Automatic classification of signal regions in 1H Nuclear Magnetic Resonance spectra

The identification and characterization of signal regions in Nuclear Magnetic Resonance (NMR) spectra is a challenging but crucial phase in the analysis and determination of complex chemical compounds. Here, we present a novel supervised deep learning approach to perform automatic detection and classification of multiplets in 1H NMR spectra. Our deep neural network was trained on a large number of synthetic spectra, with complete control over the features represented in the samples. We show that our model can detect signal regions effectively and minimize classification errors between different types of resonance patterns. We demonstrate that the network generalizes remarkably well on real experimental 1H NMR spectra

High resolution global spatiotemporal assessment of rooftop solar photovoltaics potential for renewable electricity generation

Rooftop solar photovoltaics currently account for 40% of the global solar photovoltaics installed capacity and one-fourth of the total renewable capacity additions in 2018. Yet, only limited information is available on its global potential and associated costs at a high spatiotemporal resolution. Here, we present a high-resolution global assessment of rooftop solar photovoltaics potential using big data, machine learning and geospatial analysis. We analyse 130 million km2 of global land surface area to demarcate 0.2 million km2 of rooftop area, which together represent 27 PWh yr−1 of electricity generation potential for costs between 40–280 $MWh−1. Out of this, 10 PWh yr−1 can be realised below 100$ MWh−1. The global potential is predominantly spread between Asia (47%), North America (20%) and Europe (13%). The cost of attaining the potential is lowest in India (66 $MWh−1) and China (68$ MWh−1), with USA (238 $MWh−1) and UK (251$ MWh−1) representing some of the costliest countries

Remote sensing of impervious surface area and its interaction with land surface temperature variability in Pretoria, South Africa

