Multi-step-ahead solar output time series prediction with gate recurrent unit neural network using data decomposition and cooperation search algorithm

Energy, Journal Year: 2022, Volume and Issue: 261, P. 125217 - 125217

Published: Aug. 23, 2022

Language: Английский

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Compressive Strength Prediction Using Coupled Deep Learning Model with Extreme Gradient Boosting Algorithm: Environmentally Friendly Concrete Incorporating Recycled Aggregate

Complexity, Journal Year: 2022, Volume and Issue: 2022(1)

Published: Jan. 1, 2022

The application of recycled aggregate as a sustainable material in construction projects is considered promising approach to decrease the carbon footprint concrete structures. Prediction compressive strength (CS) environmentally friendly (EF) containing important for understanding structures’ behaviour. In this research, capability deep learning neural network (DLNN) examined on simulation CS EF concrete. developed compared well‐known artificial intelligence (AI) approaches named multivariate adaptive regression spline (MARS), extreme machines (ELMs), and random forests (RFs). dataset was divided into three scenarios 70%‐30%, 80%‐20%, 90%‐10% training/testing explore impact data division percentage capacity AI model. Extreme gradient boosting (XGBoost) integrated with models select influencing variables prediction. Several statistical measures graphical methods were generated evaluate efficiency presented models. regard, results confirmed that DLNN model attained highest value prediction performance minimal root mean squared error (RMSE = 2.23). study revealed could be by increasing number problem using division. demonstrated robustness over other handling complex behaviour Due high accuracy model, method can used practical future use

Language: Английский

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VMD-GP: A New Evolutionary Explicit Model for Meteorological Drought Prediction at Ungauged Catchments

Water, Journal Year: 2023, Volume and Issue: 15(15), P. 2686 - 2686

Published: July 25, 2023

Meteorological drought is a common hydrological hazard that affects human life. It one of the significant factors leading to water and food scarcity. Early detection events necessary for sustainable agricultural resources management. For catchments with scarce meteorological observatory stations, lack observed data main cause unfeasible watershed management plans. However, various earth science environmental databases are available can be used studies, even at catchment scale. In this study, Global Drought Monitoring (GDM) repository provides real-time monthly Standardized Precipitation Evapotranspiration Index (SPEI) across globe was develop new explicit evolutionary model SPEI prediction ungauged catchments. The proposed model, called VMD-GP, uses an inverse distance weighting technique transfer GDM desired area. Then, variational mode decomposition (VMD), in conjunction state-of-the-art genetic programming, implemented map intrinsic functions GMD series subsequent values study suggested applied month-ahead Erbil, Iraq. results showed improvement accuracy over classic GP gene expression programming models developed as benchmarks.

Language: Английский

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Data-Based Solar Radiation Forecasting with Pre-Processing Using Variational Mode Decomposition

2022 8th International Conference on Control, Decision and Information Technologies (CoDIT), Journal Year: 2023, Volume and Issue: unknown, P. 2061 - 2066

Published: July 3, 2023

This paper presents a hybrid method for accurately predicting Global Horizontal Irradiance (GHI) over the following 24 hours to forecast energy production from photo-voltaic system in positive building. The input data is preprocessed using Variational Mode Decomposition (VMD) extract wide-bandwidth features and decompose them into smooth modes focused on specific frequency ranges. Salp Swarm Algorithm (SSA) utilized identify optimal VMD parameters accurate extraction. analysis employed most critical of features. model's efficiency further enhanced by performing residual preprocessing step between observed solar radiance decomposed modes. Stacking technique (ST) predict 24-hour GHI residual, which are summed reconstruct final signal. proposed method's performance evaluated Normalized Root Mean Square Error (NRMSE) Absolute (NMAE) metrics three years available (2019–2022) Rabat, compared with model based raw data. results show that achieved promising an NRMSE 1.35% NMAE 0.82% cloudy day.

Language: Английский

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Daily global solar radiation time series prediction using variational mode decomposition combined with multi-functional recurrent fuzzy neural network and quantile regression forests algorithm

Energy Reports, Journal Year: 2023, Volume and Issue: 10, P. 4198 - 4217

Published: Nov. 1, 2023

Global solar radiation (GSR) prediction capability with a reliable model and high accuracy is crucial for comprehending hydrological meteorological systems. It vital the production of renewable clean energy. This research aims to evaluate performance combined variational mode decomposition (VMD) multi-functional recurrent fuzzy neural network (MFRFNN) quantile regression forests (QRF) models GSR in daily scales. The hybrid VMD-MFRFNN QRF were compared standalone MFRFNN, random forest (RF), extreme gradient boosting (XGB), M5 tree (M5T) across Lund Växjö stations Sweden. data from 2008 2017 used train models, while was verified by using 2018 2021 under five different input combinations. various meteorological-based scenarios (including are air temperatures (Tmin, Tmax, T), wind speed (WS), relative humidity (RH), sunshine duration (SSH), maximum possible (N)) considered as predictor models. current study resulted that M5T exhibited higher than RF XGB showed equivalent at both sites. MFRFNN outperformed all combinations best when fewer variables T, WS station Tmin, WS, SSH, RH station) prediction. We conclude predicts average combining RH, N).

Language: Английский

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