 ◽  
Vol 11 (11) ◽  
pp. 5025
Author(s):  
David González-Peña ◽  
Ignacio García-Ruiz ◽  
Montserrat Díez-Mediavilla ◽  
Mª. Isabel Dieste-Velasco ◽  
Cristina Alonso-Tristán
Keyword(s):  
Energy Production ◽  
Field Data ◽  
Real Data ◽  
Software Tools ◽  
Horizontal Axis ◽  
Real Field ◽  
Tracking Systems ◽  
Northern Spain ◽  
Commercial Software ◽  
Solar Tracking

Prediction of energy production is crucial for the design and installation of PV plants. In this study, five free and commercial software tools to predict photovoltaic energy production are evaluated: RETScreen, Solar Advisor Model (SAM), PVGIS, PVSyst, and PV*SOL. The evaluation involves a comparison of monthly and annually predicted data on energy supplied to the national grid with real field data collected from three real PV plants. All the systems, located in Castile and Leon (Spain), have three different tilting systems: fixed mounting, horizontal-axis tracking, and dual-axis tracking. The last 12 years of operating data, from 2008 to 2020, are used in the evaluation. Although the commercial software tools were easier to use and their installations could be described in detail, their results were not appreciably superior. In annual global terms, the results hid poor estimations throughout the year, where overestimations were compensated by underestimated results. This fact was reflected in the monthly results: the software yielded overestimates during the colder months, while the models showed better estimates during the warmer months. In most studies, the deviation was below 10% when the annual results were analyzed. The accuracy of the software was also reduced when the complexity of the dual-axis solar tracking systems replaced the fixed installation.

scholarly journals Fault Coverage-Aware Metrics for Evaluating the Reliability Factor of Solar Tracking Systems

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1074
Author(s):  
Raul Rotar ◽  
Sorin Liviu Jurj ◽  
Flavius Opritoiu ◽  
Mircea Vladutiu
Keyword(s):  
High Performance ◽  
Computation Time ◽  
Fault Coverage ◽  
Programming Environment ◽  
Tracking Systems ◽  
Circuit Testing ◽  
Reliability Factor ◽  
Solar Tracking ◽  
Mathcad Software ◽  
Python Programming

This paper presents a mathematical approach for determining the reliability of solar tracking systems based on three fault coverage-aware metrics which use system error data from hardware, software as well as in-circuit testing (ICT) techniques, to calculate a solar test factor (STF). Using Euler’s named constant, the solar reliability factor (SRF) is computed to define the robustness and availability of modern, high-performance solar tracking systems. The experimental cases which were run in the Mathcad software suite and the Python programming environment show that the fault coverage-aware metrics greatly change the test and reliability factor curve of solar tracking systems, achieving significantly reduced calculation steps and computation time.

Hybrid motor driver for solar tracking systems

2016 ◽  
Author(s):  
Vinicius N. Kuhn ◽  
Frank Gonzatti ◽  
Diogo Franchi ◽  
Maicon Miotto ◽  
Miguel N. Camargo ◽  
...  
Keyword(s):  
Tracking Systems ◽  
Solar Tracking ◽  
Hybrid Motor

scholarly journals PVsyst Software for Better Energy Efficiency of a Grid-Connected Photovoltaic Power Station

2021 ◽  
Vol 21 (2) ◽  
pp. 85-93
Keyword(s):  
Weather Station ◽  
Tracking Systems ◽  
Power Station ◽  
Solar Insolation ◽  
Power Stations ◽  
Photovoltaic Power ◽  
Solar Tracking ◽  
Orenburg Oblast ◽  
Solar Tracker

The paper presents a classification of solar tracking systems used in photovoltaic power stations (PVS) and their operating principles. A simulation model of a grid-connected 5-kW PVS has been developed in PVsyst, to which end the researchers selected PVS equipment and optimized the PV cell tilt angles. The paper further analyzes a grid-connected PVS in Orenburg Oblast in PVsyst under the following configurations: static PV cells, not tilted vs seasonally varied tilts; single-axis solar trackers with vertical and horizontal axes of rotation vs a dual-axis solar tracker. The analysis is based on solar insolation data for 2019 obtained from the research team’s HP-2000 weather station. Dual-axis solar tracker and single-axis vertical trackers are shown to have the best year-round generation, providing an increase of 13.2% and 11.5%, respectively, against the static PV cells (no change in tilt).

scholarly journals Hybrid model using logit and nonparametric methods for predicting micro-entity failure

2016 ◽  
Vol 13 (3) ◽  
pp. 35-46 ◽  
Author(s):  
A. Blanco-Oliver ◽  
A. Irimia-Dieguez ◽  
M.D. Oliver-Alfonso ◽  
M.J. Vázquez-Cueto
Keyword(s):  
Logistic Regression ◽  
Hybrid Model ◽  
Rough Set ◽  
Multilayer Perceptron ◽  
Predictive Power ◽  
Bankruptcy Prediction ◽  
Parametric Methods ◽  
Macroeconomic Variables ◽  
Accuracy Performance ◽  
Non Parametric

Following the calls from literature on bankruptcy, a parsimonious hybrid bankruptcy model is developed in this paper by combining parametric and non-parametric approaches.To this end, the variables with the highest predictive power to detect bankruptcy are selected using logistic regression (LR). Subsequently, alternative non-parametric methods (Multilayer Perceptron, Rough Set, and Classification-Regression Trees) are applied, in turn, to firms classified as either “bankrupt” or “not bankrupt”. Our findings show that hybrid models, particularly those combining LR and Multilayer Perceptron, offer better accuracy performance and interpretability and converge faster than each method implemented in isolation. Moreover, the authors demonstrate that the introduction of non-financial and macroeconomic variables complement financial ratios for bankruptcy prediction

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