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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.
Techno-economic assessment of a green hydrogen production plant for a mining operation in Chile
(Elsevier, 2025-03-25) Sánchez-Squella, Antonio; Muñoz, Mario; Toledo, Mario; Yanine, Fernando
Mining operations are the most important industrial activity in the northern regions of Chile with a high use of fossil fuels. This paper develops a techno-economic assessment of a green hydrogen complex designed to supply fuel to stationary diesel electric generators (small-scale project) and fuels cells trucks (medium-scale project) for a company dedicated to exploiting stone materials for the mining sector. The hydrogen complex consists of a photovoltaic plant, electrolyzers, hydrogen compressors and storage, considering geographical location, solar resource and company layout. The economic assessment considers the Levelized Cost of Electricity (LCOE) and Levelized Cost of Hydrogen (LCOH), including investment and sensitivity analysis. For the small-scale project, a 60-kW photovoltaic plant installed in an area of 1000 m2, with an investment of US$ 69,000, results in an average LCOH of 4.88 US$. However, the NPV is -$47,645 and the IRR is −22%, indicating that this project is not viable. For the medium-scale project, a 1700-kW photovoltaic plant installed in an area of 28,000 m2, with an investment of US$ 2,100,000, results in an average LCOH of 4.62 US$. The results indicate feasibility, economic convenience, and scalability, marking this project as a precursor for future initiatives in the Chilean mining sector.
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.
The Impact of Audiovisual Content on Bitcoin’s Transaction Volume and Price
(Universidad Catolica de Colombia, 2025-02-28) Campos, Zocimo; Yanine, Fernando; Catalan, Sebastián
This article examines the impact of audiovisual content on Bitcoin’s price and transaction volume, a topic with limited exploration in financial literature. Using correlation analysis and econometric models, it investigates audience data from films, series, and documentaries about Bitcoin, alongside public interest metrics from YouTube and Twitch. The results show weak correlations (<0.2) between search levels of most titles and Bitcoin’s financial variables. However, on YouTube, an increase in subscribers to cryptocurrency-related channels has positively and significantly affected Bitcoin’s price and transaction volume. These findings highlight the influence of content creators on cryptocurrency adoption and investment, offering a framework for future research on the impact of audiovisual media on financial markets. Este artículo analiza el impacto del contenido audiovisual en el precio y el volumen de transacciones de Bitcoin, un área poco explorada en la literatura financiera. A través un análisis correlacional y econométrico, se estudian datos de audiencia de películas, series y documentales sobre Bitcoin, junto con métricas de interés público en YouTube y Twitch. Los resultados muestran correlaciones débiles (<0,2) entre los niveles de búsqueda de la mayoría de los títulos y las variables financieras de Bitcoin. Sin embargo, en YouTube, el aumento de suscriptores en canales relacionados con criptomonedas tiene un efecto positivo y significativo en el precio y el volumen de transacciones de Bitcoin. Estos hallazgos resaltan la influencia de los creadores de contenido en la adopción e inversión en criptomonedas, lo que proporciona un marco para investigaciones futuras sobre el impacto de los medios audiovisuales en los mercados financieros.
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.
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.
Profiling and characterization of plant-based products consumers in Chile
(Asociación Castellano-Manchega de Sociología (ACMS), 2024-12-14) Campos, Zócimo; Yanine, Fernando; Nikulin Chandia, Christopher; Oyarce Ibarra, Claudia
The consumption trend of plant-based products has increased worldwide due to different reasons, and Chile has been a good example of this trend. Although sustainability, health, nutrition and taste may all seem to be main drivers for flexitarians seeking plant-based food and beverages, to date there is no research that allows characterizing and profiling Chilean consumers of plant-based products. Hence, this work seeks to close this gap. To address this problem, we conducted an online survey of 512 people, mainly focused on the Metropolitan Region and Valparaíso. Using Ward’s method, we identified 3 clusters: (1) Animal friendly, (2) Planet friendly and (3) Healthy lifestyle, where, respectively, animal welfare, care for the environment and concern for health are prioritized.
