Repositorio UFT :: Examinando por Autor "Rao, Challa Krishna"

Examinando por Autor "Rao, Challa Krishna"

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A comprehensive review of smart energy management systems for photovoltaic power generation utilizing the internet of things
(KeAi chinese roots global impact, 2025-04-25) Rao, Challa Krishna; Sahoo, Sarat Kumar; Yanine, Fernando
Renewable energy represents the most reliable and widely recognized solution for meeting the escalating global energy demands. The optimization of solar energy generation necessitates a strong focus on predictive maintenance and advanced deployment methodologies. To enhance solar power utilization, Internet of Things enabled solar monitoring systems have been proposed for real-time data acquisition and analytics, facilitating performance forecasting and ensuring consistent power output. A critical challenge in demand-side energy management lies in optimizing the integration of renewable resources while maintaining cost efficiency and minimizing energy losses. Therefore, strategic planning for the integration of renewable energy sources is imperative. Intelligent energy management systems play a pivotal role in optimizing energy distribution, particularly in scenarios with high grid dependency. Cloud computing infrastructures address the complexities and scalability challenges posed by expanding smart grids, enabling real-time data processing and adaptive energy control mechanisms. This study explores the practical implementation of energy management system in industrial settings and research domains, both of which serve as key stakeholders in advancing smart energy solutions. A comprehensive review of internet of things applications in photovoltaic power generation highlights key research objectives and technological developments in the field. The evolving landscape of internet of things driven innovations presents numerous research opportunities, including the formulation of performance evaluation metrics and the development of novel optimization techniques. Additionally, the growing emphasis on energy management within intelligent architectural frameworks underscores the necessity for deeper investigations into adaptive control strategies and system interoperability. This ongoing research is essential for driving advancements in internet of things enabled energy solutions and enhancing the efficiency of smart grid ecosystems.
A review of IoT-based smart energy solutions for photovoltaic systems
(Springer Nature, 2025-08-11) Rao, Challa Krishna; Sahoo, Sarat Kumar; Yanine, Fernando
Harnessing renewable energy stands out as the most reliable and widely accepted method to address the surging global energy needs. In particular, advancing solar energy systems requires focused efforts in operational upkeep and practical deployment. To optimize solar output, Internet of Things enabled monitoring frameworks have been introduced, enabling data collection and analysis for performance evaluation and consistent energy delivery. A core obstacle in managing energy from the consumer side lies in leveraging green power sources efficiently while keeping expenses in check and avoiding excessive energy usage. As a result, thoughtful planning is essential when integrating alternative energy technologies. Smart energy systems critically optimize consumption amid growing grid reliance. Cloud computing resolves challenges and unlocks opportunities in modern power networks. This work examines energy coordination tools' dual role in industrial operations and academic research, demonstrating their synergistic value in advancing energy efficiency and grid resilience through technological and theoretical innovation. The investigation covers comprehensive evaluations of IoT’s role in solar power generation. Emerging IoT developments open new pathways for scholarly exploration, including the formulation of evaluation standards and the pursuit of novel improvement strategies. Furthermore, deeper investigations into intelligent energy systems within smart infrastructures are increasingly necessary. Such efforts are critical for enriching the knowledge base of IoT-driven solutions and promoting steady technological progress. Articulo WoS y SCOPUS Q2 El aprovechamiento de las energías renovables destaca como el método más fiable y ampliamente aceptado para hacer frente al creciente demanda energética mundial. En particular, el avance de los sistemas de energía solar requiere esfuerzos específicos en el mantenimiento operativo y la implementación práctica. Para optimizar la producción solar, se han introducido marcos de supervisión habilitados para el Internet de las cosas, que permiten la recopilación y el análisis de datos para la evaluación del rendimiento y el suministro constante de energía. Uno de los principales obstáculos en la gestión de la energía desde el lado del consumidor radica en aprovechar las fuentes de energía verde de manera eficiente, al tiempo que se controlan los gastos y se evita el uso excesivo de energía. Por lo tanto, es esencial una planificación cuidadosa a la hora de integrar tecnologías de energía alternativa. Los sistemas energéticos inteligentes optimizan de manera crítica el consumo en medio de una creciente dependencia de la red eléctrica. La computación en la nube resuelve los retos y abre nuevas oportunidades en las redes eléctricas modernas. Este trabajo examina la doble función de las herramientas de coordinación energética en las operaciones industriales y la investigación académica, demostrando su valor sinérgico en la promoción de la eficiencia energética y la resiliencia de la red eléctrica a través de la innovación tecnológica y teórica. La investigación abarca evaluaciones exhaustivas del papel del IoT en la generación de energía solar. Los nuevos avances en el IoT abren nuevas vías para la exploración académica, incluida la formulación de normas de evaluación y la búsqueda de nuevas estrategias de mejora. Además, cada vez es más necesario profundizar en la investigación de los sistemas energéticos inteligentes dentro de las infraestructuras inteligentes. Estos esfuerzos son fundamentales para enriquecer la base de conocimientos de las soluciones impulsadas por el IoT y promover un progreso tecnológico constante.
