Increase Operational Efficiency Research Articles

Systematic Review on Phase-Shift Optimization Strategies of Dual Active Bridge based DC-DC Converter

Bidirectional DC-DC converters (BDC) between high and low voltage buses are the most promising in latest research for their use cases in micro-grids. Specifically, the dual active bridge (DAB) topology of BDC has vast number of applications and its ability to transmit power in both directions makes it effective for utilities in consumer-prosumer setup. In recent years, a substantial amount of research has been conducted to eliminate one of the crucial drawbacks of DABs which is the mitigation of DC-Bias current resulting from practical limitations of switches. DC-Bias current is the leading cause of switch stress resulting in reduced converter reliability and efficiency. Numerous strategies have been devised to overcome this issue which includes methods like flux suppression, dynamic optimization, transient control, and piecewise linear transient phase shift optimization. In each of these categories several studies devised various techniques to increase operation efficiency, the dynamic response optimization (including model predictive controls and ANNs) can also be seen in recent research trends. This state-of-the-art systematic review is conducted on the recent developments in phase shift optimization techniques developed to increase the overall efficiency of isolated DABs. Considering it as a wide research discipline, the intervention criteria of searching the relevant primary studies is chosen to be strict. Some systematic reviews accommodate all the recent studies of this area, this review particularly is conducted to enhance the view only towards the phase shift-based optimization strategies and provide boarder insights of DAB optimization to researchers and engineers. It was seen that other than 3 basic phase shift techniques (SPS, DPS, TPS), studies with modified versions enhance the efficiency of DAB by mitigating the DB current.

Automated credit assessment framework using ETL process and machine learning.

In the current business scenario, real-time analysis of enterprise data through Business Intelligence (BI) is crucial for supporting operational activities and taking any strategic decision. The automated ETL (extraction, transformation, and load) process ensures data ingestion into the data warehouse in near real-time, and insights are generated through the BI process based on real-time data. In this paper, we have concentrated on automated credit risk assessment in the financial domain based on the machine learning approach. The machine learning-based classification techniques can furnish a self-regulating process to categorize data. Establishing an automated credit decision-making system helps the lending institution to manage the risks, increase operational efficiency and comply with regulators. In this paper, an empirical approach is taken for credit risk assessment using logistic regression and neural network classification method in compliance with Basel II standards. Here, Basel II standards are adopted to calculate the expected loss. The required data integration for building machine learning models is done through an automated ETL process. We have concluded this research work by evaluating this new methodology for credit risk assessment.

Evolution of Rail Contact Fatigue on Crossing Nose Rail Based on Long Short-Term Memory

The share of rail transport in world transport continues to rise. As the number of trains increases, so does the load on the railway. The rails are in direct contact with the loaded wheels. Therefore, it is more easily damaged. In recent years, domestic and foreign scholars have conducted in-depth research on railway damage detection. As the weakest part of the track system, switches are more prone to damage. Assessing and predicting rail surface damage can improve the safety of rail operations and allow for proper planning and maintenance to reduce capital expenditure and increase operational efficiency. Under the premise that functional safety is paramount, predicting the service life of rails, especially turnouts, can significantly reduce costs and ensure the safety of railway transportation. This paper understands the evolution of contact fatigue on crossing noses through long-term observation and sampling of crossing noses in turnouts. The authors get images from new to damaged. After image preprocessing, MPI (Magnetic Particle Imaging) is divided into blocks containing local crack information. The obtained local texture information is used for regression prediction using machine-supervised learning and LSTM network (Long Short-Term Memory) methods. Finally, a technique capable of thoroughly evaluating the wear process of crossing noses is proposed.

Evaluation of Agricultural Machinery Operational Benefits Based on Semi-Supervised Learning

