Time-series generative adversarial networks for flood forecasting

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 622, P. 129702 - 129702

Published: May 20, 2023

Language: Английский

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Short-term stochastic multi-objective optimization scheduling of wind-solar-hydro hybrid system considering source-load uncertainties

Applied Energy, Journal Year: 2024, Volume and Issue: 372, P. 123781 - 123781

Published: June 27, 2024

Language: Английский

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Few-Shot Load Forecasting Under Data Scarcity in Smart Grids: A Meta-Learning Approach

Energies, Journal Year: 2025, Volume and Issue: 18(3), P. 742 - 742

Published: Feb. 6, 2025

Despite the rapid expansion of smart grids and large volumes data at individual consumer level, there are still various cases where adequate collection to train accurate load forecasting models is challenging or even impossible. This paper proposes adapting an established Model-Agnostic Meta-Learning algorithm for short-term in context few-shot learning. Specifically, proposed method can rapidly adapt generalize within any unknown time series arbitrary length using only minimal training samples. In this context, meta-learning model learns optimal set initial parameters a base-level learner recurrent neural network. The evaluated dataset historical consumption from real-world consumers. examined series’ short length, it produces forecasts outperforming transfer learning task-specific machine methods by 12.5%. To enhance robustness fairness during evaluation, novel metric, mean average log percentage error, that alleviates bias introduced commonly used MAPE metric. Finally, studies evaluate model’s under different hyperparameters lengths also conducted, demonstrating approach consistently outperforms all other models.

Language: Английский

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Dynamic thermal environment management technologies for data center: A review

Renewable and Sustainable Energy Reviews, Journal Year: 2023, Volume and Issue: 187, P. 113761 - 113761

Published: Sept. 22, 2023

Language: Английский

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Extended Application of Inertial Measurement Units in Biomechanics: From Activity Recognition to Force Estimation

Sensors, Journal Year: 2023, Volume and Issue: 23(9), P. 4229 - 4229

Published: April 24, 2023

Abnormal posture or movement is generally the indicator of musculoskeletal injuries diseases. Mechanical forces dominate injury and recovery processes tissue. Using kinematic data collected from wearable sensors (notably IMUs) as input, activity recognition force (typically represented by ground reaction force, joint force/torque, muscle activity/force) estimation approaches based on machine learning models have demonstrated their superior accuracy. The purpose present study to summarize recent achievements in application IMUs biomechanics, with an emphasis mechanical estimation. methodology adopted such applications, including pre-processing, noise suppression, classification models, force/torque corresponding effects, are reviewed. extent applications daily assessment, disease diagnosis, rehabilitation, exoskeleton control strategy development illustrated discussed. More importantly, technical feasibility opportunities prediction using IMU-based devices indicated highlighted. With novel adaptive networks deep accurate can become a research field worthy further attention.

Language: Английский

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Rapid accomplishment of cost-effective and macro-defect-free LPBF-processed Ti parts based on deep data augmentation

Journal of Manufacturing Processes, Journal Year: 2024, Volume and Issue: 120, P. 1023 - 1034

Published: May 9, 2024

Language: Английский

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Limited data-oriented building heating load prediction method: A novel meta learning-based framework

Energy and Buildings, Journal Year: 2024, Volume and Issue: 308, P. 114027 - 114027

Published: Feb. 21, 2024

Language: Английский

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Crop water productivity assessment and planting structure optimization in typical arid irrigation district using dynamic Bayesian network

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

Published: July 31, 2024

Enhancing crop water productivity is crucial for regional resource management and agricultural sustainability, particularly in arid regions. However, evaluating the spatial heterogeneity temporal dynamics of face data limitations poses a challenge. In this study, we propose framework that integrates remote sensing data, time series generative adversarial network (TimeGAN), dynamic Bayesian (DBN), optimization model to assess optimize planting structure under limited resources allocation Qira oasis. The results demonstrate combination TimeGAN DBN better improves accuracy prediction, short-term predictions with 4 years as optimal timescale (R2 > 0.8). Based on distribution suitability analysis, wheat corn are most suitable cultivation central eastern parts oasis while cotton unsuitable western region. walnuts Chinese dates mainly southeastern part Maximizing ensuring food security has led increased acreage cotton, walnuts. Under combined action five objectives, average increase 14.97%, ecological benefit 3.61%, which much higher than growth rate irrigation consumption cultivated land. It will produce relatively reduced requirement land improved productivity. This proposed can serve an effective reference tool decision-makers when determining future cropping plans.

Language: Английский

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Geohash coding-powered deep learning network for vessel trajectory prediction using clustered AIS data in maritime Internet of Things industries

Computers & Electrical Engineering, Journal Year: 2024, Volume and Issue: 120, P. 109611 - 109611

Published: Sept. 19, 2024

Language: Английский

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A data transfer method based on one dimensional convolutional neural network for cross-building load prediction

Energy, Journal Year: 2023, Volume and Issue: 277, P. 127645 - 127645

Published: April 25, 2023

Language: Английский

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