A systematic review of recent developments in IoT-based demand side management for PV power generation
(De Gruyter, 2024-06-21) Rao, Challa Krishna; Sahoo, Sarat Kumar; Yanine, Fernando
Demand-side management (DSM) with Internet of Things (IoT) integration has become a vital path for optimizing photovoltaic (PV) power generating systems. This systematic review synthesizes and evaluates the latest advancements in IoT-based DSM strategies applied to PV power generation. The review encompasses a comprehensive analysis of recent literature, focusing on the key elements of IoT implementation, data analytics, communication protocols, and control strategies in relation to solar energy DSM. The combined results show how IoT-driven solutions are changing and how they might improve PV power systems’ sustainability, dependability, and efficiency. The review also identifies gaps in current research and proposes potential avenues for future investigations, thereby contributing to the ongoing discourse on leveraging smart DSM in the solar energy domain using IoT technology.
An IoT-based intelligent smart energy monitoring system for solar PV power generation
(De Gruyter Mouton, 2023-09-15) Rao, Challa Krishna; Sahoo, Sarat Kumar; Yanine, Fernando
As the world’s attention turns to cleaner, more dependable, and sustainable resources, the renewable energy sector is rising quickly. The decline in world energy use and climate change are the two most significant factors nowadays. PV forecasting was essential to enhancing the efficiency of the real-time control system and preventing any undesirable effects. The smart energy management systems of distributed energy resources, the forecasting model of irradiation received from the sun, and therefore PV energy production might mitigate the impact of uncertainty on PV energy generation, improve system dependability, and increase the incursion level of solar power generation. Smart sensors and Internet of Things technologies are essential for monitoring and controlling applications in a broad range of fields. As a result, solar power generation forecasting was essential for microgrid stability and security, as well as solar photovoltaic integration in a strategic approach. This paper examines how to use IoT, a solar photovoltaic system being monitored, and shows the proposed monitoring system is a potentially viable option for smart remote and in-person monitoring of a solar PV system.
Chapter 12: Demand side energy management algorithms integrated with the IoT framework in the PV smart grid system
(Academic Press, 2024-01-01) Rao, Challa Krishna; Sahoo, Sarat Kumar; Yanine, Fernando
The smart grid revolution in the electric power sector will play a major role in the future. In the electric power system, the combination of new technology and communication infrastructure makes the grid smarter. To incorporate intelligence into the grid, many technological challenges must be solved, including those posed by energy storage systems, the integration of renewable sources, communication, protection, control, and demand-side management with customer involvement. Considering the rising need for electricity, one of the primary operational difficulties in the power system is balancing power generation to the constantly shifting load. Under the smart grid, utilities have realized that through demand side management and different demand response (DR) efforts, customer participation may be efficiently used for this balancing mechanism. The two-way communication between supply and demand can be successfully implemented with the help of smart grid intervention. Customers participate in DR schemes by actively reducing or shifting loads from peak to nonpeak hours concerning the pricing scheme. Therefore, it is essential to develop new demand response strategies for the smart grid, taking into consideration all features of the utility provider and the customers.