Judging the efficiency of agricultural machinery operations is the basis for evaluating the utilization rate of agricultural machinery, the driving abilities of operators, and the effectiveness of agricultural machinery management. A range of evaluative factors—including operational efficiency, oil consumption, operation quality, repetitive operation rate, and the proportion of effective operation time—must be considered for a comprehensive evaluation of the quality of a given operation, an analysis of the causes of impact, the improvement of agricultural machinery management and an increase in operational efficiency. In this study, the main factors affecting the evaluation of agricultural machinery operations are extracted, and information about the daily operations of particular items of agricultural machinery is taken as a data source. As regards modeling, a subset of data can be scored manually, and the remaining data is predicted after the training of the relevant model. With a large quantity of data, manual scoring is not only time-consuming and labor-intensive, but also produces sample errors due to subjective factors. However, a small number of samples cannot support an accurate evaluation model, and so in this study a semi-supervised learning method was used to increase the number of training samples and improve the accuracy of the least-squares support vector machine (LSSVM) training model. The experiment used 33,000 deep subsoiling operation data, 500 of which were used as training samples and 500 as test samples. The accuracy rate of the model obtained using 500 training samples was 94.43%, and the accuracy rate achieved with this method with an increased number of training samples was 96.83%. An optimal combination of agricultural machinery and tools is recommended owing to their operational benefits in terms of reduced costs and improved operating capacity.

Engaging Pathologists in a Peer-to-Peer Learning Collaborative on HER2-Low Breast Cancer

Abstract Introduction/Objective Recent advances in research have shown clinical effectiveness when targeting the lower range of HER2 expression (ie, HER2-low) in patients with metastatic breast cancer. American Society for Clinical Pathology worked in collaboration with Q Synthesis to develop a peer-to-peer learning collaborative to proactively prepare pathologists for HER2-low. This CME project was supported by an educational grant from AstraZeneca Pharmaceuticals LP and Daiichi Sankyo Inc. Methods/Case Report 38 pathologists participated in the ASCP HER2 Breast Trailblazers. For foundational knowledge, learners completed online modules covering scientific updates on HER2-low. Through small-group, case- based discussions, learners reviewed operational challenges and opportunities to prepare for HER2-low. They applied this knowledge to lead projects at their own institutions focusing on the anticipated changes around HER2-low. Results (if a Case Study enter NA) The learners identified the following challenges and opportunities: Defining HER2-low: Several learners had heard misconceptions around the definition of HER2-low. Recent studies have defined HER2-low as IHC 1+ or IHC 2+ with ISH-negative. Interobserver concordance with IHC 0 vs 1+: Several learners discussed the challenges around interpreting IHC 0 vs 1+. They felt that some pathologists may need guided feedback to improve their diagnostic skills. Use of IHC vs. ISH: Several learners only performed ISH for HER2 testing on all breast cancer samples. If HER2-low emerges as a third category, they would need to return to IHC. Implications for non-metastatic breast cancer: Recent HER2-low studies have focused on patients with metastatic breast cancer. If HER2-low emerges as a third category, it is unclear whether this designation will also be used in patients who have early-stage breast cancer. Leadership As pathologists prepare for HER2-low, they have opportunities to lead projects to assess and improve IHC interobserver concordance, coach others on IHC interpretation, increase operational efficiency, strength communication skills, and build up the team by proactively anticipating challenges around HER2-low. Conclusion HER2-low breast cancer may be emerging as a new category. Through a peer-to-peer learning collaborative, pathologists identified ways to proactively prepare and demonstrate leadership so that cancer centers and laboratories may be ready to embrace a new paradigm of HER2 classification in breast cancer.

Load of the wagon-platform for transportation of bulk cargoes

Purpose. To substantiate the improvement of the load-bearing element of the wagon-platform for the possibility of transporting bulk cargoes. Methodology. In order to be able to transport bulk cargo on the wagon-platform, it is proposed to install a composite boiler module on it. In order to determine the dynamic load of the improved load-bearing structure of the wagon-platform, mathematical modeling was performed. The mathematical model formed by professor Bohomaz H.I. was used. However, within the framework of the research this model was refined to determine the load of the wagon-platform of the proposed design. The solution of the system of differential equations is carried out in the MathCad software package. To do this, the mathematical model was reduced to the normal Cauchy form, and then integrated by the Runge-Kutta method. The obtained acceleration is taken into account when calculating the strength of the advanced load-bearing structure of the wagon-platform. The calculation is performed in the SolidWorks Simulation software package, which implements the finite element method. Also, within the research the modal analysis of a load-bearing structure of the wagon-platform is carried out. Findings. Based on the calculations, it is established that the acceleration acting on the load-bearing structure of the wagon-platform car is 0.38 g, i.e. it is within acceptable limits. The results of the calculation of the strength of the improved design of the wagon-platform showed that the maximum equivalent stresses occur in the area of interaction of the spine beam frame with the pivot and is about 340 MPa, the maximum displacement made 8.6 mm. That is, the obtained stresses do not exceed the yield strength of the structural material. The results of the modal analysis showed that the values of the natural frequencies of oscillations are within acceptable limits, because the first natural frequency has a value greater than 8 Hz. Originality. The scientific substantiation of improvement of a load-bearing structure of a universal wagon-platform to transportations of bulk cargoes is carried out. Practical value. The conducted research will promote increase in efficiency of operation of railway transport and creation of developments concerning planning of innovative designs of a rolling stock.

