A multilayer perceptron neural network approach for optimizing solar irradiance forecasting in Central Africa with meteorological insights

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Feb. 12, 2024

Abstract Promoting renewable energy sources, particularly in the solar industry, has potential to address shortfall Central Africa. Nevertheless, a difficulty occurs due erratic characteristics of irradiance data, which is influenced by climatic fluctuations and challenging regulate. The current investigation focuses on predicting an inclined surface, taking into consideration impact variables such as temperature, wind speed, humidity, air pressure. used methodology for this objective Artificial Neural Network (ANN), inquiry carried out metropolitan region Douala. data collection device research meteorological station located at IUT This was built component Douala sustainable city effort, partnership with CUD IRD. Data collected 30-min intervals duration around 2 years, namely from January 17, 2019, October 30, 2020. aforementioned been saved database that underwent pre-processing Excel later employed MATLAB creation artificial neural network model. 80% available utilized training network, 15% allotted validation, remaining 5% testing. Different combinations input were evaluated ascertain their individual degrees accuracy. logistic Sigmoid function, 50 hidden layer neurons, yielded correlation coefficient 98.883% between observed estimated sun irradiation. function suggested evaluating intensities radiation place being researched other sites have similar conditions.

Language: Английский

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A new intelligently optimized model reference adaptive controller using GA and WOA-based MPPT techniques for photovoltaic systems

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: March 21, 2024

Recently, the integration of renewable energy sources, specifically photovoltaic (PV) systems, into power networks has grown in significance for sustainable generation. Researchers have investigated different control algorithms maximum point tracking (MPPT) to enhance efficiency PV systems. This article presents an innovative method address problem systems amidst swiftly changing weather conditions. MPPT techniques supply load during irradiance fluctuations and ambient temperatures. A novel optimal model reference adaptive controller is developed designed based on MIT rule seek global without ripples rapidly. The suggested also optimized through two popular meta-heuristic algorithms: genetic algorithm (GA) whale optimization (WOA). These approaches been exploited overcome difficulty selecting adaptation gain MRAC controller. voltage generated study neuro-fuzzy inference system. controller's performance tested via MATLAB/Simulink software under varying temperature radiation circumstances. Simulation carried out using a Soltech 1sth-215-p module coupled boost converter, which powers resistive load. Furthermore, emphasize recommended algorithm's performance, comparative was done between GA WOA conventional incremental conductance (INC) method.

Language: Английский

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Enhancing grid-connected photovoltaic system performance with novel hybrid MPPT technique in variable atmospheric conditions

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: April 8, 2024

Abstract This paper proposes an innovative approach to improve the performance of grid-connected photovoltaic (PV) systems operating in environments with variable atmospheric conditions. The dynamic nature parameters poses challenges for traditional control methods, leading reduced PV system efficiency and reliability. To address this issue, we introduce a novel integration fuzzy logic sliding mode methodologies. Fuzzy enables effectively handle imprecise uncertain data, allowing decision-making based on qualitative inputs expert knowledge. Sliding control, known its robustness against disturbances uncertainties, ensures stability responsiveness under varying Through these methodologies, our proposed offers comprehensive solution complexities posed by real-world dynamics. We anticipate applications across various geographical locations climates. By harnessing synergistic benefits promises significantly enhance reliability presence On grid side, both PSO (Particle Swarm Optimization) GA (Genetic Algorithm) algorithms were employed tune current controller PI (Proportional-Integral) (inverter control). Simulation results, conducted using MATLAB Simulink, demonstrate effectiveness hybrid MPPT technique optimizing system. exhibits superior tracking efficiency, achieving convergence time 0.06 s 99.86%, less oscillation than classical methods. comparison other techniques highlights advantages approach, including higher faster response times. simulation outcomes are analyzed strategies sides (the array side). Both offer effective methods tuning controller. According considered IEEE standards low-voltage networks, total harmonic distortion values (THD) obtained considerably high (8.33% 10.63%, algorithms, respectively). Comparative analyses terms stability, rapid changes.

