Solar resource assessment in Ireland, comparison of satellite data versus ground measurement

<p>Accurate solar resource assessments are essential to project a solar photovoltaic (PV) plant’s energy production – and ultimately forecast its revenue.</p><p>Solar resource assessments are the bedrock of the ‘Revenue’ line in PV financial models. In today’s competitive financing environment, the assumptions underlying solar resource assessment often have make-or-break impact on project valuations. It’s critical that investors trust the numbers provided.</p><p>To quantify solar resource, industry typically compares different irradiation databases derived from multiple physical sources – whether measurements or satellite images. There is always some level of scatter; in Western Europe this is often around 3%, after excluding outliers.  Satellite database are never as good as accurate ground measurement.  And the rather narrow variation observed is due to past calibration of satellite derived model with data from weather stations.  The reality can be different when it comes to Ireland. </p><p>The solar sector is currently experiencing a rapid development in the Republic of Ireland, making the yield assessment and by extension the solar resource estimation key for the bankability of the projects.</p><p>The aim of our work was the validate the accuracy of different databases, available in Ireland.</p><p>The first step of this analysis will be to qualify our data sources. Everoze and Brightwind have access to measurement campaigns from multiple solar projects in Ireland. All the gathered dataset will be processed, applying state of the art quality control, to retain only trustable information.  The quality check will also include the sensors themselves, with a verification of the accuracy and calibration certificates of the different pieces of equipment.</p><p>In a second step, the qualified datasets will be used to compare satellite derived data.  We plan to use CAMS, SolarGIS and Meteonorm.  The intention is to categorise our results in regions, classified based on the difference in annual irradiation between different databases in order to reduce uncertainty – and ultimately boost investor confidence in energy yield assessments.</p>

Sustainable Risk Assessment of Resource Industry at Provincial Level in China

The development of China’s resource industry is facing great pressures from industrial structure adjustment and environmental restraints, and the sustainable risk of the provincial resource industry is different. Considering the development of the resource industry and environmental pressure, this article selects the panel data of 31 provinces from 2015 to 2019 to construct an index evaluation system with six dimensions: influence, induction, supply and demand safety, regional pollution emission, environment quality, and pollution control. The results showed that Shanxi, Anhui, Jiangsu, and Shanghai had the highest sustainable risk in the resource industry, while Heilongjiang, Jilin, Tianjin, Fujian, Jiangxi, Hunan, Guizhou, Sichuan, and Qinghai had the lowest sustainable risk. The resource industry model of all the provinces is divided into sustainable, industrial, ecological, and unsustainable. Finally, this article puts forward reasonable suggestions for the four scenarios and argues that the balanced development of the resource industry sector and environmental protection is conducive to reducing the sustainable risks of the resource industry.

Mineral prospectivity mapping using a VNet convolutional neural network

Major mineral discoveries have declined in recent decades, and the natural resource industry is in the process of adapting and incorporating novel technologies such as machine learning and artificial intelligence to help guide the next generation of exploration. One such development is an artificial intelligence architecture called VNet that uses deep learning and convolutional neural networks. This method is designed specifically for use with geoscience data and is suitable for a multitude of exploration applications. One such application is mineral prospectivity in which the machine is tasked with identifying the complex pattern between many layers of geoscience data and a particular commodity of interest, such as gold. The VNet algorithm is designed to recognize patterns at different spatial scales, which lends itself well to the mineral prospectivity problem of there often being local and regional trends that affect where mineralization occurs. We test this approach on an orogenic gold greenstone belt setting in the Canadian Arctic where the algorithm uses gold values from sparse drill holes for training purposes to predict gold mineralization elsewhere in the region. The prospectivity results highlight new target areas, and one such target was followed up with a direct-current induced polarization survey. A chargeability anomaly was discovered wherein the VNet had predicted gold mineralization, and subsequent drilling encountered a 6 g/t Au intercept within 10 m of drilling that averaged more than 1.0 g/t Au. Although most of the prospectivity targets generated from VNet were not drill tested, this first intercept helps validate the approach. We believe this method can help maximize the use of existing geoscience data for successful and efficient exploration programs in the future.