An Operational Efficiency Assessment of E-Trike and Motorized Tricycles as Public Utility Vehicles

The study focused on the operational efficiency of e-trikes and motorized tricycles as public utility vehicles, leading to the analytical comparison of the data gathered. The comparative analysis on both tricycles reveals that e-trikes are more efficient and safer, consume zero fuel, and almost double in passenger capacity when compared to the motorized tricycle. Further, e-trike can enter narrower streets or alley easily for having lesser width. On the other hand, motorized tricycles are faster and can reach higher kilometrage and with a shorter length, it has no problems on approaching slopes and bumps. M/M/1 was used to evaluate the service performance of the electrical and motorized tricycles in the terminal. The results show that the e-trike terminal service rate is more efficient than that of the motorized terminal where commuters spent lesser time waiting to be served. The study recommends the need to understand further the effects, advantages, and disadvantages of both tricycles as regard the planned implementation in the mode of public transportation system. Additionally, further study may be needed focusing on the economic and environmental sustainability, and the use of renewable resources to contribute on the sustainable development and increase operational efficiency of PUVs.

A Review of Board Level IT Governance: A Taxonomy to Inform the Quality of Financial Reports

Financial reports are important documents that stakeholders never ignore, and Information Technology (IT) is utilized throughout firms to increase operation efficiency. However, prior researches studies Information Technology Governance (ITG) that relates to cybersecurity risks, thus, this paper takes step further to reviews board level ITG literature that is relevant to Corporate Governance (CG) and discuss how it impacts on the quality of financial reports. This paper systematically presents a taxonomy of research encompassing the focus areas: performance, cybersecurity losses, more accurate earnings forecasts, lower audit fees based upon prior studies in ITG over the period 2010-2020. The main finding is that results reveal a lack of board level ITG under CG research and a lack of the relation between board level ITG and financial results. Therefore, this paper discusses research perspectives and identify avenues for future research in last part, research perspectives and future research could focus on how to improve CG based on IT and the financial consequences of CG with IT.

Technology Focus: Data Analytics (October 2022)

Oil and gas companies are starting to invest more in the energy transition and establish emissions-reduction goals, such as an end to routine gas venting and flaring, with some going as far as setting net-zero goals by mid-century. A wide range of climate technologies with varying degrees of maturity levels are needed to pave the road to net zero. These include electrification and grid decarbonization, advances in battery technology, blue and green hydrogen fuels, bioenergy, carbon capture use and storage, and mitigating emissions of potent greenhouse gases such as methane. The COVID-19 pandemic has helped accelerate the pace of the digital transformation in the oil and gas industry. Digitalization is, in effect, a part of the broader energy transition that is occurring in the industry. This includes a move from on-premises data centers to the cloud, building digital twins of physical assets, process automation using the Internet of Things (IoT), leveraging reams of data from oil and gas operations for artificial intelligence/machine leaning (AI/ML) applications, cloud-based high-performance computing for applications such as seismic imaging for carbon capture and storage, and predictive maintenance to fix leaky equipment to reduce the environmental footprint of operations. These technologies serve to improve productivity, increase operational efficiency, reduce downtime, increase cost savings, and reduce the carbon intensity of operations. The International Energy Agency report “The Oil and Gas Industry in Energy Transitions” states that reducing methane leaks to the atmosphere is the single most important and cost-effective way for the industry to bring down these emissions. This is a domain that can benefit significantly from the use of AI/ML techniques for leak detection and remediation (LDAR) efforts. ML techniques such as computer vision and anomaly detection can be used to identify both large and localized methane leaks from remote-sensing data and data streaming in from IoT sensors in the field. A combination of data modalities, with varying spatiotemporal resolutions, together with appropriate AI/ML technologies, would be essential for an effective methane LDAR program. As companies look to reduce their Scope 1, 2, and 3 emissions, breaking down data silos and establishing data standardization with common data models becomes critical in systems integration to develop an optimal emissions-reduction strategy. A seamless flow of information will enable the generation of high-fidelity data sets, which can be used to lower the operational footprint and drive business effects with AI solutions. With cloud-enabled technologies, driven by the application of ML and deep learning, companies can combine speed of implementation with scalability to accelerate their energy-transition efforts. I would like to invite readers to review the selection of papers to get an idea of various applications in the upstream oil and gas space where ML methods have been used. The highlighted papers cover the use of transformer-based models to predict oil production, the use of data analytics to study parent/child well relationships in shale plays, and the use of convolutional neural networks in core analysis. Recommended additional reading at OnePetro: www.onepetro.org. SPE 207744 Accelerating Subsurface Data Processing and Interpretation With Cloud-Based Full Waveform Inversion Systems by Sirivan Chaleunxay, Amazon Web Services, et al. SPE 205443 Natural-Language Processing and Text-Mining Approaches in Production-Shortfalls Analytics: Methodology, Case Study, and Value in the North Sea by Edgar Bernier, Total Denmark, et al. URTEC 208367 Real-Time Applications for Geological Operations: Repeatable AI Use Cases by Alfio Malossi, Eni, et al.