Includes summary for chapter 1-5Pretoria, City of Tshwane (COT), Gauteng Province, South Africa is one of the cities that continues to experience rapid urban sprawl as a result of population growth and various land use, leading to the change of natural vegetation lands into impervious surface area (ISA). These are associated with transportation (paved roads, streets, highways, parking lots and sidewalks) and cemented buildings and rooftops, made of completely or partly impermeable artificial materials (e.g., asphalt, concrete, and brick). These landscapes influence the micro-climate (e.g., land surface temperature, LST) of Pretoria City as evidenced by the recent heat waves characterized by high temperature. Therefore, understanding ISA changes will provide information for city planning and environmental management. Conventionally, deriving ISA information has been dependent on field surveys and manual digitizing from hard copy maps, which is laborious and time-consuming. Remote sensing provides an avenue for deriving spatially explicit and timely ISA information. Numerous methods have been developed to estimate and retrieve ISA and LST from satellite imagery. There are limited studies focusing on the extraction of ISA and its relationship with LST variability across major cities in Africa. The objectives of the study were: (i) to explore suitable spectral indices to improve the delineation of built-up impervious surface areas from very high resolution multispectral data (e.g., WorldView-2), (ii) to examine exposed rooftop impervious surface area based on different colours, and their interplay with surface temperature variability, (iii) to determine if the spatio-temporal built-up ISA distribution pattern in relation to elevation influences urban heat island (UHI) extent using an optimal analytical scale and (iv) to assess the spatio-temporal change characteristics of ISA expansion using the corresponding surface temperature (LST) at selected administrative subplace units (i.e., local region scale). The study objectives were investigated using remote sensing data such as WorldView-2 (a very high-resolution multispectral sensor), medium resolution Landsat-5 Thematic Mapper (TM) and Landsat-8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) at multiple scales. The ISA mapping methods used in this study can be grouped into two major categories: (i) the classification-based approach consisting of an object-based multi-class classification with overall accuracy ~90.4% and a multitemporal pixel-based binary classification. The latter yielded an area under the receiver operating characteristic curve (AUROC) = 0.8572 for 1995, AUROC = 0.8709 for 2005, AUROC = 0.8949 for 2015. (ii) the spectral index-based approach such as a new built-up extraction index (NBEI) derived in this study which yielded a high AUROC = ~0.82 compared to Built-up Area Index (BAI) (AUROC = ~0.73), Built-up spectral index (BSI) (AUROC = ~0.78), Red edge / Green Index (RGI) (AUROC = ~0.71) and WorldView-Built-up Index (WV-BI) (AUROC = ~0.67). The multitemporal built-up Index (BUI) also estimated with AUROC = 0.8487 for 1993, AUROC = 0.8302 for 2003, AUROC = 0.8790 for 2013. This indicates that all these methods employed, mapped ISA with high predictive accuracy from remote sensing data. Furthermore, the single-channel algorithm (SCA) was employed to retrieve LST from the thermal infrared (TIR) band of the Landsat images. The LST overall retrieval error for the entire study generally was quite low (overall root mean square RMSE ≤ ~1.48OC), which signifies that the Landsat TIR used provided good results for further analysis. In conclusion, the study showed the potential of multispectral remote sensing data to quantify ISA and evaluate its interaction with surface temperature variability despite the complex urban landscape in Pretoria. Also, using impervious surface LST as a complementary metric in this research helped to reveal urban heat island distribution and improve understanding of the spatio-temporal developing trend of urban expansion at a local spatial scale.Rapid urbanization because of population growth has led to the conversion of natural lands into large man-made landscapes which affects the micro-climate. Rooftop reflectivity, material, colour, slope, height, aspect, elevation are factors that potentially contribute to temperature variability. Therefore, strategically designed rooftop impervious surfaces have the potential to translate into significant energy, long-term cost savings, and health benefits. In this experimental study, we used the semi-automated Environment for Visualizing Images (ENVI) Feature Extraction that uses an object-based image analysis approach to classify rooftop based on colours from WorldView-2 (WV-2) image with overall accuracy ~90.4% and kappa coefficient ~0.87 respectively. The daytime retrieved surface temperatures were derived from 15m pan-sharpened Landsat 8 TIRS with a range of ~14.6OC to ~65OC (retrieval error = 0.38OC) for the same month covering Lynwood Ridge a residential area in Pretoria. Thereafter, the relationship between the rooftops and surface temperature (LST) were examined using multivariate statistical analysis. The results of this research reveal that the interaction between the applicable rooftop explanatory features (i.e., reflectance, texture measures and topographical properties) can explain over 22.10% of the variation in daytime rooftop surface temperatures. Furthermore, analysis of spatial distribution between mean daytime surface temperature and the residential rooftop indicated that the red, brown and green roof surfaces show lower LST values due to high reflectivity, high emissivity and low heat capacity during the daytime. The study concludes that in any study related to the spatial distribution of rooftop impervious surface area surface temperature, effect of various explanatory variables must be considered. The results of this experimental study serve as a useful approach for further application in urban planning and sustainable development.Evaluating changes in built-up impervious surface area (ISA) to understand the urban heat island (UHI) extent is valuable for governments in major cities in developing countries experiencing rapid urbanization and industrialization. This work aims at assessing built-up ISA spatio-temporal and influence on land surface temperature (LST) variability in the context of urban sprawl. Landsat-5 Thematic Mapper (TM) and Landsat-8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) were used to quantify ISA using built-up Index (BUI) and spatio-temporal dynamics from 1993-2013. Thereafter using a suitable analytical sampling scale that represents the estimated ISA-LST, we examined its distribution in relation to elevation using the Shuttle Radar Topography Mission (SRTM) and also create Getis-Ord Gi* statistics hotspot maps to display the UHI extent. The BUI ISA extraction results show a high predictive accuracy with area under the receiver operating characteristic curve, AUROC = 0.8487 for 1993, AUROC = 0.8302 for 2003, AUROC = 0.8790 for 2013. The ISA spatio-temporal changes within ten years interval time frame results revealed a 14% total growth rate during the study year. Based on a suitable analytical scale (90x90) for the hexagon polygon grid, the majority of ISA distribution across the years was at an elevation range of between >1200m – 1600m. Also, Getis-Ord Gi* statistics hotspot maps revealed that hotspot regions expanded through time with a total growth rate of 19% and coldspot regions decreased by 3%. Our findings can represent useful information for policymakers by providing a scientific basis for sustainable urban planning and management.Over the years, rapid urban growth has led to the conversion of natural lands into large man-made landscapes due to enhanced political and economic growth. This study assessed the spatio-temporal change characteristics of impervious surface area (ISA) expansion using its surface temperature (LST) at selected administrative subplace units (i.e., local region scale). ISA was estimated for 1995, 2005 and 2015 from Landsat-5 Thematic Mapper (TM) and Landsat-8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) images using a Random Forest (RF) algorithm. The spatio-temporal trends of ISA were assessed using an optimal analytical scale to aggregate ISA LST coupled with weighted standard deviational ellipse (SDE) method. The ISA was quantified with high predictive accuracy (i.e., AUROC = 0.8572 for 1995, AUROC = 0.8709 for 2005, AUROC = 0.8949 for 2015) using RF classifier. More than 70% of the selected administrative subplaces in Pretoria experienced an increase in growth rate (415.59%) between 1995 and 2015. LST computations from the Landsat TIRS bands yielded good results (RMSE = ~1.44OC, 1.40OC, ~0.86OC) for 1995, 2005 and 2015 respectively. Based on the hexagon polygon grid (90x90), the aggregated ISA surface temperature weighted SDE analysis results indicated ISA expansion in different directions at the selected administrative subplace units. Our findings can represent useful information for policymakers in evaluating urban development trends in Pretoria, City of Tshwane (COT).Globally, the unprecedented increase in population in many cities has led to rapid changes in urban landscape, which requires timely assessments and monitoring. Accurate determination of built-up information is vital for urban planning and environmental management. Often, the determination of the built-up area information has been dependent on field surveys, which is laborious and time-consuming. Remote sensing data is the only option for deriving spatially explicit and timely built-up area information. There are few spectral indices for built-up areas and often not accurate as they are specific to impervious material, age, colour, and thickness, especially using higher resolution images. The objective of this study is to test the utility of a new built-up extraction index (NBEI) using WorldView-2 to improve built-up material mapping irrespective of material type, age and colour. The new index was derived from spectral bands such as Green, Red edge, NIR1 and NIR2 bands that profoundly explain the variation in built-up areas on WorldView-2 image (WV-2). The result showed that NBEI improves the extraction of built-up areas with high accuracy (area under the receiver operating characteristic curve, AUROC = ~0.82) compared to the existing indices such as Built-up Area Index (BAI) (AUROC = ~0.73), Built-up spectral index (BSI) (AUROC = ~0.78 ), Red edge / Green Index (RGI) (AUROC = ~0.71) and WorldView-Built-up Index (WV-BI) (AUROC = ~0.67). The study demonstrated that the new built-up index could extract built-up areas using high-resolution images. The performance of NBEI could be attributed to the fact that it is not material specific, and would be necessary for urban area mapping.Environmental SciencesD. Phil. (Environmental Sciences

