Digital elevation model-based convolutional neural network modeling for searching of high solar energy regions

Heo, Ji Yeon; Jung, Jae Hwan; Kim, Byungil; Han, Sang Uk

doi:10.1016/j.apenergy.2020.114588

Cited by 23 publications

(15 citation statements)

References 74 publications

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“…However, is it possible to install solar panels in any area without planning? In Heo et al’s ( 2020 ) study, the convolution neural network model based on digital modeling is proposed to search for areas with sunlight. The annual amount of the sun in the area is examined to determine suitable locations to install solar panels.…”

Section: Methodology Of Applications On Energymentioning

confidence: 99%

The impact of artificial intelligence and digital style on industry and energy post-COVID-19 pandemic

Sharifi

Ahmadi

Ala

2021

Environ Sci Pollut Res

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The SARS-CoV-2 virus caused crises in social, economic, and energy areas and medical life worldwide throughout 2020. This crisis had many direct and indirect effects on all areas of society. In the meantime, the digital and artificial intelligence industry can be used as a professional assistant to manage and control the outbreak of the virus. The present article's objective is to investigate the effects of COVID-19 on each of the various fields of medicine, industry, and energy. What sets this article apart is studying the impact of artificial intelligence and digital style on reducing the damage of this fatal virus. Energy and related industries are of the areas affected by the SARS-CoV-2 virus. The most exciting approach in this article is to encourage countries with economies based on non-renewable energy to develop solar and wind energies. Renewable energies can operate well in the event of another phenomenon such as COVID-19 and reduce the virus's destructive effects and lead to economic prosperity.

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Section: Methodology Of Applications On Energymentioning

confidence: 99%

The impact of artificial intelligence and digital style on industry and energy post-COVID-19 pandemic

Sharifi

Ahmadi

Ala

2021

Environ Sci Pollut Res

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“…(3) Pointed at the complex network structure of REI which is shown in Fig 7 and the different properties of ESO and EC, the main features of different scenes, energy flow density [69] and space distribution [70] are extracted based on clustering [71], deep confidence network and convolution neural network [72]. ESO (e.g Taking the penetration of renewable energy into consideration, the breaking of red switch can be regarded as the independent operation and mutual coupling operation of ESO) and EC (e.g Many red switches closed) are identified and divided by adopting convolution neural [73] and clustering algorithm [74].…”

Section: Research Ideas and Methods Of Esos And Ecsmentioning

confidence: 99%

A Review of Regional Energy Internet Models Considering Complex Dynamic Evolution with Time Delay

Guo,

Xian,

2021

Preprint

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The model research of regional energy internet is of great significance to enhance the flexible coupling transformation of smart city multi-energy system, improve the reliability of energy supply and realize the preferential consumption of renewable energy. Based on the energy self-organization and energy community, from the perspective of complex network to carry out the wisdom of the city region energy complex dynamical network model research, through summarizing the many challenges facing the complex dynamic network institute, puts forward the study area energy Internet time-delay research idea and method of complex dynamic network model, to establish scientific value and practical significance of complex dynamic network model with time-delay system research theory and method.

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“…Many possible usages of DTM have been proposed. natural disasters analysis and prediction (such as Avalanche warning (Jaedicke, Syre, and Sverdrup-Thygeson 2014;Choubin et al 2019) and landslide susceptibility (Wu and Chen 2009)) to high solar energy regions localization (Heo et al 2020) and more (Bolibar et al 2020). In addition, deep learning has been used for the automatic mapping of topographic objects from DTM (Torres et al 2018;Torres, Milani, and Fraternali 2019), and satellite imagery (Li and Hsu 2020).…”

Section: Algorithmic Topographymentioning

confidence: 99%

“…From ancient times, the topographic structure of land was a key aspect in many decisions. The topography of the land is provably correlated with many other tasks: land-use (Sheikh, Van Loon, and Stroosnijder 2014), soil mapping (Scull et al 2003), soil salinity (Divan and Adriaan 2017), landslides (Prakash, Manconi, and Loew 2020) water floods (Hosseiny, Ghasemian, and Amini 2019), avalanches (Jaedicke, Syre, and Sverdrup-Thygeson 2014) and high solar-energy locations (Heo et al 2020). The techniques for perceiving, collecting, and understanding topography has changed significantly in recent years and today, geographic information systems (GIS) are built on many classical and data-driven algorithms.…”

Section: Introductionmentioning

confidence: 99%

Topo2vec: Topography Embedding Using the Fractal Effect

Kavitzky,

Zarecki,

Brusilovsky

et al. 2021

Preprint

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Recent advances in deep learning have transformed many fields by introducing generic embedding spaces, capable of achieving great predictive performance with minimal labeling effort. The geology field has not yet met such success. In this work, we introduce an extension for self-supervised learning techniques tailored for exploiting the fractal-effect in remote-sensing images. The fractal-effect assumes that the same structures (for example rivers, peaks and saddles) will appear in all scales. We demonstrate our method's effectiveness on elevation data, we also use the effect in inference. We perform an extensive analysis on several classification tasks and emphasize its effectiveness in detecting the same class on different scales. To the best of our knowledge, it is the first attempt to build a generic representation for topographic images.

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Digital elevation model-based convolutional neural network modeling for searching of high solar energy regions

Cited by 23 publications

References 74 publications

The impact of artificial intelligence and digital style on industry and energy post-COVID-19 pandemic

The impact of artificial intelligence and digital style on industry and energy post-COVID-19 pandemic

A Review of Regional Energy Internet Models Considering Complex Dynamic Evolution with Time Delay

Topo2vec: Topography Embedding Using the Fractal Effect

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