An analysis of case studies for advancing photovoltaic power forecasting through multi-scale fusion techniques

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

Published: March 20, 2024

Integration renewable energy sources into current power generation systems necessitates accurate forecasting to optimize and preserve supply-demand restrictions in the electrical grids. Due highly random nature of environmental conditions, prediction PV has limitations, particularly on long short periods. Thus, this research provides a new hybrid model for based fusing multi-frequency information different decomposition techniques that will allow forecaster provide reliable forecasts. We evaluate insights performance five multi-scale algorithms combined with deep convolution neural network (CNN). Additionally, we compare suggested combination approach's existing forecast models. An exhaustive assessment is carried out using three grid-connected plants Algeria total installed capacity 73.1 MW. The developed strategy displayed an outstanding performance. comparative analysis proposed method stand-alone other hybridization proves its superiority terms precision, RMSE varying range [0.454-1.54] studied stations.

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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Sustainable power management in light electric vehicles with hybrid energy storage and machine learning control

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

Published: March 7, 2024

Abstract This paper presents a cutting-edge Sustainable Power Management System for Light Electric Vehicles (LEVs) using Hybrid Energy Storage Solution (HESS) integrated with Machine Learning (ML)-enhanced control. The system's central feature is its ability to harness renewable energy sources, such as Photovoltaic (PV) panels and supercapacitors, which overcome traditional battery-dependent constraints. proposed control algorithm orchestrates power sharing among the battery, supercapacitor, PV optimizing utilization of available ensuring stringent voltage regulation DC bus. Notably, ML-based ensures precise torque speed regulation, resulting in significantly reduced ripple transient response times. In practical terms, system maintains bus within mere 2.7% deviation from nominal value under various operating conditions, substantial improvement over existing systems. Furthermore, supercapacitor excels at managing rapid variations load power, while battery adjusts smoothly meet demands. Simulation results confirm robust performance. HESS effectively stability, even most challenging conditions. Additionally, exceptionally robust, negligible steady-state fast also handles reversal commands efficiently, vital real-world applications. By showcasing these capabilities, lays groundwork more sustainable efficient future LEVs, suggesting pathways scalable advanced electric mobility solutions.

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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Advancing short-term solar irradiance forecasting accuracy through a hybrid deep learning approach with Bayesian optimization

Results in Engineering, Journal Year: 2024, Volume and Issue: 23, P. 102461 - 102461

Published: June 26, 2024

The optimization of solar energy integration into the power grid relies heavily on accurate forecasting irradiance. In this study, a new approach for short-term irradiance is introduced. This method combines Bayesian Optimized Attention-Dilated Long Short-Term Memory and Savitzky-Golay filtering. methodology implemented to analyze data obtained from probe situated in Douala, Cameroon. Initially, unprocessed augmented by integrating distinctive irradiation variables, filter with Optimization used enhance its quality. Subsequently, multiple deep learning models, including Memory, Bidirectional Artificial Neural Networks, Additive Attention Mechanism, Mechanism Dilated Convolutional layers, are trained evaluated. Out all models considered, proposed approach, which attention mechanism dilated convolutional demonstrates exceptional performance best convergence accuracy forecasting. further utilized fine-tune polynomial window size optimize hyperparameters models. results show Symmetric Mean Absolute Percentage Error 0.6564, Normalized Root Square 0.2250, 22.9445, surpassing previous studies literature. Empirical findings highlight effectiveness enhancing research contributes field introducing novel pre-processing techniques, hybrid architecture, development benchmark dataset. These advancements benefit both researchers plant managers, improving capabilities.

Language: Английский

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Optimization of off-grid hybrid renewable energy systems for cost-effective and reliable power supply in Gaita Selassie Ethiopia

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

Published: May 13, 2024

Abstract This paper explores scenarios for powering rural areas in Gaita Selassie with renewable energy plants, aiming to reduce system costs by optimizing component numbers meet demands. Various scenarios, such as combining solar photovoltaic (PV) pumped hydro-energy storage (PHES), utilizing wind PHES, and integrating a hybrid of PV, wind, have been evaluated based on diverse criteria, encompassing financial aspects reliability. To achieve the results, meta-heuristics Multiobjective Gray wolf optimization algorithm (MOGWO) Grasshopper (MOGOA) were applied using MATLAB software. Moreover, optimal sizing has investigated real-time assessment data meteorological from Sillasie, Ethiopia. Metaheuristic techniques employed pinpoint most favorable loss power supply probability (LPSP) least cost (COE) total life cycle (TLCC) system, all while meeting operational requirements various scenarios. The Multi-Objective Grey Wolf Optimization technique outperformed Algorithm problem, suggested results. Furthermore, MOGWO findings, PV-Wind-PHES demonstrated lowest COE (0.126€/kWh) TLCC (€6,897,300), along satisfaction village's demand LPSP value. In PV-Wind-PHSS scenario, are 38%, 18%, 2%, 1.5% lower than those Wind-PHS PV-PHSS at 0%, according Overall, this research contributes valuable insights into design implementation sustainable solutions remote communities, paving way enhanced access environmental sustainability.

