Evaluation of Different PV Prediction Models. Comparison and Experimental Validation with One-Year Measurements at Ground Level

Author(s):  
Charaf Hajjaj ◽  
Ahmed Alami Merrouni ◽  
Abdellatif Bouaichi ◽  
Mohammadi Benhmida ◽  
Badr Ikken ◽  
...  
2011 ◽  
Vol 50-51 ◽  
pp. 885-889 ◽  
Author(s):  
Fei Xue Yan ◽  
Jing Xia ◽  
Guan Qun Shen ◽  
Xu Sheng Kang

As time goes by, hazard rate of the society would increase if crime prediction was not implemented. Based on objective factors of offenders and victims characteristics, AHP method can be established to get a quantitative and qualitative analysis on crime prediction. Crime prediction is a strategic and tactical measure for crime prevention. According to AHP analysis, two prediction models of the optimal predictive crime locations are put forward. Standard Deviational Ellipses Model and Key Feature adjusted Spatial Choice Model were formulated to account for the anticipated position with various elements from AHP method. These models could be applied in a computer simulation of situation tests of the series murders. Besides, applying those models in certain real case demonstrates how the models work. Through models comparison, the results are summarized that Key Feature adjusted Spatial Choice Model is more conducive in confirming the guilty place. In conclusion, the suggested models, including detailed criminal map, are easy to implement.


Risks ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 200
Author(s):  
Youssef Zizi ◽  
Amine Jamali-Alaoui ◽  
Badreddine El Goumi ◽  
Mohamed Oudgou ◽  
Abdeslam El Moudden

In the face of rising defaults and limited studies on the prediction of financial distress in Morocco, this article aims to determine the most relevant predictors of financial distress and identify its optimal prediction models in a normal Moroccan economic context over two years. To achieve these objectives, logistic regression and neural networks are used based on financial ratios selected by lasso and stepwise techniques. Our empirical results highlight the significant role of predictors, namely interest to sales and return on assets in predicting financial distress. The results show that logistic regression models obtained by stepwise selection outperform the other models with an overall accuracy of 93.33% two years before financial distress and 95.00% one year prior to financial distress. Results also show that our models classify distressed SMEs better than healthy SMEs with type I errors lower than type II errors.


2018 ◽  
Vol 173 ◽  
pp. 476-488 ◽  
Author(s):  
Charaf Hajjaj ◽  
Ahmed Alami Merrouni ◽  
Abdellatif Bouaichi ◽  
Mohammadi Benhmida ◽  
Smail Sahnoun ◽  
...  

Author(s):  
Huan Pablo de Souza ◽  
Dione Richer Momolli ◽  
Aline Aparecida Ludvichak ◽  
Mauro Valdir Schumacher ◽  
Angélica Costa Malheiros

The present work aims to quantify the precipitation partition after interaction with the Eucalyptus urophylla canopy under two fertilization treatments. The experimental design was completely causalized with two fertilization treatments. Each plot had a dimension of 30 m x 60 m, and the spacing of the seedlings was 3 m x 2 m. The study was developed in a arenizaded area located in the municipality of Maçambará, state of Rio Grande do Sul, Brazil. The duration of the study was one year (from April 2017 to March 2018). Biweekly over twelve months the volume of precipitation was quantified. The experiment consists of two fertilization treatments in a Eucalyptus urophylla stand: T1 with smaller and T2 greater fertilization. In each treatment 3 throughfall collectors were installed at one meter of the soil level and three stemflow collectors. In the open area 3 collectors of the incident precipitation were installed 1.5 meters from the ground level. The percentages of the throughfall, stemflow and canopy interception in relation to the incident precipitation were 95.3; 1.3 and 4.3% for treatment 1 and 91.7; 3.2 and 6.2% for treatment 2. The coefficients of determination for throughfall, stemflow and canopy interception were 0.99; 0.96 and 0.85 for treatment 1 and 0.99; 0.97 and 0.89 for treatment 2. The graphical analysis of the regression residues shows independence of the errors. The fertilization management described for treatment 2 results in a greater interception of rainfall due to the greater amount of biomass of the canopy.