Improving Teleoperator Efficiency Using Position–Rate Hybrid Controllers and Task Decomposition

In hazardous environments where direct human operation of machinery is not possible, such as in a nuclear power plant (NPP), teleoperation may be utilized to complete tasks safely. However, because teleoperation tasks are time consuming, complex, highly difficult, and need to be performed with incomplete information, they may increase the human operator’s cognitive load, which can affect the efficiency of the human operator as well as the completeness of the task. In this study, we propose a teleoperation system using a hybrid teleoperation controller that can increase operator efficiency for specific teleoperation tasks in complex sequences. First, we decomposed the task into a sequence of unit subtasks. For each subtask, the input space was allocated, and either position control or rate control by the hybrid controller was determined. Teleoperation experiments were conducted to verify the controller. To evaluate the efficiency improvement of the teleoperators, the completion time and NASA task-load index (NASA-TLX) were measured. Using the hybrid controller reduced the completion time and the NASA-TLX score by 17.23% and 34.02%, respectively, compared to the conventional position controller.

The challenge of healthcare big data to China’s commercial health insurance industry: evaluation and recommendations

BackgroundChina’s social medical insurance system faces challenges in financing, product coverage, patient health responsibility sharing and data security, which commercial health insurance companies can help address. Confronting accelerated population aging, the rapid increase of patients with chronic diseases and the maternal and child healthcare needs created by the three-child policy, the Chinese government has encouraged the development of commercial health insurance. But China's commercial health insurance companies face financial sustainability problems, limited product ranges and high operating costs. At the same time, the informatization level of China's healthcare industry, and the value of healthcare big data, is increasing. We analyze and describe the potential application of healthcare big data in the life cycle of China's commercial health insurance system and provide specific action plans for Chinese commercial health insurance companies; identify the challenges to commercial health insurers; and make recommendations for the application of big health data by commercial health insurers. Our recommendations inform healthcare policy makers on the development of commercial health insurance and the improvement of the healthcare financing system. We not only verify the value of healthcare big data, but also identify specific ways that healthcare big data plays in the development of commercial health insurance. Based on the research results, we recommend new policies for government and new uses of healthcare big data for commercial health insurance institutions. The benign development of commercial health insurance will improve the level of health services in China.MethodsBy interviewing health insurance managers (including actuaries, product managers, business executives, information technology medical workers, and commercial health insurance personnel) and by accessing research papers, industry reports, news reports and public information disclosure documents about commercial health insurance, we describe the impact of healthcare big data on the life cycle of commercial health insurance products and processes.ResultsWe identify the issues and challenges of commercial health insurers in the use of healthcare big data, and advance specific strategies to expand the use of healthcare big data. In the life cycle of commercial health insurance products, healthcare big data can improve premium income, control medical costs and increase operational efficiency. First, healthcare big data can increase premiums, products and services by attenuating moral hazard and adverse selection problems, where high quality clients over-pay and high-risk clients underpay for health insurance. Second, healthcare big data can reduce medical expenses compensation pay-outs by promoting the establishment of a management medical system. Finally, the use of healthcare big data improves operational efficiency by increasing payment speeds, identifying fraud and increasing claim verification processes through automating payments and reducing offline processes. We discuss the obstacles to obtain healthcare big data confronting commercial health insurance companies. The sharing and data mining of healthcare big data brings privacy risks to the insured and there are significant differences in data standards and quality of healthcare big data that limit the application of healthcare big data in commercial health insurance. We recommend that national, regional and local government departments coordinate policies to facilitate the cooperation between commercial health insurance companies and regional healthcare big data platforms. In terms of technology, we recommend the establishment of data sharing platforms and data exchange mechanism across institutions and regions according to nation-wide standards and specifications. Government management departments should establish healthcare big data standards and specification system, promote the construction of healthcare big data and ensure the integrity, authenticity and reliability of health data. We recommend data quality continuous improvement and management mechanisms that combine technology and management. Government regulation should oversee commercial health insurance institutions and establish data security management systems to monitor and supervise the privacy of personal data.ConclusionsHealthcare big data can play an important role in the development of China's commercial health insurance industry. Healthcare big data can increase commercial health insurers’ financial viability while providing improved, and cost-effective, products and services. By providing more and better information to insurers, healthcare big data attenuates the asymmetric information problem, addressing moral hazard and adverse selection problems. By combining hospital and medical organization management information systems with insurers’ data management, healthcare big data can help insurers set sustainable premiums, control medical costs and promote operational efficiency. At present, the informatization degree of China's healthcare industry remains limited. To improve the performances, products and services of commercial health insurers, we recommend government reforms in healthcare big data, such as expanding medical industry cooperation; further developing the processes of applying healthcare big data; augmenting data sharing; addressing privacy risks; setting data standards; and improving data quality.