A Comprehensive Method For Coordinating Distributed Energy Resources In A Power Distribution System

Utilities, faced with increasingly limited resources, strive to maintain high levels of reliability in energy delivery by adopting improved methodologies in planning, operation, construction and maintenance. On the other hand, driven by steady research and development and increase in sales volume, the cost of deploying PV systems has been in constant decline since their first introduction to the market. The increased level of penetration of distributed energy resources in power distribution infrastructure presents various benefits such as loss reduction, resilience against cascading failures and access to more diversified resources. However, serious challenges and risks must be addressed to ensure continuity and reliability of service. By integrating necessary communication and control infrastructure into the distribution system, to develop a practically coordinated system of distributed resources, controllable load/generation centers will be developed which provide substantial flexibility for the operation of the distribution system. On the other hand, such a complex distributed system is prone to instability and black outs due to lack of a major infinite supply and other unpredicted variations in load and generation, which must be addressed. To devise a comprehensive method for coordination between Distributed Energy Resources in order to achieve a collective goal, is the key point to provide a fully functional and reliable power distribution system incorporating distributed energy resources. A road map to develop such comprehensive coordination system is explained and supporting scenarios and their associated simulation results are then elaborated. The proposed road map describes necessary steps to build a comprehensive solution for coordination between multiple agents in a microgrid or distribution feeder.\u2

ACTIVE: Towards Highly Transferable 3D Physical Camouflage for Universal and Robust Vehicle Evasion

