Artículos de Revistas

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https://repositorio.uft.cl/handle/20.500.12254/2436

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Design of an EEG analytical methodology for the analysis and interpretation of cerebral connectivity signals
(Elsevier, 2022-02-20) Córdova, Felisa M.; Cifuentes, Hugo F.; Díaz, Hernán A.; Yanine, Fernando; Pereira, Robertino
The objective of this study is to design an Electroencephalographic (EEG) analytic methodology that allows to develop a variety of analysis and interpretations of brain signals. The initial phase considers the acquisition and filtering of EEG signals, the division into bands in data ranges, and the storage of EEG signals in a cloud data base. Then, an analytical phase considering descriptive, predictive and prescriptive analysis is accomplished. A sequence of analytic intermediate processing steps is done in order to render a graphic visualization of significant correlations between pairs of EEG channels. Pearson correlation is utilized to detect synchronic connectivity through the brain areas. Time series in nearly instantaneous time lapses are treated by using Hilbert Huang Transform. An experimental design by submitting a set of students to an abbreviated version Raven visual test is made providing results in correlation maps of cerebral connectivity
Si6C18: a bispentalene derivative with two planar tetracoordinate carbons
(Wiley, 2022-05-30) Inostroza, Diego; Leyva-Parra, Luis; Yañez, Osvaldo; Cruz, César J.; Garza, Jorge; García, Víctor; Thimmakondu, Venkatesan S.; Cerón, María Luisa; Tiznado, William
Here we show that substituting the ten protons in the dianion of a bispentalene derivative (C18H102 ) by six Si2+ dications produces a minimum energy structure with two planar tetracoordinate carbons (ptC). In Si6C18, the ptCs are embedded in the terminal C5 pentagonal rings and participate in a three-center, two-electron (3c-2e) SiptC-Si σ-bond. Our exploration of the potential energy surface identifies a triphenylene derivative as the putative global minimum. Nevertheless, robustness to Born–Oppenheimer molecular dynamics (BOMD) simulations at 900 and 1500 K supports bispentalene derivative kinetic stability. Chemical bonding analysis reveals ten delocalized π-bonds, which, according to Hückel's 4n + 2 π-electron rule, would classify it as an aromatic system. Magnetically induced current density analysis reveals the presence of intense local paratropic currents and a weakly global diatropic current, the latter agreeing with the possible global aromatic character of this specie.
On a pickup to delivery drone routing problem: models and algorithms
(Elsevier, 2022-09-06) Gómez-Lagos, Javier; Rojas-Espinoza, Benjamín; Candia-Véjar, Alfredo
A new variant of the Pickup and Delivery Routing problem is presented. Given a set of customers, facilities, a depot, and a homogeneous fleet of drones, the Pickup to Delivery Drone Routing Problem (PDDRP) aims to find a drone scheduling such that a drone serves the customer’s order from a set of available facilities. Each drone starts in the depot, flies to pickup the customer’s order in a facility, and continues its flight to deliver the parcel to a customer. Then, the drone begins another service, and once its last service is completed, it returns to the depot. The objective is to minimize the makespan associated with the drone fleet. The layer of facilities forcing drones to visit one of them to pickup the parcel makes the problem different from traditional pickup and delivery routing problems. Three mixed-linear programming models are presented to obtain optimal solutions for the problem. The first model is related to the multiple Traveling Salesman Problem (m-TSP), the second is associated with the Parallel Machine Scheduling Problem (PMS), and the third was developed specifically for the new problem. Given the high computational complexity of the PDDRP, a Greedy Randomized Adaptive Search Procedure (GRASP) was designed to find near-optimal solutions when exact approaches cannot achieve (near) optimal solutions. Computational experiments show that a commercial solver could solve only small problem instances. GRASP can find reasonable solutions in a short time when medium and large instance sizes need to be solved. Finally, is shown that some routing problems for delivery, allowing truck-drone collaboration, could be formulated as an extension of PMS.
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.
ML models for severity classification and length-of-stay forecasting in emergency units
(Elsevier, 2023-03-02) Candia-Véjar, Alfredo; Moya-Carvajal, Jonathan; Pérez-Galarce, Francisco; Taramasco, Carla; Astudillo, César A.