Language: Английский

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Machine learning-based energy management and power forecasting in grid-connected microgrids with multiple distributed energy sources

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Aug. 19, 2024

The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and management. This paper explores the use advanced machine learning algorithms, specifically Support Vector Regression (SVR), to enhance efficiency reliability these systems. proposed SVR algorithm leverages comprehensive historical production data, detailed weather patterns, dynamic grid conditions accurately forecast generation. Our model demonstrated significantly lower error metrics compared traditional linear regression models, achieving a Mean Squared Error 2.002 for solar PV 3.059 wind forecasting. Absolute was reduced 0.547 0.825 scenarios, Root (RMSE) 1.415 1.749 power, showcasing model's superior accuracy. Enhanced predictive accuracy directly contributes optimized resource allocation, enabling more precise control schedules reducing reliance on external sources. application our resulted an 8.4% reduction overall operating costs, highlighting its effectiveness improving management efficiency. Furthermore, system's ability predict fluctuations output allowed adaptive real-time management, stress enhancing system stability. approach led 10% improvement balance between supply demand, 15% peak load 12% increase utilization enhances stability by better balancing mitigating variability intermittency These advancements promote sustainable microgrid, contributing cleaner, resilient, efficient infrastructure. findings this research provide valuable insights development intelligent systems capable adapting changing conditions, paving way future innovations Additionally, work underscores potential revolutionize practices providing accurate, reliable, cost-effective solutions integrating existing infrastructures.

Language: Английский

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Coordinated power management strategy for reliable hybridization of multi-source systems using hybrid MPPT algorithms

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: May 4, 2024

Abstract This research discusses the solar and wind sourcesintegration in aremote location using hybrid power optimization approaches a multi energy storage system with batteries supercapacitors. The controllers PV turbine systems are used to efficiently operate maximum point tracking (MPPT) algorithms, optimizing overall performance while minimizing stress on components. More specifically, generator, provided method integrating Perturb & Observe (P&O) Fuzzy Logic Control (FLC) methods. Meanwhile, for turbine, proposed approach combines P&O FLC These MPPT strategies photovoltaic (PV) aim optimize its operation, taking advantage of complementary features two While primary these is both therefore system, they also supply electricity load. For storage, this isolated renewable play crucial role due several specific benefits reasons. Unfortunately, their density still relatively lower compared some other forms storage. Moreover, have limited number charge–discharge cycles before capacity degrades significantly. Supercapacitors (SCs) provide significant advantages certain applications, particularly those that need density, quick charging discharging, long cycle life. However, limitations, such as voltage requirements, make them most effective when combined technologies, batteries. Furthermore, enhanced, result more dependable cost-effective (HESS). paper introduces novel algorithm management designed an efficient control. it focuses managing keep state charge (SOC) within defined range. simple effective. ensures longevity SCs maximizing performance. results reveal suggested successfully keeps limits (SOC). To show significance design choices impact battery’s SOC, which components, comparison been made. A classical one (PV/wind turbine/batteries) HESS batteries). scenario investigated resources appears be optimum solution areas where complementary. balance between seems contribute less potentially leading longer lifespan. An economical study has made, Homer Pro software, feasibility studied area.

Language: Английский

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Enhanced Wombat Optimization Algorithm for Multi-Objective Optimal Power Flow in Renewable Energy and Electric Vehicle Integrated Systems

Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 103671 - 103671

Published: Dec. 1, 2024

Language: Английский

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Multi-objective energy management in a renewable and EV-integrated microgrid using an iterative map-based self-adaptive crystal structure algorithm

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: July 8, 2024

Abstract The use of plug-in hybrid electric vehicles (PHEVs) provides a way to address energy and environmental issues. Integrating large number PHEVs with advanced control storage capabilities can enhance the flexibility distribution grid. This study proposes an innovative management strategy (EMS) using Iterative map-based self-adaptive crystal structure algorithm (SaCryStAl) specifically designed for microgrids renewable sources (RESs) PHEVs. goal is optimize multi-objective scheduling microgrid wind turbines, micro-turbines, fuel cells, solar photovoltaic systems, batteries balance power store excess energy. aim minimize operating costs while considering impacts. optimization problem framed as nonlinear constraints, fuzzy logic aid decision-making. In first scenario, optimized all RESs installed within predetermined boundaries, in addition grid connection. second operates turbine at rated power. third case involves integrating into three charging modes: coordinated, smart, uncoordinated, utilizing standard RES SaCryStAl showed superior performance operation cost, emissions, execution time compared traditional CryStAl other recent methods. proposed achieved optimal solutions scenario cost emissions 177.29 €ct 469.92 kg, respectively, reasonable frame. it yielded values 112.02 196.15 respectively. Lastly, achieves 319.9301 €ct, 160.9827 128.2815 uncoordinated charging, coordinated smart modes Optimization results reveal that outperformed evolutionary algorithms, such differential evolution, CryStAl, Grey Wolf Optimizer, particle swarm optimization, genetic algorithm, confirmed through test cases.

