Computer Programming to Estimate the Global Daily and Hourly solar Radiation of any location around the Globe

IF 3.4 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Data Science and Analytics Pub Date : 2023-05-20 DOI:10.18517/ijods.3.2.101-106.2022

Natnale Sitotaw Asefa

引用次数: 0

Abstract

Solar data collection and radiation analysis are basic to study several solar energy-related types of research. But, most researchers get difficulty while collecting such valuable data. In most developing countries and rural areas of the world, it is a bottleneck situation to get the ground-level solar radiation data. The intended purpose of this research is to develop a code that can estimate the daily and hourly variation of global solar radiation for any location around the globe. A python script is developed to make users flexible to use for any climatic region of the world. As an illustration, the developed code is implemented for three regions namely, Algeria, Pakistan, and Nigeria that researchers developed a correlation for each climatic region. The output shows that it is possible to use a computer program for any climatic region that researchers or any users want to find the estimated global daily and hourly solar radiation data.

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估计全球任何地点的每日和每小时太阳辐射的计算机编程

太阳能数据收集和辐射分析是研究太阳能相关研究的基础。但是，大多数研究人员在收集这些有价值的数据时遇到了困难。在世界上大多数发展中国家和农村地区，地面太阳辐射数据的获取是一个瓶颈。这项研究的预期目的是开发一种代码，可以估计全球任何地点的全球太阳辐射的每日和每小时变化。开发了一个python脚本，使用户能够灵活地用于世界上任何气候区域。作为示例，开发的代码是为三个地区实现的，即阿尔及利亚、巴基斯坦和尼日利亚，研究人员为每个气候区域开发了相关性。输出结果表明，研究人员或任何用户都可以使用任何气候区域的计算机程序来查找估计的全球每日和每小时太阳辐射数据。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Data Science and Analytics Multiple-

CiteScore

6.40

自引率

8.30%

发文量

期刊介绍： Data Science has been established as an important emergent scientific field and paradigm driving research evolution in such disciplines as statistics, computing science and intelligence science, and practical transformation in such domains as science, engineering, the public sector, business, social science, and lifestyle. The field encompasses the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. It also tackles related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation.The International Journal of Data Science and Analytics (JDSA) brings together thought leaders, researchers, industry practitioners, and potential users of data science and analytics, to develop the field, discuss new trends and opportunities, exchange ideas and practices, and promote transdisciplinary and cross-domain collaborations. The journal is composed of three streams: Regular, to communicate original and reproducible theoretical and experimental findings on data science and analytics; Applications, to report the significant data science applications to real-life situations; and Trends, to report expert opinion and comprehensive surveys and reviews of relevant areas and topics in data science and analytics.Topics of relevance include all aspects of the trends, scientific foundations, techniques, and applications of data science and analytics, with a primary focus on:statistical and mathematical foundations for data science and analytics;understanding and analytics of complex data, human, domain, network, organizational, social, behavior, and system characteristics, complexities and intelligences;creation and extraction, processing, representation and modelling, learning and discovery, fusion and integration, presentation and visualization of complex data, behavior, knowledge and intelligence;data analytics, pattern recognition, knowledge discovery, machine learning, deep analytics and deep learning, and intelligent processing of various data (including transaction, text, image, video, graph and network), behaviors and systems;active, real-time, personalized, actionable and automated analytics, learning, computation, optimization, presentation and recommendation; big data architecture, infrastructure, computing, matching, indexing, query processing, mapping, search, retrieval, interoperability, exchange, and recommendation;in-memory, distributed, parallel, scalable and high-performance computing, analytics and optimization for big data;review, surveys, trends, prospects and opportunities of data science research, innovation and applications;data science applications, intelligent devices and services in scientific, business, governmental, cultural, behavioral, social and economic, health and medical, human, natural and artificial (including online/Web, cloud, IoT, mobile and social media) domains; andethics, quality, privacy, safety and security, trust, and risk of data science and analytics