2014 ◽  
Vol 1079-1080 ◽  
pp. 379-385 ◽  
Author(s):  
Jing Luo ◽  
Jian Bei Liu ◽  
Teng Feng Guo ◽  
Cheng Yu Hu

Surface water film thickness is one of the main factors, which affect the vehicle safety on slippery roads. Water film depth is influenced by rainfall intensity, grades, cross slopes, drainage length and pavement texture. This paper reviews the research status and makes some comparative analysis of several pavement water film depth prediction models. An experimental validation has verified and calibrated the existing water film depth prediction models results. The experimental validation of the variable in the slope water flow model has been implemented by means of a small scale physical road model in a rainfall simulator, which is constructed in a laboratory. The results of comparative analysis have shown that in the existing water film depth prediction models, the regression models predict values are more closely than mathematical-physical models. Because under different experimental conditions, the regression model calibration parameters are different. In the case of specific road characteristics for prediction of water film thickness, the model parameters can be calibrated to further improve predicting accuracy.


2019 ◽  
Vol 111 ◽  
pp. 02007
Author(s):  
Hiroki Takahashi ◽  
Mariya Petrova Bivolarova ◽  
Athanasia Keli ◽  
Jürgen Nickel ◽  
Arsen Krikor Melikov

The accurate data of outdoor CO2 concentration are important for the proper design of ventilation and thus for indoor air quality and energy use in buildings. Typical design practice is to assume outdoor CO2 concentration to be 400 ppm. However, the outdoor CO2 concentration may be different in different areas of cities. This paper presents preliminary results of long-term (one year) outdoor CO2 concentration changes in four districts of Copenhagen (Denmark). The districts included downtown area and suburbs with different surroundings. Four buildings were selected for the measurements, one building in each district. Outdoor CO2 concentration measurements were performed at two levels – ground level and top of the buildings. Special attention was paid to use accurate measuring instruments. The instruments were carefully calibrated before the measurements. The calibration of the instruments was checked periodically. In this paper, preliminary results from summer and autumn measurements are presented. The outdoor CO2 concentration varied over the day and from day to day in the range between 340 and 450 ppm. The CO2 concentration at the ground of the buildings was usually 10 to 40 ppm higher than that at the top level in autumn. At the buildings in the suburbs, during the working hours, the outdoor CO2 concentration measured on the top level close to the intake duct was on average 408 ppm. At the building in the downtown area, that was on average 414 ppm. However, the outdoor CO2 concentration varied depending on the building, level and time. During the working hours, the 75 percentiles of outdoor CO2 concentration varied between 384 ppm and 442 ppm, which indicates that the required ventilation rate could be different over 10% depending on the building location site, measurement height and time. In order to ensure the required indoor limits of CO2 concentration, CO2 measurements must be performed close to the location of the outdoor air intake.


2021 ◽  
Author(s):  
Sebastiaan Remmers ◽  
Lana Lai Yin Hui ◽  
Carolin Schimmelpfennig ◽  
Peter-Paul Willemsen ◽  
Markus Kreuz ◽  
...  

Abstract The objective of this study is to develop and validate patient-level prediction models for patients on watchful waiting (WW) estimating the risk of developing symptomatic progression, hospitalization, ER visit, initiation of curative or palliative treatment, and survival. Estimation for all clinical models will be done based on 1) age and clinical measurements (e.g., PSA) 6 months before diagnosis, 2) age, clinical measurements 6 months before diagnosis, and clinical conditions one year before diagnosis. Finally, a clinically usable model will be developed based on expert clinical input. All prediction models will be implemented using Lasso logistic regression for the time at risk analyses.


