Comparison of PV potential models for africa and their potential cost implications.

2021 ◽  
Author(s):  
Arnold Wasike ◽  
Catherina Cader
Keyword(s):  
Renewable Energy ◽  
Pearson Correlation ◽  
Surface Radiation ◽  
Geographical Information ◽  
Potential Cost ◽  
Energy Agency ◽  
Data Set ◽  
Potential Models ◽  
Photo Voltaic ◽  
Cost Implications

<p>We currently have more than 7500 planned mini grids, most of them in Africa. These will soon connect more than 27 million people and cost about 12 billion dollars <sup>[1]</sup>. Africa is in a good position for Photo voltaic (PV) mini grid optimization, receiving more than 1800 KWh/m<sup>2</sup> Global Horizontal Irradiation (GHI) every year <sup>[2]</sup>, for most parts of the continent. However, the lack of a coordinated renewable energy monitoring and distribution network works against optimization of PV potential models <sup>[3]</sup>. This study shows the accuracy of existing photo voltaic potential estimators like renewables ninja <sup>[3]</sup>, the National Renewable Energy Laboratory (NREL), International Renewable Energy Agency (IRENA), and the global solar atlas <sup>[2]</sup>, by comparing the modeled values with long term measurements from ground solar stations. This is done for more than 20 stations distributed over Africa. Our results show best correlations <sup>[4]</sup> of up to 65.3% from version 2 of the Surface Radiation Data Set from Heliosat (SARAH) derived from the Photovoltaic Geographical Information System (PVGIS). However, we also have correlations as low as 16.2% for models commonly used in off grid simulations. The sensitivities of the modeled cost of a mini grid to the variation in PV potential were tested <sup>[5][6]</sup> using the statistical range in sourced PV potential from the different estimators, giving us cost variation of more than 2.8% that may arise from the different sources.</p><p><strong>References</strong></p><p>1. World Bank, ESMAP - Mini grids for half a billion people</p><p>2. https://globalsolaratlas.info/map</p><p>3. doi: 10.1016/j.energy.2016.08.060</p><p>4. Wikipedia contributors. (2021, January 7). Pearson correlation coefficient. In Wikipedia, The Free Encyclopedia. Retrieved 09:00, January 20, 2021, from https://en.wikipedia.org/w/index.php?title=Pearson_correlation_coefficient&oldid=998963119</p><p>5. Cader. 2018</p><p>5. Hoffmann. 2019</p><p>7. https://doi.org/10.2136/vzj2018.03.0062</p>

scholarly journals Combining Re-Analyzed Climate Data and Landcover Products to Assess the Temporal Complementarity of Wind and Solar Resources in Iraq

2021 ◽  
Vol 14 (1) ◽  
pp. 388
Author(s):  
Mourtadha Sarhan Sachit ◽  
Helmi Zulhaidi Mohd Shafri ◽  
Ahmad Fikri Abdullah ◽  
Azmin Shakrine Mohd Rafie
Keyword(s):  
Renewable Energy ◽  
Storage Systems ◽  
Pearson Correlation ◽  
Renewable Resource ◽  
Decision Makers ◽  
Geographical Information ◽  
Climate Data ◽  
A Value ◽  
The Stability ◽  
First Time

Considering the spatial–temporal variation of renewable energy (RE) resources, assessment of their complementarity is of great significance for decision-makers to increase the stability of power output and reduce the need for storage systems. In this regard, the current paper presents a roadmap to assess the temporal complementarity patterns between wind and solar resources for the first time in Iraq. A new approach based on re-analyzed climate data, Landcover products, and geographical information system (GIS) is proposed. As such, renewable resource datasets are collected for 759 locations with a daily timescale over five years. Landcover classes are translated into wind shear coefficients (WSCs) to model wind velocity at turbine hub height. Then, the Pearson correlation coefficient (PCC) is applied to calculate the complementarity indices for each month of the year. Results of this investigation reveal that there are significant synergy patterns spanning more than six months in the southwestern regions and some eastern parts of Iraq. The highest complementarity is observed in March and December with a value of −0.70 and −0.63, respectively. Despite this promising potential, no typical temporal complementarity has been discovered that would completely eliminate the fluctuations of clean power generation. However, the synergistic properties yielded by this work could mitigate the reliance on storage systems, particularly as they cover important regions of the country. The proposed approach and tools can help improve the planning of renewable energy systems.

