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Interconnected Intelligence: Navigating Through Power Quality Checking and Control Using Smart Intelligence-Based Methods
Globally, power quality issues incur substantial costs. In the United States, power quality problems contribute to a $150 billion annual cost, covering lost productivity, equipment damage, and safety hazards. Smart intelligence-based methods can potentially cut these costs by up to 50%. In India, power quality disturbances result in a $10 billion annual cost involving equipment damage, productivity losses, and customer dissatisfaction. The adoption of smart intelligence-based power quality methods in India is projected to grow annually by 25% for the next 5years due to increasing grid demands. In todays intricate power landscape, dependable electrical systems are crucial. Power quality disturbances, including voltage variations, harmonics, and flicker, can disrupt sensitive equipment, resulting in financial losses and safety risks. Addressing these challenges, smart intelligence-based methods emerge as promising solutions. This chapter systematically explores the application of artificial intelligence, machine learning, and data analytics for elevated power quality monitoring, assessment, and regulation. Such intelligent approaches optimise power system performance, reduce downtimes, and ensure a consistent supply of high-quality electrical energy. The assimilation of smart intelligence-based methods emerges as a promising avenue to address these challenges effectively. Harnessing the capabilities of these intelligent paradigms empower power systems to attain optimal performance, curtail downtimes, and ensure a steadfast provision of high-grade electrical energy. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Empowering Solar Power Generation: The Z-Source Inverter Approach
The Z-source inverter model is a revolutionary design obtainable in this study for solar power conversion systems that do away with the traditional intermediary DC/DC converter. The competitive pricing of renewable energy bases in the market has drawn a percentage of attention in recent times. Government funding and technological advancements are to blame. Astral photovoltaic system is a created green technology that requires no upkeep, requires less time to install, and has grid parity. Systems for solar PV supply are categorized based on the phases of conversion. In order to maximize the amount of power created by solar energy, the conventional boost converter is utilized as an intermediary power conversion circuit. Voltage source inverters, or VSIs, are frequently employed to provide a controlled AC voltage at the output. However, the inability of VSIs to control current properly results in overcurrent problems during fault conditions. The size, weight, and switching losses of the filter circuit are condensed by the suggested converter. To solve the aforementioned issues, a Z-source inverter (ZSI) is replaced as an alternative of the voltage source inverter (VSI) in variable speed drive systems. One type of single-stage buck-boost inverter is the Z-source inverter. It functions similarly to a conventional VSI in buck mode, with six active vectors, and adds an additional switching state in boost mode, known as the shoot-through state, through utilizing a resistance Z-network. The resistivity network is regarded as an appealing solution for a number of applications since it raises the DC link current to the necessary level. In comparison to the traditional two-stage influence adaptation, the developed Z-source inverter extracts greater power from photovoltaic arrays. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Power Consumption Forecasting with AI and IOT
Electricity plays a fundamental and indispensable role in modern society, driving progress, development, and the overall quality of life. Electricity is profoundly ingrained in daily life. It powers homes, providing lighting, heating, cooling, and appliances that support, comfort, and convenience. From cooking meals to powering electronic devices and entertainment systems, electricity is vital for modern living, enhancing our quality of life and enabling various activities. Power forecasting is critical to the effective management and optimization of power generation, consumption, and distribution. Power consumption forecasting has evolved significantly with the introduction of advanced technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT). AI techniques, such as machine learning and deep learning, make use of the massive amounts of data produced by IoT devices like smart meters and energy monitoring devices. These devices continuously gather real-time data on power consumption, weather conditions, grid performance, and other relevant factors. AI algorithms can find patterns and correlations and provide accurate forecasts and important insights for power forecasting by processing and analyzing data. Machine learning algorithms, such as regression models, neural networks, and ensemble approaches, are trained using historical power consumption data and the features that have been chosen. The models discover the underlying patterns and correlations between input features and power consumption. These forecasts can be used for short-term load balancing, energy procurement planning, demand response management, and optimizing energy distribution. AI and IoT power usage projections give valuable data for decision-making and energy optimization techniques. These projections can be used by energy suppliers, grid operators, building managers, and consumers to plan energy usage, distribute resources efficiently, optimize demand response programs, and discover possibilities for energy saving. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Smart and Secured Ways of E-Payment: Design of New Frameworks
