Machine learning for accelerating the discovery of high-performance low-cost solar cells

Solar energy has the potential to enhance the operation of electronic devices profoundly and is the solution to the most important challenge facing humanity today. Such devices primarily rely on rechargeable batteries to satisfy their energy needs. However, since photovoltaic (PV) technology is a mature and reliable method for converting the Sun’s vast energy into electricity, innovation in developing new materials and solar cell architectures is becoming more important to increase the penetration of PV technologies in wearable and IoT applications. Moreover, artificial intelligence (AI) is touted to be a game changer in energy harvesting. The thesis aims to optimize solar cell performance using various computational methods, from solar irradiance and solar architecture to cost analysis of the PV system. The thesis explores the PV cell architectures that can be used for optimized cost/efficiency trade-offs. In addition, machine learning (ML) algorithms are incorporated to develop reconfigurable PV cells based on switchable complementary metal-oxide-semiconductor (CMOS) addressable switches, such that the output power can be optimized for different light patterns and shading. The first part of the thesis presents a critical literature review of a range of ML techniques applied for estimating solar irradiance, followed by a review on accurately predicting the levelized cost of electricity (LCOE) and return on investment (ROI) of a PV system and lastly, presents a systematic review (SR) on the discovery of solar cells. Furthermore, the literature review consists of a thorough systematic review that reveals that ML techniques can speed up the discovery of new solar cell materials and architectures. The review covers a broad range of ML techniques that focus on producing low-cost solar cells. Additionally, a new classification method is introduced based on data synthesis, ML algorithms, optimization, and fabrication process. The review finds that Gaussian Process Regression (GPR) ML technique with Bayesian Optimization (BO) is the most promising method for designing low-cost organic solar cell architecture. Therefore, the first part of the thesis critically evaluates the existing ML techniques and guides researchers in discovering solar cells using ML techniques. The literature review also discusses the recent research work done for predicting solar irradiance and evaluating the LCOE and ROI of the PV system using various time-series forecasting techniques under ML algorithms. Secondly, the thesis proposes an ML algorithm for accurately predicting solar irradiance using the wireless sensor network (WSN) relying on batteries that need constant replacement and are hazardous waste. Therefore, WSNs with solar energy harvesters that scavenge energy from the Sun are proposed as an alternative solution. Consequently, the ML algorithms that enable WSN nodes to accurately predict the amount of solar irradiance are presented so that the node can intelligently manage its energy. The nodes use the panel’s energy to power its internal electronic components, such as the processor and transmitter, and charge its battery. Accordingly, this helps the node access an exact amount of solar irradiance predictions to plan its energy utilization more efficiently, thereby adjusting the operation schedule depending on the expected solar energy availability. The ML models were based on historical weather datasets from California, USA, and Delhi, India, from 2010 to 2020. In addition, the process of data pre-processing, followed by feature engineering, identification of outliers, and grid search to determine the most optimized ML model, is evaluated. Compared with the linear regression (LR) model, the support vector regression (SVR) model showed accurate solar irradiance forecasting. Moreover, from the predicted output calculated results, it was also found that the models with time duration of 1 year and 1 month have much better forecasting results than 10 years and 1 week, with both root square mean error (RMSE) and mean absolute error (MAE) less than 7% for California, USA. Consecutively, the third part of the thesis evaluates the parameter LCOE using demographic variables. Moreover, LCOE facilitates economic decisions and quantitative comparisons between energy generation technologies. Previous methods for calculating the LCOE were based on fixed singular input values that do not capture the uncertainty associated with determining the financial feasibility of a PV project. Instead, a dynamic model that considers important demographic, energy, and policy data that include interest rates, inflation rates, and energy yield is proposed. All these parameters will undoubtedly vary during a PV system’s lifetime and help determine a more accurate LCOE value. Furthermore, comparisons between different ML algorithms revealed that the ARIMA model gave an accuracy of 93.8% for predicting the consumer price of electricity. Moreover, the proposed model with two case studies from the United States and the Philippines is evaluated in detail. Results from these case studies revealed that LCOE values for the State of California could be almost 30% different (5.03 ¢/kWh for singular values in comparison to 7.09¢/kWh using our ML model), which can distort the risk or economic feasibility of a PV power plant. Additionally, the ML model predicts the ROI of a grid-connected PV plant in the Philippines to be 5.37 years instead of 4.23 years which gives a clear indication to the client for making an accurate estimation for the cost analysis of a PV plant

Estimation of surplus energy in off-grid solar home systems

A recent survey shows that a large percentage of people living in underdeveloped countries do not have access to electricity and are isolated from the rest of the world. Solar energy can help meet the energy demand; however, it has an intermittent nature and relatively high installation cost. The improvement in off-grid Solar Home Systems (SHS) helped many people get access to electricity. However, systems are sized to meet demand on cloudy days, which results in significant wastage of available energy on sunny days, reducing the energy return on investment. This research paper discusses the load requirement of the people living in rural locations. It uses data collected over the last year by collaborating organisations, providing detailed load and solar charging data for off-grid households in Odisha, India. This dataset is analysed to understand the working principle of the installed SHS and the typical daily load profile. Next, the solar data is compared with solar data from online accessible software on an hourly basis and on a 5-minute scale to evaluate the surplus energy. The data shows a significant surplus of solar energy for most of the year that could be used for other low-powered devices. Various methods are discussed to detect surplus energy available during the daytime based on the provided solar data

Achieving 45% efficiency of CIGS/CdS Solar Cell by adding GaAs using optimization techniques