An approach for document retrieval using cluster-based inverted indexing

2021 ◽  
pp. 016555152110184
Author(s):  
Gunjan Chandwani ◽  
Anil Ahlawat ◽  
Gaurav Dubey
Keyword(s):  
High Performance ◽  
Clustering Algorithm ◽  
Pearson Correlation ◽  
Relevant Information ◽  
Document Retrieval ◽  
Bhattacharyya Distance ◽  
Data Set ◽  
Query Matching ◽  
Inverted Indexing ◽  
Query Optimisation

Document retrieval plays an important role in knowledge management as it facilitates us to discover the relevant information from the existing data. This article proposes a cluster-based inverted indexing algorithm for document retrieval. First, the pre-processing is done to remove the unnecessary and redundant words from the documents. Then, the indexing of documents is done by the cluster-based inverted indexing algorithm, which is developed by integrating the piecewise fuzzy C-means (piFCM) clustering algorithm and inverted indexing. After providing the index to the documents, the query matching is performed for the user queries using the Bhattacharyya distance. Finally, the query optimisation is done by the Pearson correlation coefficient, and the relevant documents are retrieved. The performance of the proposed algorithm is analysed by the WebKB data set and Twenty Newsgroups data set. The analysis exposes that the proposed algorithm offers high performance with a precision of 1, recall of 0.70 and F-measure of 0.8235. The proposed document retrieval system retrieves the most relevant documents and speeds up the storing and retrieval of information.

A Constrained Delaunay Triangulation Algorithm Based on Incremental Points

2011 ◽  
Vol 90-93 ◽  
pp. 3277-3282 ◽  
Author(s):  
Bai Chao Wu ◽  
Ai Ping Tang ◽  
Lian Fa Wang
Keyword(s):  
Information System ◽  
Data Structures ◽  
Delaunay Triangulation ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Three Dimensional ◽  
Geographical Information ◽  
Two Dimensional ◽  
Data Set ◽  
Constrained Delaunay Triangulation

The foundation ofdelaunay triangulationandconstrained delaunay triangulationis the basis of three dimensional geographical information system which is one of hot issues of the contemporary era; moreover it is widely applied in finite element methods, terrain modeling and object reconstruction, euclidean minimum spanning tree and other applications. An algorithm for generatingconstrained delaunay triangulationin two dimensional planes is presented. The algorithm permits constrained edges and polygons (possibly with holes) to be specified in the triangulations, and describes some data structures related to constrained edges and polygons. In order to maintain the delaunay criterion largely，some new incremental points are added onto the constrained ones. After the data set is preprocessed, the foundation ofconstrained delaunay triangulationis showed as follows: firstly, the constrained edges and polygons generate initial triangulations，then the remained points completes the triangulation . Some pseudo-codes involved in the algorithm are provided. Finally, some conclusions and further studies are given.

scholarly journals Impact of Working Capital Management on Corporate Profitability- Empirical Evidence from Pharmaceutical Industry of Bangladesh

2018 ◽  
Vol 10 (9) ◽  
pp. 136
Author(s):  
Rakibul Islam ◽  
Mohammad Emdad Hossain ◽  
Mohammad Nazmul Hoq ◽  
Md. Morshedul Alam
Keyword(s):  
Regression Analysis ◽  
Correlation Matrix ◽  
Negative Impact ◽  
Pearson Correlation ◽  
Capital Management ◽  
Working Capital ◽  
Data Set ◽  
Working Capital Management ◽  
Pharmaceutical Firms ◽  
The Impact

