Bionic fusion perspective: Audiovisual-motivated integration network for solar irradiance prediction

Energy Conversion and Management, Год журнала: 2024, Номер 314, С. 118726 - 118726

Опубликована: Июнь 27, 2024

Язык: Английский

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An advanced hybrid deep learning model for accurate energy load prediction in smart building

Energy Exploration & Exploitation, Год журнала: 2024, Номер 42(6), С. 2241 - 2269

Опубликована: Авг. 27, 2024

In smart cities, sustainable development depends on energy load prediction since it directs utilities in effectively planning, distributing and generating energy. This work presents a novel hybrid deep learning model including components of the Improved-convolutional neural network (CNN), bidirectional long short-term memory (Bi-LSTM), Graph (GNN), Transformer Fusion Layer architectures for precise forecasting. Better feature extraction results from Improved-CNN's dilated convolution residual block accommodation wide receptive fields reduced vanishing gradient problem. By capturing temporal links both directions, Bi-LSTM networks help to better grasp complicated use patterns. improve predictive capacities across linked systems by characterizing spatial relationships between energy-consuming units cities. Emphasizing critical trends guarantee reliable forecasts, transformer models attention methods manage long-term dependencies consumption data. Combining CNN, Bi-LSTM, GNN component predictions synthesizes numerous data representations increase accuracy. With Root Mean Square Error 5.7532 Wh, Absolute Percentage 3.5001%, 6.7532 Wh R 2 0.9701, fared than other ‘Electric Power Consumption’ Kaggle dataset. develops realistic that helps informed decision-making enhances efficiency techniques, promoting forecasting

Язык: Английский

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CRAformer: a cross-residual attention transformer for solar irradiation multistep forecasting

Energy, Год журнала: 2025, Номер unknown, С. 135214 - 135214

Опубликована: Фев. 1, 2025

Язык: Английский

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A Comprehensive Analysis of Bitcoin Volatility Forecasting Using Time-series Econometric Models

Applied Soft Computing, Год журнала: 2025, Номер unknown, С. 113339 - 113339

Опубликована: Май 1, 2025

Язык: Английский

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A novel deep learning framework with artificial protozoa optimization-based adaptive environmental response for wind power prediction

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Май 28, 2025

Язык: Английский

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Forecasting mooring tension of offshore platforms based on complete ensemble empirical mode decomposition with adaptive noise and deep learning network

Measurement, Год журнала: 2024, Номер 239, С. 115515 - 115515

Опубликована: Авг. 15, 2024

Язык: Английский

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A noise resilient multi-step ahead deep learning forecasting technique for solar energy centered generation of green hydrogen

International Journal of Hydrogen Energy, Год журнала: 2024, Номер 90, С. 666 - 679

Опубликована: Окт. 8, 2024

Язык: Английский

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An effective multi-time series model of RC column backbone curve identification

Case Studies in Construction Materials, Год журнала: 2024, Номер 20, С. e03183 - e03183

Опубликована: Апрель 21, 2024

Accurate identification of the backbone curves reinforced concrete (RC) columns is key to engineering design and strengthening renovation. In view problems high cost, long time, low accuracy, large dispersion calculation results discontinuous stiffness changes existing curve methods, such as experimental method, finite element simulation method semi-theoretical semi-empirical it proposed transform problem into a multi-time series prediction problem. By introducing attention mechanism combining with bidirectional short-term memory (BiLSTM), model (BC-ABiLSTM) established considering relationship between front back points curves. Compared models for BiLSTM (BC-BiLSTM), (BC-LSTM), multilayer perceptron (BC-MLP), performance BC-ABiLSTM better, mean absolute error (MAE), percentage (MAPE), root square (RMSE), R2 on testing set are 12.492kN, 10.595%, 20.838kN 0.9924, respectively, which provides new accurate, efficient cost-effective RC column under various cyclic loading levels.

Язык: Английский

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Artificial intelligence modeling for power system planning

Electrical Engineering, Год журнала: 2024, Номер unknown

Опубликована: Авг. 11, 2024

Язык: Английский

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Explainable AI and optimized solar power generation forecasting model based on environmental conditions

PLoS ONE, Год журнала: 2024, Номер 19(10), С. e0308002 - e0308002

Опубликована: Окт. 2, 2024

This paper proposes a model called X-LSTM-EO, which integrates explainable artificial intelligence (XAI), long short-term memory (LSTM), and equilibrium optimizer (EO) to reliably forecast solar power generation. The LSTM component forecasts generation rates based on environmental conditions, while the EO optimizes model’s hyper-parameters through training. XAI-based Local Interpretable Model-independent Explanation (LIME) is adapted identify critical factors that influence accuracy of in smart systems. effectiveness proposed X-LSTM-EO evaluated use five metrics; R-squared (R 2 ), root mean square error (RMSE), coefficient variation (COV), absolute (MAE), efficiency (EC). gains values 0.99, 0.46, 0.35, 0.229, 0.95, for R , RMSE, COV, MAE, EC respectively. results this improve performance original conventional LSTM, where improvement rate is; 148%, 21%, 27%, 20%, 134% compared with other machine learning algorithm such as Decision tree (DT), Linear regression (LR) Gradient Boosting. It was shown worked better than DT LR when were compared. Additionally, PSO employed instead validate outcomes, further demonstrated efficacy optimizer. experimental simulations demonstrate can accurately estimate PV response abrupt changes patterns. Moreover, might assist optimizing operations photovoltaic units. implemented utilizing TensorFlow Keras within Google Collab environment.

Язык: Английский

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