Language: Английский

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Control Strategy for DC Micro-Grids in Heat Pump Applications with Renewable Integration

Electronics, Journal Year: 2025, Volume and Issue: 14(1), P. 150 - 150

Published: Jan. 2, 2025

DC micro-grids are emerging as a promising solution for efficiently integrating renewable energy into power systems. These systems offer increased flexibility and enhanced management, making them ideal applications such heat pump (HP) However, the integration of intermittent sources with optimal management in these poses significant challenges. This paper proposes novel control strategy designed specifically to improve performance micro-grids. The enhances by leveraging an environmental mission profile that includes real-time measurements generation evaluation. micro-grid application pumps integrates photovoltaic (PV) systems, wind generators (WGs), DC-DC converters, battery storage (BS) proposed employs intelligent maximum point tracking (MPPT) approach uses optimization algorithms finely adjust interactions among subsystems, including sources, batteries, load (heat pump). main objective this is maximize production, system stability, reduce operating costs. To achieve this, it considers factors heating cooling demand, fluctuations from MPPT requirements PV system. Simulations over one year, based on real meteorological data (average irradiance 500 W/m2, average annual speed 5 m/s, temperatures between 2 27 °C), carried out Matlab/Simulink R2022a, have shown model predictive (MPC) significantly improves micro-grids, particularly applications. ensures stable bus voltage (±1% around V) maintains state charge (SoC) batteries 40% 78%, extending their service life 20%. Compared conventional methods, efficiency 15%, reduces costs 30%, cuts CO2; emissions 25%. By incorporating strategy, sustainable reliable applications, contributing transition towards cleaner more resilient also opens new possibilities grids, providing efficient at local level.

Language: Английский

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Performance Improvement of a Standalone Hybrid Renewable Energy System Using a Bi-Level Predictive Optimization Technique

Sustainability, Journal Year: 2025, Volume and Issue: 17(2), P. 725 - 725

Published: Jan. 17, 2025

A standalone hybrid renewable energy system (HRES) that combines different types of sources and storages offers a sustainable solution by reducing reliance on fossil fuels minimizing greenhouse gas emissions. In this paper, involving wind turbines, photovoltaic (PV) modules, diesel generators (DG), battery banks is proposed. For purpose, it necessary to size run the proposed for feeding residential load satisfactorily. two typical winter summer weeks, weather historical data, including irradiance, temperature, speed, profiles, are used as input data. The overall optimization framework formulated bi-level mixed-integer nonlinear programming (BMINLP) problem. upper-level part represents sizing sub-problem solved based economic environmental multi-objectives. lower-level management strategy (EMS) sub-problem. EMS task utilizes model predictive control (MPC) approach achieve optimal technoeconomic operational performance. By definition BMINLP, defined within constraints MATLAB R2023a environment employed execute extract results entire global solver “ga” utilized implement upper while “intlinprg” solves lower evaluation metrics in study operating, maintenance, investment costs, storage unit degradation, number CO2

Language: Английский

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Harnessing the Internet of Behavior (IoB) for Food Safety and Quality

Advances in psychology, mental health, and behavioral studies (APMHBS) book series, Journal Year: 2025, Volume and Issue: unknown, P. 271 - 300

Published: Jan. 22, 2025

In this chapter, we propose a conceptual model to utilize the Internet of Behavior (IoB) for improving food safety and quality. This chapter aims at providing an exhaustive insight on what can cannot be done with behavioral data increase protocols, in other words constructive debrief theoretical foundations potential applications surrounding IoB. By means literature reviews models, presents how IoB effectively enhance quality assurance capabilities while confronting several issues view creating emerging research area academia as well supporting industry best practices

Language: Английский

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