Identification and assessment of sites for solar farms development using GIS and density based clustering technique- A case of Pakistan

Amjad, Fahd; Shah, Liaqat Ali

doi:10.1016/j.renene.2020.03.083

Cited by 40 publications

(11 citation statements)

References 25 publications

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“…However, other environmental and economic parameters are equally significant in the placement decision of RE systems [34]. Therefore, the most common factors that mimic the global scope of this study have been captured based on an extensive review of publications that addressed the site suitability assessment of wind and solar stations for different regions worldwide [1,[5][6][7]12,20,. Accordingly, WS and Wind Density (WD) were considered technical factors for siting wind stations.…”

Section: Conditioning Factorsmentioning

confidence: 99%

“…For solar PV technologies, Global Solar Atlas v2.6 was used to gather the spatial datasets of AT and Global Horizontal Irradiation (GHI), which is the overall SR received at the horizontal surface on the ground [46]. Notably, the data of Global Wind Atlas and Global Solar Atlas, based on re-analyzed climate data, are widely used in formulating technical factors for siting RE plants [5,6,35]. In addition, CI factor data were acquired from the EarthEnv project database, which is based on 15 years of cloud observations derived from twicedaily MODIS satellite imagery [62].…”

Section: Conditioning Factorsmentioning

confidence: 99%

“…However, extensive lands with special specifications are required to ensure the success of large-scale solar photovoltaic (PV) farms [5]. Similarly, the development of wind plants necessitates careful exploration of sites to meet standard operating conditions [6]. Therefore, a site suitability assessment that considers many aspects is critical in the life cycle of wind and solar systems for a low carbon future.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Global Spatial Suitability Mapping of Wind and Solar Systems Using an Explainable AI-Based Approach

Sachit

Shafri

Abdullah

et al. 2022

IJGI

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An assessment of site suitability for wind and solar plants is a strategic step toward ensuring a low-cost, high-performing, and sustainable project. However, these issues are often handled on a local scale using traditional decision-making approaches that involve biased and non-generalizable weightings. This study presents a global wind and solar mapping approach based on eXplainable Artificial Intelligence (XAI). To the best of the author’s knowledge, the current study is the first attempt to create global maps for siting onshore wind and solar power systems and formulate novel weights for decision criteria. A total of 13 conditioning factors (independent variables) defined through a comprehensive literature review and multicollinearity analysis were assessed. Real-world renewable energy experiences (more than 55,000 on-site wind and solar plants worldwide) are exploited to train three machine learning (ML) algorithms, namely Random Forest (RF), Support Vector Machine (SVM), and Multi-layer Perceptron (MLP). Then, the output of ML models was explained using SHapley Additive exPlanations (SHAP). RF outperformed SVM and MLP in both wind and solar modeling with an overall accuracy of 90% and 89%, kappa coefficient of 0.79 and 0.78, and area under the curve of 0.96 and 0.95, respectively. The high and very high suitability categories accounted for 23.2% (~26.84 million km2) of the site suitability map for wind power plants. In addition, they covered more encouraging areas (24.0% and 19.4%, respectively, equivalent to ~50.31 million km2) on the global map for hosting solar energy farms. SHAP interpretations were consistent with the Gini index indicating the dominance of the weights of technical and economic factors over the spatial assessment under consideration. This study provides support to decision-makers toward sustainable power planning worldwide.

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Section: Conditioning Factorsmentioning

confidence: 99%

Section: Conditioning Factorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Global Spatial Suitability Mapping of Wind and Solar Systems Using an Explainable AI-Based Approach

Sachit

Shafri

Abdullah

et al. 2022

IJGI

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“…It handles import, management, analysis and presentation of spatial information [20]. In context of energy system modelling, GIS analysis are conducted for resource potential studies to exploit knowledge of geo-referenced meteorological parameters such as wind speeds or solar irradiation [21,22].…”

Section: Related Work and Purpose Of Gis Analysismentioning

confidence: 99%

Offshore grid topology optimisation with a geographical information system

Fliegner

2022

J. Phys.: Conf. Ser.

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A novel methodology is investigated to identify and optimise large scale offshore grid topologies connecting multiple wind farms and countries with each other. A Geographical Information System (GIS) is setup to cluster wind farms and create a permissive graph topology. Its purpose is to propose grid layouts with potential hub locations and landing points bottom-up in a fully analytical toolchain, while avoiding manual scenario building. A coupled market model performs the investment optimisation into new lines on the GIS created graph. This two-step procedure is demonstrated at the example of the Baltic Sea Region for the target year 2040. It can be found, that future offshore topologies benefit from bundled transmission paths and many clustered wind farms. A sensitivity analysis reveals that the topology results are sensitive for wind farm location assumptions and pre-defined interconnectors or hubs. Not least, the capability of the onshore grid to integrate the influx of offshore wind power and the level of detail it is modelled in, directly reflects on the topology results for the offshore grid. It is concluded that optimising the future offshore grid is a quest of pan-European scale which benefits heavily from geo data based pre-processing in a GIS.

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“…For instance, Amjad et al proposed a method for identifying and evaluating sites for solar farm development in Pakistan using data from GIS and applying density-based clustering techniques. The study focused on clustering areas with the greatest energy potential and excluded data for infrastructure requirements 14 . However, the method presented in the article is so complicated that it is very difficult to be implement by non-specialist users.…”

Section: Introductionmentioning

confidence: 99%

AutoGIS processing for site selection for solar pond development as efficient water treatment plants in Egypt

Altahan,

Nower

2023

Sci Rep

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The increasing demand for renewable and environmentally friendly energy sources is a top priority for many countries around the world. It is obvious that renewable solar energy will help to meet most of the energy demand in the coming years. A solar pond is a huge Salt artificial Lake that serves as a solar energy collection system. However, site selection is a critical factor that affects the effectiveness and lifetime of a solar pond. Here, we present an innovative methodology for site selection based on three environmental factors, including direct solar irradiance (DNI), temperature, and wind speed. Our approach uses Python programming and clustering analysis with several libraries, including Pandas, Geopandas, Rasterio, Osgeo, and Sklearn, to analyse and process data collected over a 30-year period from NASA power. This method was applied to the geographic boundaries of Egypt, but the methods can be applied to any spatial context if the same dataset is available. The results show that Egypt has a potential land area of 500 km2 suitable for solar ponds construction along the border with Sudan throughout the year, including 2000 km2 in winter (between January and March), 800 km2 in spring (between April and June), 900 km2 in summer (between July and September), and the largest area of 3700 km2 (between October and December), most of which is located in the south of the Eastern Desert and around the Nile River. Notably, the northwestern region, close to the Mediterranean Sea on the border with Libya, exhibits suitability for solar pond development, with consistent performance throughout the year. Our results provide an efficient way for GIS and data processing and could be useful for implementing new software to find the best location for solar ponds development. This could be beneficial for those interested in investing in renewable energy and using solar ponds as an efficient water treatment plant.

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Identification and assessment of sites for solar farms development using GIS and density based clustering technique- A case of Pakistan

Cited by 40 publications

References 25 publications

Global Spatial Suitability Mapping of Wind and Solar Systems Using an Explainable AI-Based Approach

Global Spatial Suitability Mapping of Wind and Solar Systems Using an Explainable AI-Based Approach

Offshore grid topology optimisation with a geographical information system

AutoGIS processing for site selection for solar pond development as efficient water treatment plants in Egypt

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