Language: Английский

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Advanced efficient energy management strategy based on state machine control for multi-sources PV-PEMFC-batteries system

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

Published: April 5, 2024

Language: Английский

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Online stress monitoring during laser-directed energy deposition based on dynamic contour method

Virtual and Physical Prototyping, Journal Year: 2025, Volume and Issue: 20(1)

Published: Jan. 8, 2025

Deformation and cracking caused by internal stress have been a long-standing challenge in the field of metal additive manufacturing. This paper presents novel method for real-time assessment laser-directed energy deposition (LDED) based on shrinkage phenomenon layer – Dynamic Contour Method (DCM). It integrates machine vision, three-dimensional reconstruction actual morphology, numerical simulation to calculate rapidly development during LDED process. Meanwhile, mapping relationship between surface is established, providing theoretical basis DCM. Regarding validation this method, DCM simulations are compared with experimentally calibrated thermo-mechanical coupling simulations. The results show high degree consistency, demonstrating feasibility accuracy provides new digital twin framework

Language: Английский

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Multimodal dementia identification using lifestyle and brain lesions, a machine learning approach

AIP Advances, Journal Year: 2024, Volume and Issue: 14(6)

Published: June 1, 2024

Dementia diagnosis often relies on expensive and invasive neuroimaging techniques that limit access to early screening. This study proposes an innovative approach for facilitating dementia screening by estimating diffusion tensor imaging (DTI) measures using accessible lifestyle brain factors. Conventional DTI analysis, though effective, is hindered high costs limited accessibility. To address this challenge, fuzzy subtractive clustering identified 14 influential variables from the Lifestyle Brain Health Atrophy Lesion Index frameworks, encompassing demographics, medical conditions, factors, structural markers. A multilayer perceptron (MLP) neural network was developed these selected predict fractional anisotropy (FA), a metric reflecting white matter integrity cognitive function. The MLP model achieved promising results, with mean squared error of 0.000 878 test set FA prediction, demonstrating its potential accurate estimation without costly techniques. values in dataset ranged 0 1, higher indicating greater integrity. Thus, suggests model’s predictions were highly compared observed values. multifactorial aligns current understanding dementia’s complex etiology influenced various biological, environmental, By integrating readily available data into predictive model, method enables widespread, cost-effective risk assessment. proposed tool could facilitate timely interventions, preventive strategies, efficient resource allocation public health programs, ultimately improving patient outcomes caregiver burden.

Language: Английский

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Metaheuristic Algorithms for Solar Radiation Prediction: A Systematic Analysis

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 100134 - 100151

Published: Jan. 1, 2024

In the contemporary world, where escalating demand for energy and imperative sustainable sources, notably solar energy, have taken precedence, investigation into radiation (SR) has become indispensable. Characterized by its intermittency volatility, SR may experience considerable fluctuations, exerting a significant influence on supply security. Consequently, precise prediction of imperative, particularly in context potential proliferation photovoltaic panels need optimized management. Several works existing literature review state art prediction, focusing trends identified using machine learning (ML) or deep (DL) techniques. However, there is gap regarding integration optimization algorithms with ML DL techniques prediction. This systematic addresses this studying models that leverage metaheuristic alongside artificial intelligence (AI) techniques, aiming primarily maximum accuracy. Metaheuristic such as Particle Swarm Optimization (PSO) Genetic Algorithm (GA) featured 29% 12.1% analyzed articles, respectively, while intelligent approaches like Convolutional Neural Networks (CNN), Extreme Learning Machine (ELM), Multilayer Perceptron (MLP) emerged predominant choices, collectively accounting 43.9% studies. Analysis encompassed studies examining across hourly, daily, monthly intervals, daily intervals representing 48.7% focus. Noteworthy variables including temperature, humidity, wind speed, atmospheric pressure surfaced, capturing proportions 90%, 68.2%, 56%, 41.4%, within reviewed literature.

Language: Английский

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