Artificial intelligence-driven prediction models for the cultivation of Chlorella vulgaris FSP-E in food waste culture medium: A comparative analysis and validation of models

Algal Research, Journal Year: 2025, Volume and Issue: unknown, P. 103935 - 103935

Published: Jan. 1, 2025

Language: Английский

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Integration of LSTM networks with gradient boosting machines (GBM) for assessing heating and cooling load requirements in building energy efficiency

Energy Exploration & Exploitation, Journal Year: 2024, Volume and Issue: 42(6), P. 2191 - 2217

Published: Aug. 2, 2024

Due to rising demand for energy-efficient buildings, advanced predictive models are needed evaluate heating and cooling load requirements. This research presents a unified strategy that blends LSTM networks GBM improve building energy estimates’ precision reliability. Data on usage, weather conditions, occupancy trends, features is collected prepared start the process. model attributes created using sequential relationships initial projections networks. Combining with takes advantage of each model's strengths: LSTM's data processing GBM's complex nonlinear connection capture. Performance measures like RMSE MAE used hybrid validity. Compared individual models, integrated LSTM-GBM method improves prediction accuracy. higher capacity allows real-time management systems, improving operations reducing use. Implementing this in Building Management Systems (BMS) shows its practicality achieving sustainable efficiency.

Language: Английский

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Machine learning-based prediction model for the yield of nitrogen-enriched biomass pyrolysis products: Performance evaluation and interpretability analysis

Journal of Analytical and Applied Pyrolysis, Journal Year: 2024, Volume and Issue: 182, P. 106723 - 106723

Published: Aug. 28, 2024

Language: Английский

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AI-Driven Design Optimization for Sustainable Buildings: A Systematic Review

Energy and Buildings, Journal Year: 2025, Volume and Issue: unknown, P. 115440 - 115440

Published: Feb. 1, 2025

Language: Английский

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Generative adversarial network approach for predicting tensile behavior and failure pattern of fiber-reinforced cementitious matrices

Engineering Structures, Journal Year: 2025, Volume and Issue: 335, P. 120276 - 120276

Published: April 15, 2025

Language: Английский

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Improving computer numerical control (CNC) turning performance of AISI D2 steel with nanofluid composites and advanced machine learning techniques

The International Journal of Advanced Manufacturing Technology, Journal Year: 2025, Volume and Issue: unknown

Published: April 21, 2025

Language: Английский

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Machine-Learning-Enhanced Building Performance-Guided Form Optimization of High-Rise Office Buildings in China’s Hot Summer and Warm Winter Zone—A Case Study of Guangzhou

Sustainability, Journal Year: 2025, Volume and Issue: 17(9), P. 4090 - 4090

Published: May 1, 2025

Given their dominant role in energy expenditure within China’s Hot Summer and Warm Winter (HSWW) zone, high-fidelity performance prediction multi-objective optimization framework during the early design phase are critical for achieving sustainable efficiency. This study presents an innovative approach integrating machine learning (ML) algorithms genetic to predict optimize of high-rise office buildings HSWW zone. By Rhino/Grasshopper parametric modeling, Ladybug Tools simulation, Python programming, this developed a building model validated five advanced mature predicting use intensity (EUI) useful daylight illuminance (UDI) based on architectural form parameters under climatic conditions. The results demonstrate that CatBoost algorithm outperforms other models with R2 0.94 CVRMSE 1.57%. Pareto optimal solutions identify substantial shading dimensions, southeast orientations, high aspect ratios, appropriate spatial depths, reduced window areas as determinants optimizing EUI UDI research fills gap existing literature by systematically investigating application ML complex relationships between metrics design. proposed data-driven provides architects engineers scientific decision-making tool early-stage design, offering methodological guidance similar regions.

Language: Английский

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Skip or not: Hybrid machine learning for decision support in strategic port-skipping behavior to enhance liner shipping reliability

Ocean Engineering, Journal Year: 2025, Volume and Issue: 324, P. 120730 - 120730

Published: Feb. 22, 2025

Language: Английский

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A framework for anomaly classification in Industrial Internet of Things systems

Internet of Things, Journal Year: 2024, Volume and Issue: unknown, P. 101446 - 101446

Published: Nov. 1, 2024

Language: Английский

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A Systematic Optimization Method for Permanent Magnet Synchronous Motors Based on SMS-EMOA

Sensors, Journal Year: 2024, Volume and Issue: 24(9), P. 2956 - 2956

Published: May 6, 2024

The efficient design of Permanent Magnet Synchronous Motors (PMSMs) is crucial for their operational performance. A key parameter, cogging torque, significantly influenced by various structural parameters the motor, complicating optimization motor structures. This paper proposes an method PMSM structures based on heuristic algorithms, named Motor Self-Optimization Lift Algorithm (PMSM-SLA). Initially, a dataset capturing efficiency motors under parameter scenarios created using finite element simulation methods. Building this dataset, batch solution aimed at structure was introduced to identify set that maximize efficiency. approach presented in study enhances optimizing structures, overcoming limitations traditional trial-and-error methods and supporting industrial application design.

Language: Английский

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