Optimal Solar Plant Site Identification Using GIS and Remote Sensing: Framework and Case Study

Alhammad, Abdulaziz; Sun, Qian; Tao, Yaguang

doi:10.3390/en15010312

Cited by 29 publications

(11 citation statements)

References 23 publications

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“…Whereas spatial resolution of sensed data in the visible spectrum has significantly increased over last years, reaching 30 cm, data sensed in the infrared spectrum are still around tens of meters. Nowadays many scientific works, aiming to fill the current gap from SRS data and applications' requirements, are available (Alhammad et al, 2022, Jörges et al, 2023. Furthermore, satellite technology is improving at a very quick pace, making possible to get high-spatial resolution data at affordable prices.…”

Section: State Of the Artmentioning

confidence: 99%

A GeoSpatial Information System for Photovoltaic Plants Development and Monitoring

Clementini,

Patrizi,

Santic

et al. 2024

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Photovoltaic (PV) energy production is supposed to hugely increase during current and next decades. PV plants of any extension, technology and power are supposed to be developed and monitored, with the awareness that monitoring phase lasts around 25 years. One of the major challenges of PV plant industry is to choose suitable sites for plant installation and optimal configuration to maximize energy production. In fact, not all places are suitable for solar energy generation, due to several factors, like environmental factors (e.g., solar radiation, wind, shading), technical factors (e.g., distance from the distribution line), economic factors (e.g., development of local economies), and social/political factors (e.g., public acceptance, protected areas). We propose to address the problem of solar PV site selection using a multi-criteria decision-making (MCDM) approach together with geographic information system (GIS) software to determine the most suitable area or alternative, both in case of rural environments and smart city installations. The resulting framework, highly-integrated with several EU sources and initiatives, can be used for crucial steps in PV plants lifecycle, from initial design to monitoring and maintenance.

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Section: State Of the Artmentioning

confidence: 99%

A GeoSpatial Information System for Photovoltaic Plants Development and Monitoring

Clementini,

Patrizi,

Santic

et al. 2024

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Accessibility and proximity to roads are important considerations when selecting the best sites for PV power plants, as they facilitate the transportation of equipment and modules and make maintenance easier during and after construction [43]. Using the Euclidean Proximity method, a maximum radius of 12 km from road lines was considered.…”

Section: Data Collection Selection Of Criteria and Restrictionsmentioning

confidence: 99%

GIS- and AHP-based Decision Systems for Evaluating Optimal Locations of Photovoltaic Power Plants: Case Study of Republic of North Macedonia

Adjiski,

Serafimovski

2024

GaEE

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This study employs a geographic information system (GIS) and an analytical hierarchy process (AHP) to identify optimal locations for photovoltaic (PV) solar farms in the Republic of North Macedonia. It assesses land suitability using six criteria (solar irradiance, aspect, slope, distance from power lines, roads, and urban areas) and six constraints (urban settlements, agricultural zones, national parks, water bodies, steep slopes, elevations above 1500 m). A suitability map was created using a matrix of pairwise comparisons, and the weights for each criterion were calculated. The map was divided into four categories: highly suitable, suitable, less suitable, and unsuitable. The results showed that 11.6% of the study area was classified as being highly suitable, 40.1% as suitable, 3.6% as less suitable, and 0.8% as unsuitable. Additionally, restricted areas (comprised of national parks, residential and agricultural lands, elevations above 1500 m, and water surfaces with 1000 m buffer zones) accounted for 43.7% of the study area. Utilizing just 0.6% of highly suitable land for PV technology could generate approximately 2870 GWh annually, enough to meet the average electricity needs of the industrial sector across the eight administrative regions of R. N. Macedonia. The study offers a replicable GIS-based approach for solar energy planning, contributing to sustainable development and providing insights for integrating solar PV systems into the national energy strategy.

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“…The use of geographic information systems proved useful for manually identifying potential sites for PV plants. We can see in many works with GIS [65,66].…”

Section: Solar Energy Optimal Sitesmentioning

confidence: 99%

Deep learning for photovoltaic panels segmentation

Bouzaachane¹,

Darouichi²,

Guarmah³

2023

Math. Model. Comput.

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Due to advanced sensor technology, satellites and unmanned aerial vehicles (UAV) are producing a huge amount of data allowing advancement in all different kinds of earth observation applications. Thanks to this source of information, and driven by climate change concerns, renewable energy assessment became an increasing necessity among researchers and companies. Solar power, going from household rooftops to utility-scale farms, is reshaping the energy markets around the globe. However, the automatic identification of photovoltaic (PV) panels and solar farms' status is still an open question that, if answered properly, will help gauge solar power development and fulfill energy demands. Recently deep learning (DL) methods proved to be suitable to deal with remotely sensed data, hence allowing many opportunities to push further research regarding solar energy assessment. The coordination between the availability of remotely sensed data and the computer vision capabilities of deep learning has enabled researchers to provide possible solutions to the global mapping of solar farms and residential photovoltaic panels. However, the scores obtained by previous studies are questionable when it comes to dealing with the scarcity of photovoltaic systems. In this paper, we closely highlight and investigate the potential of remote sensing-driven DL approaches to cope with solar energy assessment. Given that many works have been recently released addressing such a challenge, reviewing and discussing them, it is highly motivated to keep its sustainable progress in future contributions. Then, we present a quick study highlighting how semantic segmentation models can be biased and yield significantly higher scores when inference is not sufficient. We provide a simulation of a leading semantic segmentation architecture U-Net and achieve performance scores as high as 99.78%. Nevertheless, further improvements should be made to increase the model's capability to achieve real photovoltaic units.

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Optimal Solar Plant Site Identification Using GIS and Remote Sensing: Framework and Case Study

Cited by 29 publications

References 23 publications

A GeoSpatial Information System for Photovoltaic Plants Development and Monitoring

A GeoSpatial Information System for Photovoltaic Plants Development and Monitoring

GIS- and AHP-based Decision Systems for Evaluating Optimal Locations of Photovoltaic Power Plants: Case Study of Republic of North Macedonia

Deep learning for photovoltaic panels segmentation

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