Influence of Iodine substitutions on the photophysical properties of rhenium(I) tricarbonyl complexes with pyridine schiff base (PSB-IHB)
(Wiley, 2025-01-08) Morales-Guevara, Rosaly; Páez-Hernández, Dayán; Ancede-Gallardo, Evys; Carreño, Alexander
This theoretical investigation aims to evaluate the influence of iodine substitutions on the phenolic moiety of the pyridine Schiff bases harboring an intramolecular hydrogen bond (PSB-IHB) ancillary ligand within the monocationic. fac-[Re(CO)3(N,N)(PSB-IHB)]+ architecture on photophysical properties, particularly emission range. Optimized structures of two Re(I) tricarbonyl complexes (C1 and C2) were analyzed, revealing a distorted octahedral coordination geometry. Geometric parameters were compared with experimental data from analogous complexes. NBO analysis confirmed the presence of intramolecular hydrogen bonds (IHBs) in both singlet and triplet states, providing significant stabilization. Theoretical calculations predicted three distinct absorption bands for all complexes in dichloromethane, indicating substantial electronic delocalization between the pyridinic and phenolic rings via the azomethine group. These findings underscore the crucial role of delocalization and donor–acceptor interactions in stabilizing Re(I) tricarbonyl complexes and their impact on photophysical properties. Emissions calculated for the C1 and C2 complexes were observed within the range of 632–643 nm. The presence of IHBs was found to be essential for modulating photophysical properties, with emissions attributed to ligand-to-ligand charge transfer transitions.
Tuning the sensitization pathway T1→ 5DJ in Eu-based MOF through modification of the antenna ligand: a theoretical approach via multiconfigurational quantum calculations
(Elsevier, 2023-04-27) Hidalgo-Rosa, Yoan; Santoyo-Flores, Julián; Treto-Suárez, Manuel A.; Schott, Eduardo; Páez-Hernández, Dayán; Zarate, Ximena
The current study demonstrates the possibility of improving the efficiency of Eu3+ sensitization in an Eu-based MOF, labelled as EuL-R, by modifying the antenna ligand. The electronic structure of seven antenna ligand derivatives with the form R-4-(pyrimidin-5-yl) benzoic acid (L-R) ligand derivates was thoroughly examined in this research. Also, the most likely energy transfer channels for the sensitization pathway of Eu3+ ions in the EuL-R systems (R: CH3, –OH, –SH and –NH2) was studied. The efficiency in the intersystem crossing (ISC) process from S1 to T1 in the antenna make plausible a subsequent energy transfer to the emissive state in the Eu3+. This process was studied via multireference CASSCF/NEVPT2 calculations due to the multiconfigurational character of Eu3+ ions. Based on Latva and Reindhout's empirical rules, the 4-(2-(thiophen-2-yl)pyrimidin-5-yl)benzoic acid (L6) was chosen as efficient antenna for the sensitization process in this system. In conclusion, it was discovered that including the thiophenyl group at the L antenna (L6), increased the energy gaps S1→T1 and T1→5DJ (Eu3+) to an optimal range for the ISC and subsequent energy transfer. Finally, a deeply understanding of the sensitization and emission mechanisms was possible from the excited-state dynamics analysis of the L6 antenna, via fluorescence, inter-system crossing (ISC), phosphorescence, and kF, kISC, and kP, rates calculations. This study therefore highlights the importance of a thorough theoretical procedure via a robust quantum mechanical tool to guide the development of novel luminescent lanthanide-based MOFs.
Diagnosing grid's service quality issues: the virtual microgrid and the digitization and innovations in the power distribution grid
(Elsevier, 2024-08-23) Cordova, Felisa; Yanine, Fernando; Carrasco, Sergio; Rother, Hans; Duarte, Vanessa
Power quality issues from small and medium-sized grid-integrated solar photovoltaic systems (PMGD), whose plant capacity is less than or equal to 9 MW, challenge electric power distribution companies like ENEL Distribución S.A. in Chile. To tackle these challenges, leveraging rapidly configurable, scalable, and easily deployable distributed energy resources (DER) through a virtual power plant (VPP) is crucial. This paper introduces a "virtual microgrid," a specific VPP design and functional specification that swiftly diagnoses and resolves power quality issues. Virtual power plants use renewable energy sources, energy storage, and smart energy management to provide ancillary services to the grid. With the expanding PMGD market in Chile and globally, there is an urgent need for deployable DER to energize feeder sub-sections during reliability events and create grid pathways between DER and loads. Thus, DER are essential for enhancing grid flexibility, reliability, and maintaining the stability and integrity of electric supply quality standards. VPPs can reduce high costs associated with utility grid power capacity deficiencies in areas with unstable power supply, enhancing resilience and flexibility without compromising system stability. This study presents a novel approach—currently at the conceptual development stage—for diagnosing service quality issues related to grid supply through the rapid deployment of a configurable and fully scalable virtual microgrid. This solution is particularly relevant for zones in Santiago, Chile, where ENEL Chile faces challenges due to the increasing presence of PMGD—independent solar farms injecting all their production into the grid for profit, as permitted by current Chilean electric law (N°88/2019 of Ministry of Energy). Such practices negatively impact grid service quality standards, which can be mitigated through the deployment of virtual microgrids. The concept of a "virtual microgrid" emphasizes rapid configurability, scalability, and ease of deployment, tailored to address specific grid quality issues. Unlike previous efforts, this approach focuses on seamlessly integrating DER with existing grid infrastructure to quickly address and mitigate power quality issues caused by PMGD.