This paper proposes an efficient three-layered p-GaAs/p-CIGS/n-CdS (PPN), a unique solar cell architecture. Copper indium gallium selenide (CIGS)-based solar cells exhibit substantial performance than the ones utilizing cadmium sulfide (CdS). On the contrary, CIGS-based devices are more efficient, considering their device performance, environmentally benign nature, and reduced cost. Therefore, our paper proposes a numerical analysis of the homojunction PPN-junction GaAs solar cell structure along with n-ZnO front contact that was simulated using the Solar Cells Capacitance Simulator (SCAPS-1D) software. Moreover, we investigated optimization techniques for evaluating the effect of the thickness and the carrier density on the performance of the PPN layer on solar cell architecture. Subsequently, the paper discusses the electronic characteristics of adding GaAs material on the top of the conventional (PN) junction, further leading to improved values of the parameters, such as the power conversion efficiency (PCE), open-circuit voltage (VOC), fill factor (FF) and short-circuit current density (JSC) of the solar cell. The most promising results of our study show that adding the GaAs layer using the optimised values of thickness as 5 ({\mu}m) and carrier density as 1*1020 (1/cm) will result in the maximum PCE, VOC, FF, and JSC of 45.7%, 1.16V, 89.52% and 43.88 (mA/m2), respectively, for the proposed solar cell architecture

Contextual Beamforming: Exploiting Location and AI for Enhanced Wireless Telecommunication Performance

The pervasive nature of wireless telecommunication has made it the foundation for mainstream technologies like automation, smart vehicles, virtual reality, and unmanned aerial vehicles. As these technologies experience widespread adoption in our daily lives, ensuring the reliable performance of cellular networks in mobile scenarios has become a paramount challenge. Beamforming, an integral component of modern mobile networks, enables spatial selectivity and improves network quality. However, many beamforming techniques are iterative, introducing unwanted latency to the system. In recent times, there has been a growing interest in leveraging mobile users' location information to expedite beamforming processes. This paper explores the concept of contextual beamforming, discussing its advantages, disadvantages and implications. Notably, the study presents an impressive 53% improvement in signal-to-noise ratio (SNR) by implementing the adaptive beamforming (MRT) algorithm compared to scenarios without beamforming. It further elucidates how MRT contributes to contextual beamforming. The importance of localization in implementing contextual beamforming is also examined. Additionally, the paper delves into the use of artificial intelligence schemes, including machine learning and deep learning, in implementing contextual beamforming techniques that leverage user location information. Based on the comprehensive review, the results suggest that the combination of MRT and Zero forcing (ZF) techniques, alongside deep neural networks (DNN) employing Bayesian Optimization (BO), represents the most promising approach for contextual beamforming. Furthermore, the study discusses the future potential of programmable switches, such as Tofino, in enabling location-aware beamforming

Machine learning for accelerating the discovery of high performance low-cost solar cells: a systematic review

Solar photovoltaic (PV) technology has merged as an efficient and versatile method for converting the Sun's vast energy into electricity. Innovation in developing new materials and solar cell architectures is required to ensure lightweight, portable, and flexible miniaturized electronic devices operate for long periods with reduced battery demand. Recent advances in biomedical implantable and wearable devices have coincided with a growing interest in efficient energy-harvesting solutions. Such devices primarily rely on rechargeable batteries to satisfy their energy needs. Moreover, Artificial Intelligence (AI) and Machine Learning (ML) techniques are touted as game changers in energy harvesting, especially in solar energy materials. In this article, we systematically review a range of ML techniques for optimizing the performance of low-cost solar cells for miniaturized electronic devices. Our systematic review reveals that these ML techniques can expedite the discovery of new solar cell materials and architectures. In particular, this review covers a broad range of ML techniques targeted at producing low-cost solar cells. Moreover, we present a new method of classifying the literature according to data synthesis, ML algorithms, optimization, and fabrication process. In addition, our review reveals that the Gaussian Process Regression (GPR) ML technique with Bayesian Optimization (BO) enables the design of the most promising low-solar cell architecture. Therefore, our review is a critical evaluation of existing ML techniques and is presented to guide researchers in discovering the next generation of low-cost solar cells using ML techniques

Predicting Renewable Energy Resources using Machine Learning for Wireless Sensor Networks

No abstract available

Solar Energy Systems Design in 2D and 3D: A Comparison of User Vital Signs

No abstract available

A Review of Range Extenders in Battery Electric Vehicles: Current Progress and Future Perspectives

Emissions from the transportation sector are significant contributors to climate change and health problems because of the common use of gasoline vehicles. Countries in the world are attempting to transition away from gasoline vehicles and to electric vehicles (EVs), in order to reduce emissions. However, there are several practical limitations with EVs, one of which is the “range anxiety” issue, due to the lack of charging infrastructure, the high cost of long-ranged EVs, and the limited range of affordable EVs. One potential solution to the range anxiety problem is the use of range extenders, to extend the driving range of EVs while optimizing the costs and performance of the vehicles. This paper provides a comprehensive review of different types of EV range extending technologies, including internal combustion engines, free-piston linear generators, fuel cells, micro gas turbines, and zinc-air batteries, outlining their definitions, working mechanisms, and some recent developments of each range extending technology. A comparison between the different technologies, highlighting the advantages and disadvantages of each, is also presented to help address future research needs. Since EVs will be a significant part of the automotive industry future, range extenders will be an important concept to be explored to provide a cost-effective, reliable, efficient, and dynamic solution to combat the range anxiety issue that consumers currently have

Visualising Climate Change using Extended Reality: A Review

No abstract available