Working capital management plays centric role in enhancing operational efficiency and their ultimate profitability. Globally financial managers have been searching the proper way on how to utilize working capital components which prolong profitability. The purpose of this study is to assess the impact of working capital components on profitability indicators of selected pharmaceutical firms in Bangladesh. The paper used financial data of 9 pharmaceutical firms listed in Dhaka stock exchange (DSE) covered 2011-2015. Two methods were used in this study for analysis data set. Firstly, to measure the relationship between selected variables Pearson Correlation matrix was used. Secondly, multiple regression analysis was used to investigate the impact working capital components on profitability of selected pharmaceutical firms. The study also conducted Durbin Watson test to assess autocorrelation of selected variables. In this study the correlation matrix identified a negative correlation between working capital components and profitability, whereas regression analysis found number of days account receivable (AR) had significant positive and current ratio (CR) and debt ratio (DR) had appeared a significant negative impact on profitability.

scholarly journals Assessment of the Relationship between Cerebrospinal Meningitis and Climate Variables in Kaduna State, Nigeria

2021 ◽  
pp. 33-41
Author(s):  
Joyce Imara Nchom ◽  
A. S. Abubakar ◽  
F. O. Arimoro ◽  
B. Y. Mohammed
Keyword(s):  
Relative Humidity ◽  
Air Temperature ◽  
Linear Trend ◽  
Pearson Correlation ◽  
Maximum Temperature ◽  
Data Set ◽  
International Research Institute ◽  
Wind Speed Maximum ◽  
Weather Parameters ◽  
The Relationship

This study examines the relationship between Meningitis and weather parameters (air temperature, maximum temperature, relative humidity, and rainfall) in Kaduna state, Nigeria on a weekly basis from 2007–2019. Meningitis data was acquired weekly from Nigeria Centre for Disease Control (NCDC), Bureau of Statistics and weather parameters were sourced from daily satellite data set National Oceanic and Atmospheric Administration (NOAA), International Research Institute for Climate and Society (IRI). The daily data were aggregated weekly to suit the study. The data were analysed using linear trend and Pearson correlation for relationship. The linear trend results revealed a weekly decline in Cerebro Spinal Meningitis (CSM), wind speed, maximum and air temperature and an increase in relative humidity and rainfall. Generally, results reveal that the most important explanatory weather variables influencing CSM amongst the five (5) are the weekly maximum temperature and air temperature with a positive correlation of 0.768 and 0.773. This study recommends that keen interest be placed on temperature as they play an essential role in the transmission of this disease and most times aggravate the patients' condition.

scholarly journals Research and implementation of big data visualization based on WebGIS

2019 ◽  
Vol 2 ◽  
pp. 1-6
Author(s):  
Wenjuan Lu ◽  
Aiguo Liu ◽  
Chengcheng Zhang
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Visual Analysis ◽  
Distributed Database ◽  
Research Problem ◽  
Research Area ◽  
Geographical Information ◽  
Data Set ◽  
Visualization Technology ◽  
Thematic Data

<p><strong>Abstract.</strong> With the development of geographic information technology, the way to get geographical information is constantly, and the data of space-time is exploding, and more and more scholars have started to develop a field of data processing and space and time analysis. In this, the traditional data visualization technology is high in popularity and simple and easy to understand, through simple pie chart and histogram, which can reveal and analyze the characteristics of the data itself, but still cannot combine with the map better to display the hidden time and space information to exert its application value. How to fully explore the spatiotemporal information contained in massive data and accurately explore the spatial distribution and variation rules of geographical things and phenomena is a key research problem at present. Based on this, this paper designed and constructed a universal thematic data visual analysis system that supports the full functions of data warehousing, data management, data analysis and data visualization. In this paper, Weifang city is taken as the research area, starting from the aspects of rainfall interpolation analysis and population comprehensive analysis of Weifang, etc., the author realizes the fast and efficient display under the big data set, and fully displays the characteristics of spatial and temporal data through the visualization effect of thematic data. At the same time, Cassandra distributed database is adopted in this research, which can also store, manage and analyze big data. To a certain extent, it reduces the pressure of front-end map drawing, and has good query analysis efficiency and fast processing ability.</p>

scholarly journals Experimental Studies on an Inclined Collector Divergent Chimney Pilot Plant