Adversarial camouflage has garnered attention for its ability to attack object detectors from any viewpoint by covering the entire object's surface. However, universality and robustness in existing methods often fall short as the transferability aspect is often overlooked, thus restricting their application only to a specific target with limited performance. To address these challenges, we present Adversarial Camouflage for Transferable and Intensive Vehicle Evasion (ACTIVE), a state-of-the-art physical camouflage attack framework designed to generate universal and robust adversarial camouflage capable of concealing any 3D vehicle from detectors. Our framework incorporates innovative techniques to enhance universality and robustness, including a refined texture rendering that enables common texture application to different vehicles without being constrained to a specific texture map, a novel stealth loss that renders the vehicle undetectable, and a smooth and camouflage loss to enhance the naturalness of the adversarial camouflage. Our extensive experiments on 15 different models show that ACTIVE consistently outperforms existing works on various public detectors, including the latest YOLOv7. Notably, our universality evaluations reveal promising transferability to other vehicle classes, tasks (segmentation models), and the real world, not just other vehicles.Comment: Accepted for ICCV 2023. Main Paper with Supplementary Material. Project Page: https://islab-ai.github.io/active-iccv2023

INCREASED PENETRATION OF DISTRIBUTED ROOF-TOP PHOTOVOLTAIC SYSTEMS IN SECONDARY LOW VOLTAGE NETWORKS: INTERCONNECTION IMPACT ANALYSIS AND MITIGATION

The worsening global climatic condition has necessitated increased investments in renewable energy resources and in turn increased penetration of these resources in electricity grids worldwide. Distributed photovoltaic (PV) energy is one of the rapidly growing and viable forms of renewable energy. Distributed PV systems currently exist in two modes, a few numbers of large or utility scale systems and a plethora of small or residential scale roof-top systems which are rapidly growing in terms of number. Residential systems are alternatively called Behind the Meter (BTM) systems because they are not directly monitored by utility operators and are therefore invisible vis-à-vis their performances. While an individual Behind-The-Meter (BTM) system's size holds little significance in comparison to the inertia of the utility grid, the collective presence of numerous interconnected BTM systems within a single feeder has the potential to jeopardize the stability and security of utility operations. Conventional protective devices within distribution networks are designed to accommodate a unidirectional downstream power flow. However, as the integration of PV generators into utility grids intensifies, the prospect of reverse or upstream power flow becomes more probable. This development raises various apprehensions, including the potential for voltage level breaches and a notable reduction in the operational longevity of these devices. BTM systems generally have a wide geographical coverage within a region and each system operates independently of others as well as the fact that their real-time performances are concealed in the net-load data relayed by electricity meters. Consequently, traditional forecasting methods have proved insufficient in predicting the outputs of PV systems on a regional level requiring the development of spatial aggregation approaches. Three basic sub-areas aimed at increasing the penetration of BTM PV generators in utility grids are the principal focus of this study. The sub-areas include performance analysis of BTM systems; day-ahead regional scale PV power forecasting model and a PV ramp events extraction model. The first sub-area tries to address the challenges with small scale solar power performance data access on a regional basis. The performance analysis was aimed at evaluating the credibility and reliability of BTM data from public webpages and their representativeness for high profile research. Consequently, this sub-area proposes and investigates the feasibility of the instrumentation of every invisible solar system for near real-time data monitoring. The investigation involved detailing the convergence between simulated and reported power outputs on a spectrum of orientation and tilt angles. Two simulation methods as well as two case studies public web repositories from which a subset of representative solar sites were adopted to provide a basis for the proposed approach. The results show that the proposed model is viable and feasible depending on the participation of certain key stakeholders in electricity market discourses. Day-ahead forecasts are required by electricity market investors to make informed decisions on the trading floor. Whereas it is relatively easier to predict the performance of a few large-scale PV systems, a large number of small-scale PV systems with a wide geographical spread poses more challenges because they are not metered for real-time monitoring. This sub-area proposes an artificial neural network (ANN)-based model to achieve regional-scale day-ahead PV power forecasts from numerical weather predictions of weather variables excluding solar irradiance as inputs. The model was first implemented by dividing a region into clusters and selecting a representative site for each cluster using data dimension reduction algorithms. Solar irradiance forecasts were then generated for each representative PV system and the corresponding PV power was simulated. The cluster power output was obtained using a linear upscaling model and summed to produce regional-scale power forecasts. The model’s accuracy is validated using power generation data of several distributed systems in California. Compared with available benchmark models with similar objectives, the proposed model performed significantly better. Insufficient information on solar power ramp events is counterproductive to the operational flexibility and economics of electricity grids. Accurate solar ramp extraction and characterization in terms of ramp magnitude, rate and duration are useful to power system operators for system planning especially with regards to ensuring supply security and sizing ancillary services. The characterization of ramp events in historical databases is also useful for testing forecast models’ accuracy in predicting significant solar ramp events that are of more concern to utility operators. A novel technique for solar power ramp events (SPREs) detection using the modified swinging door algorithm (MSDA) considering different time resolutions and weather profile is proposed in this sub-area. Firstly, the swinging door algorithm (SDA) is used to create ramp segments of the solar power data that are collected from different randomly selected systems. Afterwards, the power generation variability patterns of these segments are studied. The SDA is then modified to merge adjacent segments according to the observations made by comparing the variability patterns. The solar power data simulated from irradiances measured with different time resolutions is utilized for performance validation and testing. The proposed technique shows much improved performance than existing detection algorithms with respect to the number of detected ramps, detection accuracy and in some cases, computation time

