Applying machine learning for biomass gasification prediction: enhancing efficiency and sustainability DOI

C. H. Tai,

Shasha Xiong

Chemical Product and Process Modeling, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 30, 2024

Abstract In the contemporary era, marked by increasing significance of sustainable energy sources, biomass gasification emerges as a highly promising technology for converting organic materials into valuable fuel, offering an environmentally friendly approach that not only mitigates waste but also addresses growing demands. However, effectiveness is intricately tied to its predictability and efficiency, presenting substantial challenge in achieving optimal operational parameters this complex process. It at precise juncture machine learning assumes pivotal role, initiating transformative paradigm shift gasification. This article delves convergence prediction introduces two innovative hybrid models amalgamate Support Vector Regression (SVR) algorithm with Coot Optimization Algorithm (COA) Walrus (WaOA). These harness nearby data forecast elemental compositions CH 4 C 2 H n , thereby enhancing precision practicality predictions, potential solutions intricate challenges within domain. The SVWO model (SVR optimized WaOA) effective tool predicting these compositions. exhibited outstanding performance notable R values 0.992 0.994 emphasizing exceptional accuracy. Additionally, minimal RMSE 0.317 0.136 underscore SVWO. accuracy SVWO’s predictions affirms suitability practical, real-world applications.

Language: Английский

Predicting Daily Heating Energy Consumption in Residential Buildings through Integration of Random Forest Model and Meta-Heuristic Algorithms DOI

Weiyan Xu,

Jielei Tu,

Ning Xu

et al.

Energy, Journal Year: 2024, Volume and Issue: 301, P. 131726 - 131726

Published: May 20, 2024

Language: Английский

Citations

20
Enhancing residential heating load prediction with advanced machine learning and optimization techniques DOI
Milad Mohebbi, Sadegh Afzal

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: 95, P. 110199 - 110199

Published: July 14, 2024

Language: Английский

Citations

6
Enhancing Building Energy Efficiency: An Integrated Approach to Predicting Heating and Cooling Loads using Machine Learning and Optimization Algorithms DOI

Tianfei Gao,

Xu Han, J. Wang

et al.

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 110759 - 110759

Published: Sept. 1, 2024

Language: Английский

Citations

6
An Ensemble Model for the Energy Consumption Prediction of Residential Buildings DOI

Ritwik Mohan,

Nikhil Pachauri

Energy, Journal Year: 2024, Volume and Issue: unknown, P. 134255 - 134255

Published: Dec. 1, 2024

Language: Английский

Citations

4
Developing boosting-based estimation models for the ultimate strength of shear connectors in bridge DOI

Guihai Gao,

Jianxu Chen,

Xiong Mei

et al.

Mechanics Based Design of Structures and Machines, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 26

Published: March 8, 2025

Language: Английский

Citations

0
Building heating energy demand estimated by random forest model in individual and hybrid approaches DOI

Huijiao Nie

Journal of Ambient Intelligence and Humanized Computing, Journal Year: 2025, Volume and Issue: unknown

Published: April 10, 2025

Language: Английский

Citations

0
Random Forest model for precise cooling load estimation in optimized and non-optimized form DOI
Lei Wang,

Hongmei Gu,

Qingqing Zhang

et al.

Chemical Product and Process Modeling, Journal Year: 2025, Volume and Issue: unknown

Published: April 15, 2025

Abstract Energy is vital for life and human development, with global warming due to activities such as the combustion of fossil fuels deforestation emitting dangerous greenhouse gases, changing climate Earth. Global energy demand increasing, developed nations viewing buildings major consumers. Due long lifespan buildings, it important evaluate their suitability future change possible changes in consumption. Appraisal cooling loads each building now required rising costs need reduce impacts caused by consumption from buildings. This paper aims apply Random Forest Regression (RF) Support Vector (SVR), well-known machine learning algorithms predict loads. It utilizes Jellyfish Search Optimizer (JSO) Transit Optimization Algorithm (TSOA) enhance accuracy minimize overall error Cooling Load (CL) estimation. The investigation suggests two high-performance schemes, applies optimizers hybrid an ensemble approach accurate appraisal . Moreover, SHAP method utilized compare effectiveness parameters. research proves be insightful constructing CL projection that a RFJS-based model most effective way optimize attained R 2 0.994 at its best RMSE 0.744. Other than this, following was RSJS, whose were 0.989 0.985, accordingly. third best-performing SVJS values 0.972 1.583,

Language: Английский

Citations

0
An advanced hybrid deep learning model for accurate energy load prediction in smart building DOI Creative Commons

R. Sunder,

R Sreeraj,

Vince Paul

et al.

Energy Exploration & Exploitation, Journal Year: 2024, Volume and Issue: 42(6), P. 2241 - 2269

Published: Aug. 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

Language: Английский

Citations

3
Design of iron-based metal-organic framework (Fe-MOF) and molybdenum telluride (MoTe2) nanohybrids for enhanced energy storage and hydrogen evolution reactions DOI
Hasan B. Albargi, Adnan Abbas, Muhammad Zeeshan

et al.

Inorganic Chemistry Communications, Journal Year: 2024, Volume and Issue: unknown, P. 113791 - 113791

Published: Dec. 1, 2024

Language: Английский

Citations

2
Enhanced Ensemble Learning-Based Uncertainty and Sensitivity Analysis of Ventilation Rate in a Novel Radiative Cooling Building DOI Creative Commons

Majid Mohsenpour,

Mohsen Salimi,

A. Kermani

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 11(1), P. e41572 - e41572

Published: Dec. 31, 2024

Language: Английский

Citations

2