The rapid development in technology and the rapid growth of e-commerce have paved the way for changes in the method of payment. It also gave rise to various hazards while making the e-payment when compared to the normal or standard payment methods it is necessary to make a secured payment. Various initiatives are taken by the government for providing a secure payment system which will be more useful for all the commercial activities done through online. Already various e-payment systems are used by the consumers for paying the amount for the materials purchased. The increasing need of foreign exchange with an effective and efficient electronic payment system is required for making the low value payment. The framework that is been used in the global market and also in virtual marketplace require a complete legal structure which should also have impact on the economy of the mediaeval trade. Rapid development of e-commerce during the recent years has made more changes in the financial and non-financial transactions. In e-commerce, the payment gateway plays an important role in the exchange in ensuring that the transactions occur without any disputes and also maintains the security of the system. Most of the payment gateways used in the e-commerce are provided by rusted third party who will provide monetary information. Due to the increased use of e-commerce and online payment system, there is also any increase in security breaches during the past few years. So, it is necessary to build a new framework that will provide a secured platform for the e-payment system through which the consumers can directly connect to their merchants securely. Most of the third-party providers are also asking for the identity of the customers while making the payment which might even have change of loss of person information of the customer. The new framework should contain an improved security and the data collected should be confident, proper authentication method should be used, and availability of the data and integrity of the data should be maintained. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Effective Models for Computing Optimized Storage Systems for Energy
This chapter investigates effective modeling techniques for designing optimized storage systems that minimize energy consumption. We explore various models capturing the interplay between storage performance, capacity, and energy efficiency, focusing on computational methods to enhance effectiveness. As the demand for renewable energy sources continues to increase, the need for reliable and efficient storage solutions becomes increasingly crucial. We discuss the design and implementation of optimized storage systems for energy, highlighting computational models role in improving efficiency. Starting with an overview of the energy storage system, we examine different modeling approaches such as mathematical optimization, machine learning, and simulation techniques. Each approach offers a unique approach to addressing the complexities of energy storage. Additionally, we discuss optimization models, ensuring that energy storage solutions are both technically efficient and economically viable. In summary, this section emphasizes the importance of computational modeling in developing efficient energy storage systems, which are crucial for meeting energy integration demands and ensuring stability and sustainability. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Model Selection Strategies for Identifying Effective Energy Storage Systems
Energy is present in various forms in and around us. Capturing and storing energy from various sources have diverse challenges. Designing and developing energy storage systems are challenging, as various techniques are used to distribute energy from sources and to store for diverse use cases. Identifying the optimal and effective energy storage system requires the application of various model selection strategies. The success and adoption of effective energy storage systems can be identified with numerous factors, which include the systems efficiency, reliability, cost-effectiveness, and scalability. Various model selection strategies are available to compute and determine the effective energy storage mechanisms. Various researchers are planning and designing energy storage systems based on the insights from the data with the support of optimisation algorithms, mathematical models, and Artificial Intelligence (AI) and Machine Learning (ML) technologies. The chapter discusses the various model selection strategies for identifying effective models for energy storage systems. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Model Selection Strategies for Identifying Effective Energy Storage Systems
Energy is present in various forms in and around us. Capturing and storing energy from various sources have diverse challenges. Designing and developing energy storage systems are challenging, as various techniques are used to distribute energy from sources and to store for diverse use cases. Identifying the optimal and effective energy storage system requires the application of various model selection strategies. The success and adoption of effective energy storage systems can be identified with numerous factors, which include the systems efficiency, reliability, cost-effectiveness, and scalability. Various model selection strategies are available to compute and determine the effective energy storage mechanisms. Various researchers are planning and designing energy storage systems based on the insights from the data with the support of optimisation algorithms, mathematical models, and Artificial Intelligence (AI) and Machine Learning (ML) technologies. The chapter discusses the various model selection strategies for identifying effective models for energy storage systems. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Smart Intelligence Aided Power and Energy Management