Design and deployment of a novel decisive algorithm to enable real-time optimal load scheduling within an intelligent smart energy management system based on IoT
(Elsevier, 2024-12-01) Rao, Challa Krishna; Sahoo, Sarat Kumar; Yanine, Fernando
Consumers routinely use electrical devices, leading to a disparity between consumer demand and the supply side a significant concern for the energy sector. Implementing demand-side energy management can enhance energy efficiency and mitigate substantial supply-side shortages. Current energy management practices focus on reducing power consumption during peak hours, enabling a decrease in overall electricity costs without sacrificing usage. To tackle the mentioned challenges and maintain system equilibrium, it is essential to develop a flexible and portable system. Introducing an intelligent energy management system could pre-empt power outages by implementing controlled partial load shedding based on consumer preferences. During a demand response event, the system adapts by imposing a maximum demand limit, considering various scenarios and adjusting appliance priorities. Experimental work, incorporating user comfort levels, sensor data, and usage times, is conducted using Smart Energy Management Systems (SEMS) integrated with cost-optimization algorithms.
Design of smart socket for monitoring of IoT-based intelligent smart energy management system
(Springer, 2021) Rao, Challa Krishna; Kumar Sahoo, Sarat; Balamurugan, M.; Yanine, Fernando
Smart socket is designed for collecting and sending the data from the various nodes in one field to other fields. Smart socket consists of the Arduino_Uno, XBee, sensors, gateway, computer, USB, and IDE. This works emphasis on design and development of smart socket with wireless capability, this can be used to collect the data from each electrical device by using sensors. An XBee transmitter and receiver node are used for data communication in wireless networks. Real-time data gathered at the central node can be used to prioritize and schedule the appliances. Then, the system analyzes the data to generate control commands to turn the devices attached to the smart socket on or off. This paper presents the operation and functions of smart socket in different sensor network topologies. The results show that the proposed smart socket can correctly read the data from the various nodes and also send it to different nodes of different parameters.
Designing an intelligent smart energy monitoring system for optimizing the utilization of PV energy
(Springer Nature, 2024-11-01) Rao, Challa Krishna; Sahoo, Sarat Kumar; Yanine, Fernando
Consumers in both residential and commercial settings are increasingly interested in reducing their energy consumption, influenced by feed-in tariffs for renewable resources and the recent surge in electricity rates. This study introduces a central control system and a smart power plug utilizing the XBee communication protocol to effectively manage energy usage. Smart energy management systems are employed to measure and optimize power consumption at the consumer premises level. The primary objective of this paper is the design and development of wireless smart plugs capable of assessing various power characteristics and collecting real-time data on individual consumer appliances' power usage. The SEMS setup establishes a Consumer Area Network through an XBee transmitter and receiver node, enabling real-time data collection at the central node for scheduling and prioritizing appliances. Utilizing the SEMS setup, consumer appliance datasets are generated and additional datasets aid in load disaggregation. The system configuration enables wireless data transfer from smart outlets to a central controller. Control instructions derived from data analysis are then used by the system to turn connected devices to the smart plug on or off. Test results indicate that the proposed smart plug accurately assesses power consumption up to eighteen meters away without compromising data integrity. The central controller, guided by a planned user program code, effectively manages multiple plugs based on the test findings. The Smart Energy Management algorithm suggests that employing smart plugs as load controllers results in a significant decrease in energy consumption (0.811 kW min or 0.0134 kWh) when accompanied by the appropriate scheduling algorithm. This technology holds potential in a comprehensive smart energy management system. The data's insights highlight the superiority of the proposed approach compared to current standard practices.