A Study on the Optimal Flexible Job-Shop Scheduling with Sequence-Dependent Setup Time Based on a Hybrid Algorithm of Improved Quantum Cat Swarm Optimization

Multi-item and small-lot-size production modes lead to frequent setup, which involves significant setup times and has a substantial impact on productivity. In this study, we investigated the optimal flexible job-shop scheduling problem with a sequence-dependent setup time. We built a mathematical model with the optimal objective of minimization of the maximum completion time (makespan). Considering the process sequence, which is influenced by setup time, processing time, and machine load limitations, first, processing machinery is chosen based on machine load and processing time, and then processing tasks are scheduled based on setup time and processing time. An improved quantum cat swarm optimization (QCSO) algorithm is proposed to solve the problem, a quantum coding method is introduced, the quantum bit (Q-bit) and cat swarm algorithm (CSO) are combined, and the cats are iteratively updated by quantum rotation angle position; then, the dynamic mixture ratio (MR) value is selected according to the number of algorithm iterations. The use of this method expands our understanding of space and increases operation efficiency and speed. Finally, the improved QCSO algorithm and parallel genetic algorithm (PGA) are compared through simulation experiments. The results show that the improved QCSO algorithm has better results, and the robustness of the algorithm is improved.

Value of Mineralogical Monitoring for the Mining and Minerals Industry

The shift towards lower grade ore deposits, sustainable energy, CO2 reduction, volatile market conditions and digitalization has pushed the mining and minerals industry towards predictive, sustainable and agile analytical solutions to improve safety and increase operational efficiency [...]

구글 티처블 머신을 활용한 군사장애물 분류 모델 설계 및 실증 연구

With the recent development of Obstacle Clearance Tank (K-600) that can overcome minefield, rockfall and road crator, ROK Army can shorten the time required to overcome obstacles and increase operation efficiency. However, in order to overcome the lack of military service resources in the future and be guaranteed to survive operator, Unmanned Obstacle Clearance Tank should be introduced along with artificial intelligence technologies. In order to develop the Unmanned Obstacle Clearance Tank, the initial recognition stage is critical among “recognitioncontrol-action” stages. This study aims to build the obstacle recognition and classification model based on Google teachable machine and verify the model using the real RC-car camera test environment.

Determinants of Credit Risk in Ethiopian Banking Industry: Does Political Stability Matter?