2021 ◽  
Vol 850 (1) ◽  
pp. 012008
Author(s):  
N Rajamurugu
Keyword(s):  
Renewable Energy ◽  
Fossil Fuels ◽  
Low Cost ◽  
Renewable Energy Sources ◽  
Experimental Studies ◽  
High Mass ◽  
Energy Sources ◽  
Solar Pv ◽  
Solar Chimney ◽  
Energy Agency

Abstract Renewable energy sources become suitable valid options to reduce the dependency on fossil fuels or petroleum products. The International Renewable Energy Agency reports that the world will harvest 40% of energy from renewable energy sources by 2030. Conventional technologies such as solar PV technology, consumes higher capital per unit (kWh) of electricity generation cost significantly higher than the traditional sources. Hence, solar chimney power generation system can be suitable option for generating low cost energy. Solar chimneys are developed and tested by different researchers in enhancing the performance of the system. Studies on the geometric modifications of the collector, and chimney are limited. The aim of this paper is to analyse the experimental data obtained from a divergent solar chimney. Experimentation is carried under sunlight in an open atmosphere. The airflow rates in the chimneys are tested under different collector outlet height. The experimental results showed that a chimney with higher collector openings was performed well than other models. The computational analysis is also carried out using ANSYS Fluent software package which shows that the collector opening of 2.5m is recommended for higher high mass flow rate and system efficiency.

scholarly journals Sentinel-1 and Sentinel-2 Data Fusion for Mapping and Monitoring Wetlands

2018 ◽  
Author(s):  
Gordana Kaplan ◽  
Ugur Avdan
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Classification Accuracy ◽  
Accuracy Assessment ◽  
Geographical Information ◽  
Wetland Classification ◽  
Significant Information ◽  
Data Set ◽  
Object Based ◽  
Sentinel 2

Wetlands benefits can be summarized but are not limited to their ability to store floodwaters and improve water quality, providing habitats for wildlife and supporting biodiversity, as well as aesthetic values. Over the past few decades, remote sensing and geographical information technologies has proven to be a useful and frequent applications in monitoring and mapping wetlands. Combining both optical and microwave satellite data can give significant information about the biophysical characteristics of wetlands and wetlands` vegetation. Also, fusing data from different sensors, such as radar and optical remote sensing data, can increase the wetland classification accuracy. In this paper we investigate the ability of fusion two fine spatial resolution satellite data, Sentinel-2 and the Synthetic Aperture Radar Satellite, Sentinel-1, for mapping wetlands. As a study area in this paper, Balikdami wetland located in the Anatolian part of Turkey has been selected. Both Sentinel-1 and Sentinel-2 images require pre-processing before their use. After the pre-processing, several vegetation indices calculated from the Sentinel-2 bands were included in the data set. Furthermore, an object-based classification was performed. For the accuracy assessment of the obtained results, number of random points were added over the study area. In addition, the results were compared with data from Unmanned Aerial Vehicle collected on the same data of the overpass of the Sentinel-2, and three days before the overpass of Sentinel-1 satellite. The accuracy assessment showed that the results significant and satisfying in the wetland classification using both multispectral and microwave data. The statistical results of the fusion of the optical and radar data showed high wetland mapping accuracy, with an overall classification accuracy of approximately 90% in the object-based classification. Compared with the high resolution UAV data, the classification results give promising results for mapping and monitoring not just wetlands, but also the sub-classes of the study area. For future research, multi-temporal image use and terrain data collection are recommended.

scholarly journals Benchmarking Effectiveness and Efficiency of Deep Learning Models for Semantic Textual Similarity in the Clinical Domain: Validation Study

10.2196/27386 ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. e27386
Author(s):  
Qingyu Chen ◽  
Alex Rankine ◽  
Yifan Peng ◽  
Elaheh Aghaarabi ◽  
Zhiyong Lu
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Mean Squared Error ◽  
Pearson Correlation ◽  
Data Set ◽  
Squared Error ◽  
Real Time Applications ◽  
Effectiveness And Efficiency ◽  
Pearson Correlations