Development of a smart cloud-based monitoring system for solar photovoltaic energy generation
(KeAi chinese roots global impact, 2025-04-01) Rao, Challa Krishna; Sahoo, Sarat Kumar; Yanine, Fernando
The main controllers overseeing both solar panels and loads have all panels connected with sensors. The radiation striking the solar cell determines the power produced and real-time monitoring is crucial to evaluating the performance of a solar photovoltaic system. The emerging Internet of Things provides an opportunity to significantly enhance the monitoring of solar energy output and plant operations. To achieve this, a remote monitoring system is necessary, utilizing the Internet of Things to gather and transmit data. This study aims to utilize the Internet of the Things to monitor solar photovoltaic systems and assess their effectiveness. The monitoring system includes components such as a data gateway, data collection, and presentation for a cloud application. The collected data were stored in the cloud, enabling a visual representation of the sensed parameters. The system achieved a better accuracy rate, with an average transmission time of 53.01 s. The results indicate that the recommended monitoring system allowed users to observe current, voltage, and daylight, which could serve as a viable substitute for smart monitoring of solar energy output and plant operations.
Development of intelligent smart energy monitoring systems for renewable energy source using Proteus‑8 environment and validated with experimentation
(Springer Nature, 2025-04-22) Rao, Challa Krishna; Sahoo, Sarat Kumar; Yanine, Fernando
Customers of energy, both in residential and commercial structures, are now more interested in lowering their energy usage as an effect of the feed-in tariffs for renewable resources and the recent rise in electricity rates. The central control system and smart power Plug proposed in this study use the XBee communication protocol to manage energy use. Smart energy management systems are used to measure and optimize power use at the consumer premises level. The design and development of wireless smart Plugs that can assess several power characteristics and gather data on the real-time power use of individual consumer appliances is the main goal of this paper. An XBee transmitter and receiver node aids in the formation of the Consumer Area Network, which is created by the SEMS setup. The central node’s real-time data collection allows for the scheduling and prioritization of the appliances. Consumer appliance datasets may be created using the SEMS setup, and additional datasets can be utilized for load disaggregation. The configuration of the system allows for wireless data transfer from smart outlets to a central controller. The connected devices to the smart Plug are then turned on or off by the system using control instructions generated by the data analysis. According to test findings, the suggested smart Plug can assess the power consumption of wirelessly connected devices up to 18 meters away with accuracy and without compromising data. Based on a planned user program code, the central controller is capable of successfully controlling several Plugs. The proposed Smart Energy Management algorithm demonstrates that using smart Plugs as load controllers results in a decrease in energy consumption of 0.811 kW min (0.0134 kWh) with the right scheduling algorithm, the suggested smart Plug technology may, therefore, be used to its full potential in a smart energy management system. The data’s findings show how much better the proposed approach is than the standard ones in use now.
Implementation of real-time optimal load scheduling for IoT-based intelligent smart energy management system using new decisive algorithm
(Springer Nature, 2025-03-15) Rao, Challa Krishna; Sahoo, Sarat Kumar; Yanine, Fernando
This paper presents the implementation of a real-time optimal load scheduling system for an IoT-based intelligent smart energy management system (SEMS) using a novel decisive algorithm. The increasing use of electrical equipment by consumers often leads to a mismatch between demand and supply, posing signifcant challenges to the energy sector. The proposed system addresses these challenges by optimizing load distribution and enhancing energy efciency through advanced demand-side management techniques. By leveraging real-time data from IoT sensors and incorporating user preferences, the new algorithm dynamically adjusts power consumption to avoid peak-hour overloads, thus preventing widespread power outages. Experimental results demonstrate that the system efectively reduces overall energy consumption while maintaining user comfort and optimizing costs. The innovative approach of controlled partial load shedding based on consumer priorities ensures a balanced and resilient energy supply. This study highlights the potential of IoT and advanced algorithms in transforming energy management practices and providing sustainable solutions for the future.
