Read-First LSTM model: A new variant of long short term memory neural network for predicting solar radiation data

Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 305, P. 118267 - 118267

Published: March 7, 2024

Language: Английский

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Forecasting Solar Photovoltaic Power Production: A Comprehensive Review and Innovative Data-Driven Modeling Framework

Energies, Journal Year: 2024, Volume and Issue: 17(16), P. 4145 - 4145

Published: Aug. 20, 2024

The intermittent and stochastic nature of Renewable Energy Sources (RESs) necessitates accurate power production prediction for effective scheduling grid management. This paper presents a comprehensive review conducted with reference to pioneering, comprehensive, data-driven framework proposed solar Photovoltaic (PV) generation prediction. systematic integrating comprises three main phases carried out by seven modules addressing numerous practical difficulties the task: phase I handles aspects related data acquisition (module 1) manipulation 2) in preparation development scheme; II tackles associated model 3) assessment its accuracy 4), including quantification uncertainty 5); III evolves towards enhancing incorporating context change detection 6) incremental learning when new become available 7). adeptly addresses all facets PV prediction, bridging existing gaps offering solution inherent challenges. By seamlessly these elements, our approach stands as robust versatile tool precision real-world applications.

Language: Английский

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Prediction of submarine soil dredging difficulty scale in cutter suction dredger construction with clustering-based deep learning

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 147, P. 110370 - 110370

Published: Feb. 27, 2025

Language: Английский

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Evaluation of Solar Radiation Prediction Models Using AI: A Performance Comparison in the High-Potential Region of Konya, Türkiye

Atmosphere, Journal Year: 2025, Volume and Issue: 16(4), P. 398 - 398

Published: March 30, 2025

Solar radiation is one of the most abundant energy sources in world and a crucial parameter that must be researched developed for sustainable projects future generations. This study evaluates performance different machine learning methods solar prediction Konya, Turkey, region with high potential. The analysis based on hydro-meteorological data collected from NASA/POWER, covering period 1 January 1984 to 31 December 2022. compares Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), GRU (Bi-GRU), LSBoost, XGBoost, Bagging, Random Forest (RF), General Regression Neural Network (GRNN), Support Vector Machines (SVM), Artificial Networks (MLANN, RBANN). variables used include temperature, relative humidity, precipitation, wind speed, while target variable radiation. dataset was divided into 75% training 25% testing. Performance evaluations were conducted using Mean Absolute Error (MAE), Root Square (RMSE), coefficient determination (R2). results indicate Bi-LSTM models performed best test phase, demonstrating superiority deep learning-based approaches prediction.

Language: Английский

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Hybrid solar radiation forecasting model with temporal convolutional network using data decomposition and improved artificial ecosystem-based optimization algorithm

Energy, Journal Year: 2023, Volume and Issue: 280, P. 128171 - 128171

Published: June 18, 2023

Language: Английский

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A robust real-time energy scheduling strategy of integrated energy system based on multi-step interval prediction of uncertainties

Energy, Journal Year: 2024, Volume and Issue: 300, P. 131639 - 131639

Published: May 13, 2024

Language: Английский

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Energy market trading in green microgrids under information vulnerability of renewable energies: A data-driven approach

Energy Reports, Journal Year: 2024, Volume and Issue: 11, P. 4467 - 4484

Published: April 21, 2024

Language: Английский

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Designing a reliable machine learning system for accurately estimating the ultimate condition of FRP-confined concrete

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

Published: Sept. 3, 2024

Language: Английский

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Precipitation variability using GPCC data and its relationship with atmospheric teleconnections in Northeast Brazil

Climate Dynamics, Journal Year: 2023, Volume and Issue: 61(11-12), P. 5035 - 5048

Published: June 4, 2023

Abstract The present study investigates the influence of different atmospheric teleconnections on annual precipitation variability in Northeast Brazil (NEB) based data from Global Precipitation Climatology Center (GPCC) 1901 to 2013. objective this is analyze total NEB for 1901–2013 period, considering physical characteristics four subregions, i.e., Mid-north, Backwoods, Agreste, and Forest zone. To teleconnections, GPCC were used, behavior was assessed using Pearson correlation coefficient, Rainfall Anomaly Index (RAI), cross-wavelet analysis. used studied region. RAI calculate frequency patterns drought episodes. analysis applied identify similarity signals between series teleconnections. results according Student's t test showed that Atlantic Multidecadal Oscillation (AMO) exerts a more significant Backwoods region at an interannual scale. In contrast, Pacific Decadal (PDO) greater control over modulation climatic NEB. are insightful reveal differential impacts such as AMO, PDO, MEI, NAO sub-regions circulation strongly interdecadal Mid-north regions, possibly associated with Intertropical Convergence Zone (ITCZ) position. Finally, contributes understanding internal planning water resources agricultural activities Graphic abstract

Language: Английский

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A New Insight for Daily Solar Radiation Prediction by Meteorological Data Using an Advanced Artificial Intelligence Algorithm: Deep Extreme Learning Machine Integrated with Variational Mode Decomposition Technique

Sustainability, Journal Year: 2023, Volume and Issue: 15(14), P. 11275 - 11275

Published: July 19, 2023

Reliable and precise estimation of solar energy as one the green, clean, renewable inexhaustible types energies can play a vital role in management, especially developing countries. Also, has less impact on earth’s atmosphere environment help to lessen negative effects climate change by lowering level emissions greenhouse gas. This study developed thirteen different artificial intelligence models, including multivariate adaptive regression splines (MARS), extreme learning machine (ELM), Kernel (KELM), online sequential (OSELM), optimally pruned (OPELM), outlier robust (ORELM), deep (DELM), their versions combined with variational mode decomposition (VMD) integrated models (VMD-DELM, VMD-ORELM, VMD-OPELM, VMD-OSELM, VMD-KELM, VMD-ELM), for radiation Kurdistan region, Iraq. The daily meteorological data from 2017 2018 were used implement suggested at Darbandikhan Dukan stations, input parameters included maximum temperature (MAXTEMP), minimum (MINTEMP), relative humidity (MAXRH), (MINRH), sunshine duration (SUNDUR), wind speed (WINSPD), evaporation (EVAP), cloud cover (CLOUDCOV). results show that proposed VMD-DELM algorithm considerably enhanced simulation accuracy standalone models’ prediction, average improvement terms RMSE 13.3%, 20.36%, 25.1%, 27.1%, 34.17%, 38.64%, 48.25% station 5.22%, 10.01%, 10.26%, 21.01%, 29.7%, 35.8%, 40.33% station, respectively. outcomes this reveal two-stage model performed superiorly other approaches predicting considering climatic predictors both stations.

Language: Английский

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