The Artificial Intelligence (AI) has become a revolutionary technology in power and energy management, providing exceptional prospects for improving efficiency, reliability, and sustainability. This study delves into the incorporation of AI methodologies into smart intelligence-driven systems for power and energy management. It delves into how AI algorithms, encompassing machine learning and optimization approaches, are utilized to enhance energy generation, distribution, and consumption across a range of environments, including smart grids, microgrids, and intelligent buildings. The abstract examines the primary challenges and factors to consider when implementing AI-driven solutions for power and energy management, which encompass issues such as data quality, privacy, security, and scalability. It emphasizes the crucial role of transparency and interpretability in AI algorithms to cultivate trust among stakeholders and secure user acceptance. Additionally, it addresses the importance of upholding ethical standards and regulatory requirements to address societal apprehensions and mitigate potential risks linked to the deployment of AI in energy systems. Moreover, the abstract highlights AIs contribution to advancing energy efficiency and sustainability through dynamic demand response, incorporating renewable resources, and the optimization of grid operations. It underscores the importance of on-going monitoring and evaluation of AI-driven energy management systems to pinpoint areas for enhancement and mitigate unintended repercussions. In summary, this paper offers perspectives on AIs potential to transform power and energy management methodologies, leading to more intelligent, robust, and eco-friendly energy systems. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Smart Intelligence Perspective and Integrating AI with the Power System
The confluence of Artificial Intelligence (AI) and energy frameworks has become a focal point in contemporary research, driven by the imperative to innovate and advance our energy systems. The book chapter delves into the nuanced relationship between AI and these systems, highlighting its opportunities for improved performance, resilience, and environmental conservation. Central to this exploration is the elucidation of AIs transformative potential in energy dynamics, spanning areas such as machine learning, deep learning, neural architectures, and foresight modelling. The chapter portrays diverse AI-driven applications, emphasising their transformative capabilities in steering energy strategies. Insights into the multifaceted advantages of integrating AI into energy frameworks are presented, stressing augmented stability, amplified efficiency, fiscal savings, and forward-thinking outage resolutions. Based on real-world examples, the research highlights the value of integrating AI into energy strategies. It also identifies new paradigms in the evolving energy landscape that will shape our energy future. This underscores AIs crucial role in driving energy transformations and calls for collaboration among academia, decision-makers, and industry leaders to create a greener, more efficient energy path. The importance of data analytics in managing power system data, enabling insights into consumption trends, and assisting in making educated decisions is also covered in this chapter. In order to ensure a responsible and secure implementation, the ethical and privacy issues associated with AI deployment in the power sector are also addressed. Furthermore, the chapter elaborates on the prospective trajectory of AI within Power Systems, elucidating its involvement in quantum computing, edge computing, and the incorporation of IoT to facilitate Microgrid Management. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Empowering a Sustainable Future: Blockchains Role in Economy, Organizations, and Finance Towards Smart Circular Economy
In the present era, blockchain is considered as driving force reshaping the global economy. It is redefining the organizational structure and the business activities and revolutionizing the financial market. Blocking-based technologies also have immense potential to address climate change, concerns, and resource constraints. This chapter provides insights into the multifaceted role of blockchain in driving sustainable development goals by fostering transparency, improving accountability, and integrating sustainability across organizational and financial domains. It analyses the blockchain potential to strengthen financial sustainability through decentralized finance, an equitable financial system. Further, it also highlights the instrumental role of blockchain by improving the supply chain, transparency, corporate governance, and incorporation of environmental, social, and governance (ESG) principles into the business model. Moreover, this chapter discusses the alignment of blockchain with the United Nations sustainable development goals, highlighting its potential to address, pressing issues such as resource efficiency, climate, change, and inequality. It critically assesses the various challenges in the adoption of blockchain and underscores its importance in achieving long-term sustainability goals. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Biofortification and Sustainable Intensification of Soil: Perspectives on Rice Cultivation in India
Biofortified crops have inherently been known to acquire climate-smart traits and stress resilience. Climate-smart agriculture integrates climate change into the planning and implementation of sustainable agricultural strategies. Biofortification is a climate-smart concept that enhances crop nutrient quality and quantity through conventional breeding, agronomic practices, or genetic engineering. It will enrich food availability, stability, accessibility, and utilization and positively impact the health, livelihood, production, and distribution of food crops. The system of rice (Oryza sativa L.) intensification involves a set of agronomic principles to improve the structure and functioning of the soil system by fortifying it with organic matter and micronutrients. With the exceeding urbanization and population explosion, food security is a primary concern for policymakers all around the globe. Widespread zinc, iodine, iron, and selenium micronutrient malnutrition is a significant cause of numerous health problems in human populations where rice is part of the staple diet. Climate-smart biofortification is a durable and effective option to reach the vast numbers of malnourished populations scattered across the world sustainably. Approaches have been strategized worldwide under rice biofortification research projects for maintaining, increasing, and introducing new micronutrients in rice grain. Biofortification has been safely implemented as an environmentally friendly approach to produce higher yields at low costs without undesirable soil effects. Prospective advancements can be achieved by integrating mineral and organic fertilizers with superior germplasm, promoting improved nutrient uptake and localization in the consumed parts of the crop. 2025 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Revolutionizing Road Transportation: The Role of Artificial Intelligence in Smart and Efficient Systems