Getting emotional or cognitive on social media? Analyzing renewable energy technologies in Instagram posts

Renewable energy development is a widely and intensively discussed topic, though it is still unclear which exactly variables may influence people's evaluation of the phenomenon. There is a need to study the general public's knowledge, emotions, and cognitions linked to energy technologies especially in the context of advanced inventions. Social media is a powerful communication tool which has a huge impact on studying public opinions. This study aims to describe linguistic connections through an analysis of 1500 Instagram posts, assuming and interpreting emotional and/or cognitive words. Using a socio-cognitive approach, this research explores the salient words under a set of pre-specified renewable energy technology (RET) hashtags. Building on the appraisal theories of emotions, this research investigates the coexistence of several energy technologies (solar, wind, biomass, and geothermal) and powerlines. The results showed the highest linguistic interconnection between solar and wind energy posts. Furthermore, powerlines were not linguistically connected to the RETs, as they are not included in the schema or not salient when people write posts about renewable energy. Solar, wind, and geothermal posts evoked more emotional and positive emotions than the other RETs and powerlines. Instead, biomass posts had a high frequency of cognitive processes and causal words. Powerline posts were linked to the words of risk, body, health, and biological process showing a great concern for health and perceived threat. These differences in the words used can be a guide to understanding peoples' reactions and communication for each of the energy sources. This study, taking both emotions and cognitions into account, explains different types of considerations towards energy projects

Energy Efficiency in Buildings: Both New and Rehabilitated

Buildings are one of the main causes of the emission of greenhouse gases in the world. Europe alone is responsible for more than 30% of emissions, or about 900 million tons of CO2 per year. Heating and air conditioning are the main cause of greenhouse gas emissions in buildings. Most buildings currently in use were built with poor energy efficiency criteria or, depending on the country and the date of construction, none at all. Therefore, regardless of whether construction regulations are becoming stricter, the real challenge nowadays is the energy rehabilitation of existing buildings. It is currently a priority to reduce (or, ideally, eliminate) the waste of energy in buildings and, at the same time, supply the necessary energy through renewable sources. The first can be achieved by improving the architectural design, construction methods, and materials used, as well as the efficiency of the facilities and systems; the second can be achieved through the integration of renewable energy (wind, solar, geothermal, etc.) in buildings. In any case, regardless of whether the energy used is renewable or not, the efficiency must always be taken into account. The most profitable and clean energy is that which is not consumed

Studies in urban informatics: tools and techniques to explore socio-ecological urban systems

This work details the emerging discipline of urban informatics and the diverse set of tools, data and techniques necessary for the quantitative analysis of urban systems. Three studies are presented based on distinct data types including administrative data, user-generated data and sensor data. The first study focuses on urban waste management and demonstrates how existing administrative datasets can be used to forecast waste generation, which can be useful for optimizing waste collection efforts and developing future waste reduction strategies. The second study shifts from administrative data to focus on the benefits of public participation and the challenges of working with user-generated data. The final study presents a multi-week calibration campaign to evaluate calibration techniques and the quality of data generated by a low-cost air quality monitoring platform in order to increase the spatial resolution of PM2.5 measurements in an urban environment. The results from and evaluation of these studies highlight the potential for these urban data streams to provide new in-sight into socio-ecological urban systems, and create new opportunities for local governments to operate in a more effective, efficient and sustainable way