2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
M Blum ◽  
K McKendrick ◽  
L.P Gelfman ◽  
N.E Goldstein

Abstract Background Predicting survival in patients with advanced heart failure (HF) remains difficult and prognostic scores such as the Seattle Heart Failure Model (SHFM) are cumbersome to use. Alternative approaches like the Surprise Question (SC) or the number of HF hospitalisations within the last year (NoH) could simplify prognostication. Purpose We assessed the prognostic utility of the SHFM, SC and NoH for predicting one-year survival status in patients with advanced HF. Methods A secondary analysis of a multisite, single-blinded cluster-randomized, controlled trial to test whether a structured intervention of educational content and automated reminders increased the likelihood of ICD deactivation conversations and ICD deactivation. The study was performed within the advanced HF practices at six US academic medical centers, between September 2011 to February 2016. Patient eligibility criteria included advanced HF, an implantable cardiac defibrillator and a high risk of death, with complete data on SHFM, SC, NoH and one-year survival status. SHFM survival was calculated from baseline variables; the SC (“Would you be surprised if the patient were to die within one year?”) was answered by cardiologists; and the NoH was extracted from medical records. For prediction of survival status, cut-offs for predicted survival per SHFM and NoH were chosen empirically by means of receiver operating characteristic (ROC) curve analysis maximising Youden's index. The resulting binary prediction models were assessed based on area under the ROC curve (AUC), sensitivity and specificity. Results Of the 535 subjects in our sample, 82 (15.3%) had died after one-year of follow-up. For the SHFM and the NoH, optimal cut-offs were found to be a predicted survival <86% and ≥2 hospitalisations, respectively. Performance metrics of prognostic models are detailed in Table 1. The SHFM yielded an AUC of 0.65 (0.60–0.71 95% confidence interval [CI]), a sensitivity of 0.76 (0.65–0.84 95% CI), and a specificity of 0.55 (0.50–0.60 95% CI). The SC demonstrated a comparable AUC 0.58 (0.54–0.63 95% CI), similar sensitivity 0.84 (0.74–0.91 95% CI), but lower specificity 0.33 (0.28–0.37 95% CI) compared to the SHFM. The NoH demonstrated a comparable AUC 0.56 (0.50–0.62 95% CI), similar sensitivity 0.56 (0.45–0.67 95% CI), and similar specificity 0.56 (0.51–0.61 95% CI) compared to the SHFM. The combination of positive SC and NoH ≥2 showed significantly higher specificity compared to the SHFM (0.68 [0.64–0.73 95% CI]). Conclusion The SC and NoH are clinically feasible bedside alternatives to the more complex SHFM model, yet yield similar overall prognostic utility for one-year survival status among advanced HF patients. FUNDunding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Heart, Lung, and Blood Institute


Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3407
Author(s):  
Eduard Massaguer ◽  
Albert Massaguer ◽  
Eudald Balló ◽  
Ivan Ruiz Cózar ◽  
Toni Pujol ◽  
...  

Solar thermoelectric generators (STEGs) are a promising technology to harvest energy for off-grid applications. A wide variety of STEG designs have been proposed with the aim of providing non-intermittent electrical generation. Here, we designed and tested a STEG 0.5 m long formed by nine commercial thermoelectric generator modules and located at ground level. Data were used to validate a numerical model that was employed to simulate a one-year cycle. Results confirmed the very high variability of energy generation during daylight time due to weather conditions. By contrast, energy generation during night was almost independent of atmospheric conditions. Annual variations of nighttime energy generation followed the trend of the daily averaged soil temperature at the bottom of the device. Nighttime electrical energy generation was 5.4 times smaller than the diurnal one in yearly averaged values. Mean energy generation values per day were 587 J d−1 (daylight time) and 110 J d−1 (nighttime). Total annual energy generation was 255 kJ. Mean electrical output power values during daylight and nighttime were 13.4 mW and 2.5 mW, respectively. Annual mean output power was 7.9 mW with a peak value of 79.8 mW.


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