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.
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.
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.
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.
Editorial: Recent advances in renewable energy automation and energy forecasting
(Frontiers Media S.A., 2023-05-10) Sahoo, Sarat Kumar; Yanine, Fernando; Kulkarni, Vikram; Kalam, Akhtar
Renewable energy sources like solar, wind, and hydroelectric power are gaining popularity as we work towards a more sustainable future. However, their intermittent and often unpredictable nature, creates challenges for the energy industry in terms of being able to ensure continuous electric power generation over regular periods of time. Thus, accurate forecasting of renewable energy output is crucial for their reliable integration into the power grid. In this regard, automation and machine learning have made significant improvements in energy forecasting by enabling more precise predictions of energy output. Advanced algorithms and high-performance computing systems allow for better grid management and increased power generation systems’ efficiency. Automation is also being used for the operation and maintenance of renewable energy systems. Real-time monitoring and control systems enable a rapid response to changes in weather conditions, optimizing energy production. This editorial summarizes recent advancements in renewable energy automation and energy forecasting, which are critical areas for achieving a sustainable energy future. The Research Topic covers areas like machine learning-based energy forecasting, control and optimization of renewable energy systems, and the integration of renewable energy into microgrids as shown in Figure 1. Continued research and development in renewable energy automation and energy forecasting are essential for the transition towards a sustainable energy future.
Sustaining business performance management: an operational framework
(Elsevier, 2023-01-01) Yanine, Fernando; Campos, Zocimo
This article explores an increasingly important theme for all business sectors: sustainability and its linkage to business performance management, and proposes an operational framework based on the Balanced Scorecard model. Enterprise sustainability is a subject that is extensively being assessed and measured in various industries nowadays. We focus on Chilean SMEs and explore how a sustainability trend led by technology-related innovation and performance measurement systems can enable and allow to maintain sustainable enterprises through the today's volatile, uncertain and complex socio political and economic scenario. Worldwide, issues such as sustainability, innovation and social responsibility are driving business performance management (BPM) of thriving companies, many of which champion for greater social equity, business sustainability and fairness. This process entails a paradigm shift that is particularly noticeable in small and medium size enterprises or SMEs―the largest segment of Latin America's businesses, and especially so in Chile, a country which, according to OECD 2022’s Table 8.2. Distribution of firms in Chile, 2018, SMEs account for over 90% of the enterprises in the country.
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.
Design and development of grid connected home automation system for prosumers
(Institute of Electrical and Electronics Engineers (IEEE), 2023-03-23) Patro, Swati P.; Sahoo, Sarat Kumar; Thanarak, Paprita; Yanine, Fernando
Home automation system extend convenience and sophisticated to operate electronic product with in the household. Home automation can be a solution to automating functions like monitoring, controlling etcetera in the home. This paper describes the drives, restraints, opportunities and challenges pertaining to the home automation system market. The grid solar power system is connected to the utility grid where solar power is generated. The power generated if in excess send back to utility grid. Hence the consumer gets compensation as the extra power sent back to the utility grid. Whenever the sunlight, electricity is generated by solar cell, the inverter connected to grid convert the dc power into ac power. The electricity produced is routed to the grid from, where it used to run the various appliances such as air conditioners, washing machine, refrigerator, geyser, induction cooker, pump etc. The excess power at every instant is transmitted to the ground without grid connection and if solar power system is synchronized with the utility grid then return to the grid. It will also determine the feasibility of reducing green gas emission by the use of grid tied PV power system.
Closed-shell d10–d10 mechanochromic [AuPh(CNPh)]n complex: quantum chemistry electronic and optical properties
(Royal Society of Chemistry, 2024-01-31) Mendizabal, Fernando; Cerón, María Luisa; Lara, Dina; Miranda-Rojas, Sebastián
The electronic structure, spectroscopic properties, and solid state chemistry of monomer and dimers of [AuPh(CNPh)] complex were studied at post-Hartree-Fock (MP2, SCS-MP2, and CC2) and density functional theory levels. The absorption spectra of these complexes were calculated using single excitation time-dependent (TD) methods at DFT, CC2, and SCS-CC2 levels. The influences of the bulk are accounted for at the PBE-D3 level, incorporating dispersion effects. The calculated values agree with the experimental range, where absorption and emission energies reproduce experimental trends with large Stokes shifts. The aurophilic interaction is identified as a key factor influencing the spectroscopic and structural properties of these complexes. The intermetallic interactions were found as the main factor responsible for MMCT electronic transitions in the models studied.

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