Intelligent power management system for optimizing load strategies in renewable generation
(Springer Nature, 2024-08-29) Rao, Challa Krishna; Sahoo, Sarat Kumar; Yanine, Fernando
Effectively utilizing renewable energy sources while avoiding power consumption restrictions is the problem of demand-side energy management. The goal is to develop an intelligent system that can precisely estimate energy availability and plan ahead for the next day in order to overcome this obstacle. The Intelligent Smart Energy Management System (ISEMS) described in this work is designed to control energy usage in a smart grid environment where a significant quantity of renewable energy is being added. The proposed system evaluates various prediction models to achieve accurate energy forecasting with hourly and day-ahead planning. When compared to other prediction models, the Support Vector Machine (SVM) regression model based on Particle Swarm Optimization (PSO) seems to have better performance accuracy. Then, using the anticipated data, the experimental setup for ISEMS is shown, and its performance is evaluated in various configurations while considering features that are prioritized and user comfort. Furthermore, Internet of Things (IoT) integration is put into practice for monitoring at the user end.
IoT enabled intelligent energy management system employing advanced forecasting algorithms and load optimization strategies to enhance renewable energy generation
(Elsevier, 2024-08-08) Rao, Challa Krishna; Sahoo, Sarat Kumar; Yanine, Fernando
Effectively utilizing renewable energy sources while avoiding power consumption restrictions is the problem of demand-side energy management. The goal is to develop an intelligent system that can precisely estimate energy availability and plan ahead for the next day in order to overcome this obstacle. The Intelligent Smart Energy Management System (ISEMS) described in this work is designed to control energy usage in a smart grid environment where a significant quantity of renewable energy is being introduced. The proposed system evaluates various predictive models to achieve accurate energy forecasting with hourly and day-ahead planning. When compared to other predictive models, the Support Vector Machine (SVM) regression model based on Particle Swarm Optimization (PSO) seems to have better performance accuracy. Then, using the anticipated requirements, the experimental setup for ISEMS is shown, and its performance is evaluated in various configurations while considering features that are prioritized and associated with user comfort. Furthermore, Internet of Things (IoT) integration is put into practice for monitoring at the user end.
Renewable power generation price prediction and forecasting using machine learning
(Wiley, 2024-05-28) Rao, Challa Krishna; Sahoo, Sarat Kumar; Yanine, Fernando
In the power market, electricity price forecasting (EPF) is a crucial consideration for decision-making. The need to optimize earnings by adjusting bids in day-ahead power markets is becoming more and more important to different market participants. Prior information is required for marketers to have an advantage over the competition while controlling the risk of pricing fluctuation. However, not all marketers must accurately predict the worth of future pricing when making decisions. To make a choice, it is necessary to determine whether the cost will be prohibitive. Thus, in order to determine the prices that have an impact on marketers, electricity price classification is first performed. Based on a threshold value, prices are categorized as low class prices and high class pricing. In order to determine the precise value of prices for utility maximization, the EPF has been explored next. In order to maximize advantage or utility, buying and selling bidding techniques rely on the accurate projections of prices for the following day. Effective forecasting models can improve the performance of producers and consumers, who play important roles in the electrical markets. The EPF approaches now in use produce complicated models and are not generalizable.The best method for forecasting prices with superior generalization performance, kernel functions, and distributive prediction is to use machine learning (ML)-based models. In order to forecast prices, machine learning-based electricity price forecasting has been studied. On the markets of Ontario, Austria, and India, the ML models have been put to the test. Neural networks, support vector machines, core vector machines, information vector machines, and relevance vector machines (RVM) have all been used with EPF, and their performances have been assessed in order to determine the optimum model. When all of these models are examined, it becomes clear that RVM is the most effective and trustworthy ML approach for various markets. The price of power fluctuates a lot, with both typical costs and spikes that might be tens to hundreds of times higher than the average price range. The prediction for the overall price of energy is created by adding together average prices and price peaks. A good and precise price spike forecast is required because price spikes have a big influence on the electrical market. As a result, a threshold value has also been used to forecast the spike value. The potential for more research in this field has also been emphasized.

Examinando por Autor "Rao, Challa Krishna"

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