This chapter is about how AI is transforming road transportation by improving efficiency, safety, and comfort. By emphasising the importance of AI integration and moving on to main AI technologies such as machine learning (ML) for applications like traffic prediction and autonomous driving. Deep learning techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are essential for vehicle detection and traffic analysis. Also, natural language processing (NLP) enhances traffic planning and customer support by providing real-time information and virtual assistants. We can also know from the chapter that AI applications like self-driving cars that use AI for vision and control, intelligent traffic management systems that optimise signal timings, and predictive maintenance to avoid vehicle problems. It also discusses data privacy, technology, and ethics questions, as well as demonstrates effective real-world AI deployments and future trends. Overall, the chapter emphasises AIs disruptive impact on road transport and its potential for ongoing innovation and improvement in the pursuit of a smarter, more efficient transportation network. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Smart Traffic Systems: Revolutionizing Road Transport with AI and Image Processing
In the past few years, Big data, machine learning, and artificial intelligence (AI) inclusion have changed the dynamics in various industries, including road transport. This research explores machine learning and artificial intelligence in road transportation, accentuating the advanced image processing methods. We apply these technologies to enhance how traffic is managed, how safe vehicles are operated, and how the most efficient routes are planned. This study also expands to various techniques and methods for analysis, such as image processing, object recognition, and recognition systems, and is effective in implementation. Through extensive experiments and several case studies, we have shown substantial improvements in accuracy and efficiency achieved when image processing in road transport, including machine learning and artificial intelligence. This study points out the tremendous prospects posed by these technologies in shaping the future of transport and suggests further developments and applications. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Pathways for Sustainable Food Security: Status, Challenges, and Opportunities in Developing World
Food security is a significant concern in developing countries because of the dense population of disadvantaged individuals, insufficient food resources, and restricted availability of retail options. Food availability, quantity, and consumption are significantly influenced by social conditions. This study aims to analyze the present circumstances, challenges, and potential prospects in order to suggest pathways towards achieving sustainable food security in developing countries. The study utilizes a comprehensive approach to identify the vital societal elements that impact food security, through the analysis of pertinent literature and case studies from multiple nations. The variables encompass affluence, gender, education, social capital, and culture. This study investigates the causal relationship between these variables and the occurrence of food insecurity. It proposes strategies that foster sustainable agriculture, social protection, community empowerment, and gender equality as potential remedies. The report asserts that addressing the socioeconomic determinants impacting food security is crucial for emerging nations to attain sustainable food security. Attaining sustainable food security necessitates a comprehensive approach that acknowledges the significance of social factors and focuses on the holistic well-being of individuals. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Factors Influencing Purchase Intention of Organic Coffee
This study looks at the variables affecting Indian customers propensity to buy organic coffee. Drawing on the Theory of Planned Behavior (TPB) and considering the unique cultural context of India, the study integrates a multi-dimensional framework, incorporating personality, attitude, lifestyle, subjective norm, collectivism, perceived behavioral control, perceived consumer effectiveness, and environmental concerns, to analyze the dynamics of organic coffee purchase intention. Data was gathered from a representative sample size of 420 Indian coffee customers making use of a formal questionnaire and analyzed using statistical tools such as SPSS and Amos. Our findings show a favorable relationship between purchase intention with attitude, collectivism, and environmental concern. This study has implications for both research and practice. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
The Future of Banking: Leveraging AI for Business Transformation
Artificial intelligence (AI) has moved the banking sector into a new era, offering unmatched channels to improve efficiency, risk management, and customer experiences. The research delved into the role of AI in banking, particularly the applications, challenges, and implications. Descriptive research design is used to describe the acceptance rate of banking customers toward leveraging AI in Banking transactions through theTechnology Acceptance Model (TAM) to prove the hypothesis thatAttitude Towards Using AI mediates the individual effects of Perceived Usefulness, Perceived Ease of Use, and Perceived Risk with Behavioural Intention. The primary data was collected from 385 banking customers using the purposive sampling technique by circulating astructured questionnaire. This research shows that AI brings about more benefits than challenges, but it is crucial to handle the latter carefully to enable sustainable sectoral growth. The empirical results support theexistence of a promising environment for AI adoption in thebanking industry as there the customers show a positive behavioural intention. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