Background Semantic textual similarity (STS) measures the degree of relatedness between sentence pairs. The Open Health Natural Language Processing (OHNLP) Consortium released an expertly annotated STS data set and called for the National Natural Language Processing Clinical Challenges. This work describes our entry, an ensemble model that leverages a range of deep learning (DL) models. Our team from the National Library of Medicine obtained a Pearson correlation of 0.8967 in an official test set during 2019 National Natural Language Processing Clinical Challenges/Open Health Natural Language Processing shared task and achieved a second rank. Objective Although our models strongly correlate with manual annotations, annotator-level correlation was only moderate (weighted Cohen κ=0.60). We are cautious of the potential use of DL models in production systems and argue that it is more critical to evaluate the models in-depth, especially those with extremely high correlations. In this study, we benchmark the effectiveness and efficiency of top-ranked DL models. We quantify their robustness and inference times to validate their usefulness in real-time applications. Methods We benchmarked five DL models, which are the top-ranked systems for STS tasks: Convolutional Neural Network, BioSentVec, BioBERT, BlueBERT, and ClinicalBERT. We evaluated a random forest model as an additional baseline. For each model, we repeated the experiment 10 times, using the official training and testing sets. We reported 95% CI of the Wilcoxon rank-sum test on the average Pearson correlation (official evaluation metric) and running time. We further evaluated Spearman correlation, R², and mean squared error as additional measures. Results Using only the official training set, all models obtained highly effective results. BioSentVec and BioBERT achieved the highest average Pearson correlations (0.8497 and 0.8481, respectively). BioSentVec also had the highest results in 3 of 4 effectiveness measures, followed by BioBERT. However, their robustness to sentence pairs of different similarity levels varies significantly. A particular observation is that BERT models made the most errors (a mean squared error of over 2.5) on highly similar sentence pairs. They cannot capture highly similar sentence pairs effectively when they have different negation terms or word orders. In addition, time efficiency is dramatically different from the effectiveness results. On average, the BERT models were approximately 20 times and 50 times slower than the Convolutional Neural Network and BioSentVec models, respectively. This results in challenges for real-time applications. Conclusions Despite the excitement of further improving Pearson correlations in this data set, our results highlight that evaluations of the effectiveness and efficiency of STS models are critical. In future, we suggest more evaluations on the generalization capability and user-level testing of the models. We call for community efforts to create more biomedical and clinical STS data sets from different perspectives to reflect the multifaceted notion of sentence-relatedness.

scholarly journals Geospatial and Social Factors Influencing Morbidity due to Acute Infection in Pediatric Patients in Matiari, Rural Pakistan.

2021 ◽  
Author(s):  
Elise Corden ◽  
Saman Hasan Siddiqui ◽  
Yash Sharma ◽  
Muhammad Faraz Raghib ◽  
William Adorno III ◽  
...  
Keyword(s):  
Random Forest ◽  
Geographical Information Systems ◽  
Respiratory Infections ◽  
Pearson Correlation ◽  
Acute Infection ◽  
Disease Prevalence ◽  
Geographical Information ◽  
Continuous Variables ◽  
Gis Technology ◽  
Healthcare Facilities

Infectious disease is the leading cause of mortality in children under five. This study has investigated environmental factors related to the morbidity of acute respiratory infections (ARIs), diarrhea, and growth using geographical information systems (GIS) technology. Anthropometric, address and disease prevalence data were collected through the SEEM study in Matiari, Pakistan. Publicly available map data was used to compile coordinates of healthcare facilities. A Pearson correlation coefficient (r) was used to calculate the correlation between distance from healthcare facilities and participant growth and morbidity. Other continuous variables influencing these outcomes were analyzed using a random forest regression model. In this study of 416 children, we found participants living closer to secondary hospitals had lower prevalence of ARI (r=0.154, p&amp;lt;0.010) and diarrhea (r=0.228, p&amp;lt;0.001) as well as participants living closer to Maternal Health Centers (MHCs): ARI (r=0.185, p&amp;lt;0.002) and diarrhea (r=0.223, p&amp;lt;0.001) compared to those living near primary facilities. Our random forest model showed distance to have high variable importance in the context of disease prevalence. Our results indicated that participants closer to more basic healthcare facilities reported a higher prevalence of both diarrhea and ARI than those near more urban facilities, highlighting potential public policy gaps in ameliorating rural health.

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