Length-of-stay (LoS) prediction and severity classification for patients in emergency units in a clinic or hospital are crucial problems for public and private health networks. An accurate estimation of these parameters is essential for better planning resources, which are usually scarce. Although it is possible to find several works that propose traditional Machine Learning (ML) models to face these challenges, few works have exploited advances in Natural Language Processing (NLP) on Spanish raw-text vector representations. Consequently, we take advantage of those advances, incorporating sentence embeddings in traditional ML models to improve predictions. Moreover, we apply a strategy based on SHapley Additive exPlanations (SHAP) values to provide explanations for these predictions. The results of our case study demonstrate an increase in the accuracy of the predictions using raw text with a minimum preprocessing. The precision increased by up to 2% in the classification of the patient’s post-care destination and by up to 8% in the prediction of LoS in the hospital. This evidence encourages practitioners to use available text to anticipate the patient’s need for hospitalization more accurately at the earliest stage of the care process.
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.
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.
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.
Rare-earth-based metal–organic frameworks with improved visible-light-harvesting properties: a quantum chemistry study
(Springer, 2023-05-25) Hidalgo-Rosa, Yoan; Saavedra-Torres, Mario; Koivisto, Bryan D.; Treto-Suárez, Manuel A.; Páez-Hernández, Dayán; Zarate, Ximena; Schott, Eduardo
This report proves that improving the visible-light-harvesting properties in rare-earth-based metal–organic frameworks (RE-MOFs) (labelled as RE–UiO-66, UiO = University of Oslo MOFs), with the aim of performing as potential visible-light-driven photocatalysts, is achievable. Thus, the design of MOFs with specific applications, especially those involving sunlight and material interactions, represents a growing field, which has been addressed in the herein work using quantum mechanical tools. We achieved to relate the light absorption properties with the structure in systems Y–UiO-66, Sc–UiO-66 and La–UiO-66, by evaluating the inclusion of well-known electron donor substituents in the structure of the 1,4-benzenedicarboxylate (BDC) linker (i.e. BDC-R, R: –CH3, –OH, –SH and –NH2). The electronic structure and optical properties of Y–UiO-66 were rigorously investigated using computational techniques combining molecular and periodic density functional theory (DFT) calculations. As a remarkable result, it was shown that including the groups –SH or –NH2 in the BDC linker, induced a shift in the absorption bands to the visible region (≥ 400 nm). Hence, a group of new RE-MOFs materials with optimal structural and photocatalytic properties is proposed. This could encourage researchers to prepare these new materials to be tested in photocatalysis, such as cleaving the C–H bond, water splitting or photocatalytic degradation of organic contaminants.
Photophysical characterization of tetrahydroxyphenyl porphyrin Zn(II) and V(IV) complexes: experimental and DFT study
(Springer Nature, 2023-06-21) Diaz-Uribe, Carlos; Rangel, Daily; Vallejo, William; Valle, Roger; Hidago-Rosa, Yoan; Zarate, Ximena; Schott, Eduardo
Photodynamic therapy (PDT) is a promising technique for the treatment of various diseases. In this sense, the singlet oxygen quantum yield (Φ∆) is a physical–chemical property that allows to stablish the applicability of a potential photosensitizers (PS) as a drug for PDT. In the herein report, the Φ∆ of three photosensitizers was determined: metal-free tetrahydroxyphenyl porphyrin (THPP), THPP-Zn and the THPP-V metal complexes. Their biological application was also evaluated. Therefore, the in vitro study was carried out to assess their biological activity against Escherichia coli. The metal-porphyrin complexes exhibited highest activities against the bacterial strain Escherichia coli. at the highest concentration (175 μg/mL) and show better activity than the free base ligand (salts and blank solution). Results indicated a relation between Φ∆ and the inhibitory activity against Escherichia coli, thus, whereas higher is the Φ∆, higher is the inhibitory activity. The values of the Φ∆ and the inhibitory activity follows the tendency THPP-Zn > THPP > THPP-V. Furthermore, quantum chemical calculations allowed to gain deep insight into the electronic and optical properties of THPP-Zn macrocycle, which let to verify the most probable energy transfer pathway involved in the singlet oxygen generation.
Revisiting the potential-energy surface of CnBe3n+2H2n+22+ (n = 2–4) clusters: are planar pentacoordinate carbon structures the global minima?
(Royal Society of Chemistry, 2023-07-07) Inostroza, Diego; Vásquez-Espinal, Alejandro; Leyva-Parra, Luis; García-Argote, Williams; Cerón, María Luisa; Yañez, Osvaldo; Tiznado, William
Using various exploration strategies, in this study, we investigated the potential energy surfaces (PES) of CBe5H5+ and CnBe3n+2H2n+22+ (n = 2-4) clusters. Previous studies proposed that the planar pentacoordinate carbons (ppCs) were the global minima of these clusters. However, our study identified new putative global minima and competitive isomers, refuting some previous assignments. We employed several methods, including evolutive-inspired stochastic approaches guided by "chemical criteria", and ab initio molecular dynamics simulations at elevated temperatures. Our results showed that the size of the scanned population significantly affected the evolutive method and that constrained or guided procedures showed an advantage in identifying better minima for larger systems. This study demonstrated that using multiple complementary strategies can result in a wider variety of minima in a given energy range. Our findings provide valuable insights into exploring the potential energy surfaces of clusters, mainly medium-sized clusters, which could be the connections between small clusters and nanomaterials.