The continued political instability in Ethiopia’s Tigray Region has made it tough for bankers to recover loan repayments, triggering banks’ nonperforming assets to rise. Beyond the bank industry-specific and macroeconomic determinants, the article aims to understand the relationship between political stability and credit risk. The article focuses on 16 Ethiopian banks to understand the mentioned relationship in developing countries. From 2010 to 2020, data are obtained from the World Governance Indicators, World Development Indicators database and Ethiopia’s National Bank. Based on a two-step dynamic panel estimator, the results suggest that political stability is negatively correlated with credit risk, while political instability does have a positive impact on bank credit risk. Credit risk was inversely related to increased operating efficiency, diversified earnings and national currency appreciation. In contrast, greater bank size, rising inflation and economic expansion all contribute to a high level of credit risk. The loan-to-asset ratio and profitability were shown to have a minor influence on nonperforming loans (NPLs). Thus, this study provides a better understanding of the effects of political stability on the factors that determine levels of NPLs in developing economies.

M202 Increase operation efficiency by setting beta-hydroxybutyrate in automatic chemistry analyzer

M202 Increase operation efficiency by setting beta-hydroxybutyrate in automatic chemistry analyzer

Design of Airborne Hyperspectral Automatic Filling Device for Liquid Nitrogen Refrigeration

To maintain sensitivity of the sensor, especially the infrared band sensor, liquid nitrogen is needed for cooling during the imaging process of airborne hyperspectrometer. However, traditional filling process requires a lot of manual intervention, which is not only time-consuming and labor-intensive, but also creates great potential safe hazard during aviation operations. In view of this, this paper designs an airborne hyperspectral automatic filling device for liquid nitrogen using time-delay electromagnetic switch, pressure sensing detector and electromechanical device, which achieves the purpose of automatic liquid nitrogen filling, increases operation efficiency of airborne hyperspectral equipment, reduces air stage operation steps, and guarantees personal safety of air operators, thus playing a good demonstration role in further standardizing and optimizing the technical process of air-borne hyperspectral remote sensing data.

Effects of service robots in the hospitality and tourism sectors : A critical review and future research directions

Service robots provide hospitality and tourism organizations significant technological abilities to reduce costs and increase operational efficiency by replacing human labor. This study aims to comprehensively review the recent empirical literature on the effects or outcomes of service robots in leading hospitality and tourism journals, ascertain the critical research gaps and limitations, and propose new research directions. The literature review of the positive effects of service robots showed that they can enhance customer satisfaction and loyalty, engagement, experiences, and perceived stability of service, mainly in hotels. By contrast, the literature review of the negative effects of service robots indicated that consumers are concerned about the dehumanization of services, financial risks, and lower service efficiency, mainly in the case of restaurants and food service providers. As there are critical limitations in the existing literature, this study showed that all the related studies focus only on customers and the market aspect of businesses. Moreover, studies relied heavily on external sources of customer data and had limited sectoral scope since they mainly focused on hotels and restaurants. To fill in the research gaps, this study highlighted the need for future research on the employee and organizational aspects of businesses. It urges researchers to investigate the employee outcomes and collaborations with service robots and to address the critical issues of financial investment results, technology management, and the role of service robots during the COVID-19 pandemic.

Novelties and Benefits of Fintech in the Financial Industry

New technologies and innovations that are emerging and developing today through the financial industry can be called by one name and that is “FinTech”. The main feature of “FinTech” is "digital proximity", where the relationship between customers and banks changes from "physical proximity" to "digital proximity". Recognition of new digital technologies available to the financial industry and ultimately their implementation can lead to an increase of operational efficiency and business excellence. The objective of this work was (1) to study and explore the importance of digitalization and digital transformation and its impact on FinTech (2) to analyze and explore Fintech's innovation and novelty areas in the financial sector, (3) to ensure a review of the research literature in these field. However, there is still a lack of research that correlates fintech and the financial industry, which is evident from the review of the presented researched literature.

RELATIONSHIP BETWEEN OIL AND RENEWABLE ENERGIES PRICES

Large companies in the oil sector have been expanding their activities to the renewable energy segment. The objective of this work is to identify patterns and relationships - in the 10 last years - between oil price and prices of the following renewable energies: wind, hydroelectric, solar, and bioenergy. The analysis of standards and the identification of relationships help decision-makers to define strategies and increase operation efficiency. Besides, it contributes to the development of models capable of predicting, interpreting, and testing hypotheses about the data. The main analytical and data treatment methodology used in this work comprises statistical tests involving Cross-Correlation and Granger Causality. The Cross-Correlation Test showed a strong negative correlation between oil and wind energy prices, and the Granger’s test did not show causality between them. New studies comparing oil and renewable energy prices by country and covering a longer period can bring interesting results.