Over 4,400 large-scale solar photovoltaic (LSPV) facilities operate in the United States as of December 2021, representing more than 60 gigawatts of electric energy capacity. Of these, over 3,900 are ground-mounted LSPV facilities with capacities of 1 megawatt direct current (MW) or more. Ground-mounted LSPV installations continue increasing, with more than 400 projects appearing online in 2021 alone; however, a comprehensive, publicly available georectified dataset including spatial footprints of these facilities is lacking. The United States Large-Scale Solar Photovoltaic Database (USPVDB) was developed to fill this gap. Using US Energy Information Administration (EIA) data, locations of 3,699 LSPV facilities were verified using high-resolution aerial imagery, polygons were digitized around panel arrays, and attributes were appended. Quality assurance and control were achieved via team peer review and comparison to other US PV datasets. Data are publicly available via an interactive web application and multiple downloadable formats, including: comma-separated value (CSV), application programming interface (API), and GIS shapefile and GeoJSON.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10632423 | PMC |
http://dx.doi.org/10.1038/s41597-023-02644-8 | DOI Listing |
Sci Rep
January 2025
State-owned Jiaozuo Forest Farm, Jiaozuo, 454000, Henan, China.
Accurately estimating forest carbon sink and exploring their climate-driven mechanisms are critical to achieving carbon neutrality and sustainable development. Fewer studies have used machine learning-based dynamic models to estimate forest carbon sink. The climate-driven mechanisms in Shangri-La have yet to be explored.
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January 2025
National Engineering Laboratory for Modern Silk, College of Textile and Clothing Engineering, Soochow University, Suzhou, Jiangsu 215123, China.
Flexible thermoelectric systems capable of converting human body heat or solar heat into sustainable electricity are crucial for the development of self-powered wearable electronics. However, challenges persist in maintaining a stable temperature gradient and enabling scalable fabrication for their commercialization. Herein, we present a facile approach involving the screen printing of large-scale carbon nanotube (CNT)-based thermoelectric arrays on conventional textile.
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January 2025
School of Economics and Management, North University of China, Taiyuan, China.
The exploration of digital transformation peer effects on the innovation performance of emerging industries is crucial for analyzing the underlying mechanisms of digital transformation, optimizing resource allocation among peer enterprises, and enhancing industrial competitiveness. This study empirically examines the influence of digital transformation peer effects on the innovation performance of the photovoltaic industry, using data from 150 photovoltaic companies listed in Shanghai and Shenzhen between 2011 and 2022. The study found that: (1) The digital transformation of the photovoltaic industry is influenced by regional and industry-specific peer effects.
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January 2025
Computational Data Science Program, College of Computational and Natural Science, Addis Ababa University, P.O. Box 1176, Addis Ababa, Ethiopia.
This study presents an in-depth analysis and evaluation of the performance of a standard 200 W solar cell, focusing on the energy and exergy aspects. A significant research gap exists in the comprehensive integration of numerical models with advanced machine-learning approaches, specifically emotional artificial neural networks (EANN), to simulate and optimize the electrical characteristics and efficiency of solar panels. To address this gap, a numerical model alongside a novel EANN was employed to simulate the system's electrical characteristics, including open-circuit voltage, short-circuit current, system resistances, maximum power point characteristics, and characteristic curves.
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December 2024
School of Electrical and Information, Hunan University, Changsha, 410083, China.
Accurately predicting solar power to ensure the economical operation of microgrids and smart grids is a key challenge for integrating the large scale photovoltaic (PV) generation into conventional power systems. This paper proposes an accurate short-term solar power forecasting method using a hybrid machine learning algorithm, with the system trained using the pre-trained extreme learning machine (P-ELM) algorithm. The proposed method utilizes temperature, irradiance, and solar power output at instant i as input parameters, while the output parameters are temperature, irradiance, and solar power output at instant i+1, enabling next-day solar power output forecasting.
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