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Description of the patent clusters.

Description of the patent clusters.

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Solar power systems and their related technologies have developed into a globally utilized green energy source. Given the relatively high installation costs, low conversion rates and battery capacity issues, solar energy is still not a widely applied energy source when compared to traditional energy sources. Despite the challenges, there are many i...

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... these seven clusters, the topic contents are defined and symbolic key words are listed in Table 2. Cluster 1 describes grid-connected energy storage systems and silicon based or lithium ion solar cells which are identified by the keywords, such as battery, connect, storage, inverter, charge, grid, wire, install, switch, conduction, AC and DC. ...
Context 2
... 6 describes air processing systems of CSP, especially for fluid heat conduction mediums (keywords: air, heat, storage, water, dust, indoor, channel, purification, sensor and greenhouse). For these seven clusters, the topic contents are defined and symbolic key words are listed in Table 2. Cluster 1 describes grid-connected energy storage systems and silicon based or lithium ion solar cells which are identified by the keywords, such as battery, connect, storage, inverter, charge, grid, wire, install, switch, conduction, AC and DC. ...

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Citations

... However, one of the key challenges is achieving maximum energy harvesting. Trappey et al. 28 presented solar thermoelectric absorbers as a method to harness solar energy and convert it into electricity. However, several issues need addressing to maximize energy harvesting with solar thermoelectric absorbers. ...
... Trappey et al. 28 ...
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... Technological advancement and innovation path reviews provide a methodology for technological improvements and directions for their search. This can be guided by building on R&D strengths and business value, and structured technology exploration can be extended to explore and stimulate other forms of development [74]. It is necessary to carefully consider whether and which industrial and service optimisation processes (including the optimisation of machine resources as part of preventive maintenance) are consistent with sustainable development [75][76][77]. ...
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... This research's objective is to overcome the research gap. Some of the recent OKG research is found to emphasize machine learning and NLP text mining techniques to generate the OKG in various knowledge representations [14,15] as well as employing intelligent ontology-based patent analyses [16][17][18][19]. ...
... These keywords are commonly used for document semantic clustering, topic discovery and their information retrieval [27]. For instance, collected relevant patent data on Cyber-Physical Systems (CPS) and solar energy technology innovations, respectively, are retrieved and topic modeling using LDA is deployed for in-depth patent portfolio analyses [18,28]. ...
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... Among them, neural network models, such as Word2Vec embedding techniques, have been widely applied in patent intelligence research that involves the semantic representation of patent texts. These models excel at capturing and computing the semantic information and associations of textual data through vector representations (Grawe, Martins, & Bonfante, 2017;Kim & Park, 2019;Sarica et al., 2020;Sun & Ding, 2018;Trappey et al., 2019), and can be effective in revealing the process of technological fusion at the semantic level. However, current research on technological fusion lacks semantic metrics necessary for exploring the intrinsic differences of fusion, thereby missing out on a comprehensive and detailed semantic landscape of technology fusion. ...
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... Some research in the literature reviewed the issue of software failures, that may lead to inaccurate approximations [13][14][15]. The use of partial quantitative measurements and inconsistent information on the solar production environment in AI models can allow agents to operate in a variety of situations and restrictions, both at the users and the service provider levels [16]. The reviewed research literature demonstrates that several models have been employed for solar generating, with the benefits and drawbacks explored [1, 17,18], demonstrating that there is a need to establish models and findings that are generally applicable to a wide variety of scenario. ...
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... The country's physical position contributes to a very rapid accumulation of dust [3]. Photovoltaic panels quickly deteriorate when dust accumulates, significantly lowering their ability to produce electricity [4]. The amount of dust that gathers in a specific location depends on both the surrounding circumstances and the characteristics of the dust. ...
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... The potential of solar energy as a renewable, clean and abundant source of energy is well known. Trappey et al. [31] There are a number of issues that need to be addressed in order to maximize energy harvesting with solar thermoelectric absorbers ...
... However, one of the major challenges surrounding solar energy is achieving maximum energy harvesting. Trappey et al. [31] has presented the solar thermoelectric absorbers are one way to harness solar energy and convert it into electricity. However, there are a number of issues that need to be addressed in order to maximize energy harvesting with solar thermoelectric absorbers. ...
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Thermal energy harvesting has seen a rise in popularity in recent years due to its potential to generate renewable energy from the sun. One of the key components of this process is the solar absorber, which is responsible for converting solar radiation into thermal energy. In this paper, a smart performance optimization of energy efficient solar absorber for thermal energy harvesting is proposed for modern industrial environments using solar deep learning model. In this model, data is collected from multiple sensors over time that measure various environmental factors such as temperature, humidity, wind speed, atmospheric pressure, and solar radiation. This data is then used to train a machine learning algorithm to make predictions on how much thermal energy can be harvested from a particular panel or system. In a computational range, the proposed solar deep learning model (SDLM) reached 83.22% of testing and 91.72% of training results of false positive absorption rate, 69.88% of testing and 81.48% of training results of false absorption discovery rate, 81.40% of testing and 72.08% of training results of false absorption omission rate, 75.04% of testing and 73.19% of training results of absorbance prevalence threshold, and 90.81% of testing and 78.09% of training results of critical success index. The model also incorporates components such as insulation and orientation to further improve its accuracy in predicting the amount of thermal energy that can be harvested. Solar absorbers are used in industrial environments to absorb the sun's radiation and turn it into thermal energy. This thermal energy can then be used to power things such as heating and cooling systems, air compressors, and even some types of manufacturing operations. By using a solar deep learning model, businesses can accurately predict how much thermal energy can be harvested from a particular solar absorber before making an investment in a system.
... [116] There-fore, as a necessary step, all the ML algorithms should undergo model validation. [117] Another key issue is data scarcity in the field of datadriven solar materials science. [118] Also, text mining and picture recognition are too considered as solutions for overcoming these primary problems involving poor quantity and quality of the datasets. ...
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... The k-means analysis through the clustering of the most similar technologies forecast promising technologies, but are limited to technological clusters without taking into account technological evolution and exactly which is the most promising patent [38]. A study based on machine learning techniques mapped scientific and technological knowledge related to solar energy [39]. This paper helps researchers and R&D managers to prospect technologies on this domain and reinforces the relevance of patents as an input for research supported by high technology. ...
... The most promoted types of renewable energy in sub-Saharan Africa are the biomass, hydro, wind and solar (Bishoge, Kombe, and Mvile 2020;Hafner, Tagliapietra, and de Strasser 2018). There are many factors that may justify this trend, however, the key factors may be laid as follows: firstly, the region has considerable potential for these types of energy; secondly, the technology for capturing, transforming, conserving and applying these energy systems are well established, developed to commercial stage and affordable for low-income countries (de Jesus Acosta-Silva et al. 2019 ;Hatata, El-Saadawi, and Saad 2019;Kaunda, Kimambo, and Nielsen 2012;Madvar et al. 2019;Sinke 2019;Trappey et al. 2019); thirdly the access to these energy resources is eased by the fact that the power plant can be established on land, unlike ocean-based sources of energy (Kerr et al. 2018). ...
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