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.
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.
Exploring the adsorption of five emerging pollutants on activated carbon: a theoretical approach
(Elsevier, 2024-06-06) González-Rodríguez, Lisdelys; Yáñez, Osvaldo; Mena-Ulecia, Karel; Hidalgo-Rosa, Yoan; García-Carmona, Ximena; Ulloa-Tesser, Claudia
The identification and management of contaminants of emerging concern (CECs) in water systems is crucial for protecting public and environmental health. This paper reports a theoretical approach to studying the adsorption of five CECs: Atrazine (ATZ), Caffeine (CAF), Carbamazepine (CBZ), Sulfamethoxazole (SMX), and Ibuprofen (IBU) - onto Activated Carbon (AC). A set of computational methods, including electrostatic molecular potential maps, conceptual density functional theory, Fukui functions, thermodynamic analysis, and tight-binding molecular dynamics simulations, were employed to analyze the electronic/energetic interactions and mechanisms involved in the adsorption of CECs on AC. The theoretical methodology offered valuable predictions on reactivity sites, stability, and binding mechanisms. Results showed that adsorption primarily occurred through non-covalent interactions like π-π electron donor-acceptor interactions, van der Waals forces, and hydrophobic interactions. Thermodynamic properties suggested the adsorption process was spontaneous and exothermic. However, for the AC/SMX system, the Gibbs free energy reveals that adsorption may be unfavorably compared to the other study systems. Molecular dynamics simulations validated the kinetic stability in the following order CAF (0.13 Å)>CBZ (0.23 Å)>ATZ (0.75 Å)> IBU (1.28 Å)>SMX (1.54 Å). This exploratory theoretical study provides a deep understanding of the interactions between AC and five CECs, aiding in the rational design and optimization of AC-based treatment systems for environmental and industrial applications.
UiO-66(Zr) as drug delivery system for non-steroidal anti-inflammatory drugs
(Elsevier, 2024-06-06) Salazar, Javier; Hidalgo-Rosa, Yoan; Burboa, Pia C.; Escalona, Néstor; Leiva, Angel; Zarate, Ximena; Schott, Eduardo
The toxicity for the human body of non-steroidal anti-inflammatory drugs (NSAIDs) overdoses is a consequence of their low water solubility, high doses, and facile accessibility to the population. New drug delivery systems (DDS) are necessary to overcome the bioavailability and toxicity related to NSAIDs. In this context, UiO-66(Zr) metal-organic framework (MOF) shows high porosity, stability, and load capacity, thus being a promising DDS. However, the adsorption and release capability for different NSAIDs is scarcely described. In this work, the biocompatible UiO-66(Zr) MOF was used to study the adsorption and release conditions of ibuprofen, naproxen, and diclofenac using a theoretical and experimental approximation. DFT results showed that the MOF-drug interaction was due to an intermolecular hydrogen bond between protons of the groups in the defect sites, (μ3 − OH, and − OH2) and a lone pair of oxygen carboxyl functional group of the NSAIDs. Also, the experimental results suggest that the solvent where the drug is dissolved affects the adsorption process. The adsorption kinetics are similar between the drugs, but the maximum load capacity differs for each drug. The release kinetics assay showed a solvent dependence kinetics whose maximum liberation capacity is affected by the interaction between the drug and the material. Finally, the biological assays show that none of the systems studied are cytotoxic for HMVEC. Additionally, the wound healing assay suggests that the UiO-66(Zr) material has potential application on the wound healing process. However, further studies should be done.
Furfural adsorption on V2O5 surface: A combined experimental-theoretical study
(Elsevier, 2024-06-16) Lizana, Ignacio; Schott , Eduardo; Saavedra-Torres, Mario; Hidalgo-Rosa, Yoan; Pecchi, Gina; Karelovic, Alejandro; Zarate, Ximena;
The adsorption of furfural on the V2O5 surface was investigated using experimental and theoretical methods. In situ Diffuse Reflectance Infrared Fourier-Transform Spectroscopy results show the presence of physi- and chemi-sorption phenomena, where trans-furfural is mostly chemisorbed at the beginning of the adsorption process. These results are in agreement with theoretical DFT results, as the most thermodynamically favored configurations corresponds to the chemisorbed trans-furfural (T1) and cis-furfural (C1) with binding energies of −1.83 and −2.05 eV.
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.
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.
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.
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.

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