An Integrated Model for Team Dynamics for Enhanced Collaboration and Performance
The CTF model introduces a new approach to team organization, inspired by the atomic structure of carbon. This interdisciplinary model combines organizational psychology, team dynamics research, and chemistry principles. The paper evaluates current team models, such as Belbins team roles, Hackmans model of team effectiveness, and the GRPI model, emphasizing their strengths and weaknesses. CTF expands on these frameworks while addressing their limitations. It consists of a productivity core (similar to protons and neutrons) and critique networks (similar to electrons), connected by Team Cohesion Factors. Unlike previous models that focus on specific aspects, CTF provides a comprehensive structure that integrates leadership, execution, and feedback mechanisms. It balances a hierarchical structure with collaborative input, including internal and external feedback systems. Inspired by the adaptability of carbon, the model is suitable for dynamic environments. Although it shows promise in addressing the limitations of previous models, CTF needs empirical validation. This paper lays out the theoretical basis of CTF, compares it with existing frameworks, explores its potential benefits and limitations, and outlines future research directions. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Powering the Future: The Role of Solar Energy in Indian Energy Transition
Energy has become one of the basic human needs, and the expanding population demands more energy for day-to-day needs. As the demand for energy increases, the easy solution for everyone to rely on is the employment of fuel-powered generation systems, which adversely affect the ecology and the environment. In order to address the energy needs of the time without harming the environment, we need considerable investments in the renewable energy sector. Government alone cannot perform this task. A collective effort from government, public, and private investors is required here. Energy conferences like Conferences of Parties (COP) focus on the transition of energy from non-renewable sources to renewable sources and on bringing down the loss of energy during transmission and distribution. This paper current Indian energy sector scenario, suggests solar energy as a solution to Indias energy crisis and discusses the reason behind the lack of motivation for people to invest in solar energy. Addressing these factors can attract more investors to the investment. The research in battery technology and solar panels can help the Indian energy sector focus on energy harvesting and the development of energy-independent systems which will solve the energy crisis to an extent. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Impact of Environmental Studies on Sustainable Consumption Among the Students: A Study from Indian Higher Education Institutions
This research investigates the impact of compulsory university courses focusing on sustainability on students attitudes and behaviors towards responsible consumption. The study seeks to determine how education impacts sustainable decision-making by analyzing students consumption habits both before and after they enroll in these courses. The study based on descriptive nature, as per convenience sampling method the required data are collected from 220 students enrolled under graduate programme offered by colleges affiliated to Bangalore University. The structure questionnaire constructed with six EALEnvironmental Awareness Level; PHWBPersonal health and wellbeing; SR-Social responsibility; PVIPersonal value and identity; ICInnovation and creativity; EB-Economic Benefits and these dimension analyzed with help of correlation matrix. Based on the analysis of six dimensions of green consumption the findings indicate that the courses significantly influenced students inclination towards sustainable consumption, suggesting the potential for educational initiatives to drive positive change. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Assessing Financial Distress to Foster Sustainable Growth: Insights from Indian Listed Companies
A companys success and survival depend on its financial viability, which is primarily determined by its strong key financial ratios. When these ratios fall below the critical levels, the companys long-term survival is threatened indicating the onset of financial distress (FD). In this context the FD prediction models can act as an early warning system, enabling corporate managers to intervene mid-way and take corrective action. This study focuses on Indian-listed companies that filed under the Insolvency and Bankruptcy Code (IBC) 2016, between the years 2020 and 2023. Out of the 200 listed companies that applied for bankruptcy, 40 companies were selected for the study that showed either the status as Resolution plan or withdrawal or Rejected as per the National Company Law Tribunal (NCLT) order. To analyze the long-term survival prospects of these companies, the study employs Altmans Z-score modelsthe initial Z-score model for publicly traded companies and the EMZ score that is modeled for emerging markets like India. Both the models were calculated along with Z-score probabilities to gauge the likelihood of financial distress. Additionally, the Beneish M-Score was calculated to validate the financial data. The results from the Z-score models indicate that several companies showed declining financial stability during the study period with some on the verge of bankruptcy, while the M-score revealed potential financial manipulation in certain cases. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.