Hierarchical reconciliation of convolutional gated recurrent units for unified forecasting of branched and aggregated district heating loads DOI

Xinyi Li,

Shitong Wang,

Zhiqiang Chen

et al.

Energy, Journal Year: 2024, Volume and Issue: unknown, P. 134097 - 134097

Published: Dec. 1, 2024

Language: Английский

Evaluating the Performance of Machine Learning Models for Energy Load Prediction in Residential HVAC Systems DOI

Paul Boadu Asamoah,

Ekundayo Shittu

Energy and Buildings, Journal Year: 2025, Volume and Issue: unknown, P. 115517 - 115517

Published: Feb. 1, 2025

Language: Английский

Citations

0
Predictive Analytics for Sustainable Energy: An In-depth Assessment of Novel Stacking Regressor Model in the Off-Grid Hybrid Renewable Energy Systems DOI

Yangbing Zheng,

Xu Zhou, Jiantao Yu

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135916 - 135916

Published: April 1, 2025

Language: Английский

Citations

0
Enhancing the safety of hydroelectric power generation systems: an intelligent identification of axis orbits based on a nonlinear dynamics method DOI
Fei Chen, Jie Liu, Xiaoxi Hu

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135864 - 135864

Published: April 1, 2025

Language: Английский

Citations

0
Building Energy Optimization Using an Improved Exponential Distribution Optimizer Based on Golden Sine Strategy Minimizing Energy Consumption Under Uncertainty DOI Creative Commons

Mohammad Ali Karbasforoushha,

Mohammad Khajehzadeh, Suraparb Keawsawasvong

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104840 - 104840

Published: April 1, 2025

Language: Английский

Citations

0
Enhancing Smart Home Efficiency with Heuristic-Based Energy Optimization DOI Creative Commons

Yasir Khan,

Faris Kateb, Ateeq Ur Rehman

et al.

Computers, Journal Year: 2025, Volume and Issue: 14(4), P. 149 - 149

Published: April 16, 2025

In smart homes, heavy reliance on appliance automation has increased, along with the energy demand in developing urban areas, making efficient management an important factor. To address scheduling of appliances under Demand-Side Management, this article explores use heuristic-based optimization techniques (HOTs) homes (SHs) equipped renewable and sustainable resources (RSERs) storage systems (ESSs). The optimal model for minimization peak-to-average ratio (PAR), considering user comfort constraints, is validated by using different techniques, such as Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO), Wind-Driven (WDO), Bacterial Foraging (BFO) Modified (GmPSO) algorithm, to minimize electricity costs, PAR, carbon emissions delay discomfort. This research investigates results three real-world scenarios. scenarios demonstrate benefits gradually assembling RSERs ESSs integrating them into SHs employing HOTs. simulation show substantial outcomes, scenario Condition 1, GmPSO decreased from 300 kg 69.23 kg, reducing 76.9%; bill prices were also cut unplanned value 400.00 cents 150 cents, a 62.5% reduction. PAR was unscheduled 4.5 2.2 which reduced 51.1%. 2 showed that 0.5 (unscheduled) 0.2, 60% reduction; costs 500.00 200.00 250.00 reduction GmPSO. 3, where batteries integrated, algorithm emission 158.3 208.3 24%. cost 500 GmPSO, decreasing overall 40%. achieved 57.1% 2.8 1.2.

Language: Английский

Citations

0
Energy Use Intensity Analysis of Office Buildings Using Green BIM-Integrated Interpretable Machine Learning DOI
Ngoc‐Mai Nguyen, Minh-Tu Cao

Journal of Building Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 112760 - 112760

Published: April 1, 2025

Language: Английский

Citations

0
Development and application of an intelligent nitrogen removal diagnosis and optimization framework for WWTPs: Low-carbon and stable operation DOI

Zhichi Chen,

Hong Cheng,

X P Wang

et al.

Water Research, Journal Year: 2024, Volume and Issue: 266, P. 122337 - 122337

Published: Aug. 30, 2024

Language: Английский

Citations

3
Adaptive Difference Least Squares Support Vector Regression for Urban Road Collapse Timing Prediction DOI Open Access

Yafang Han,

Limin Quan, Yanchun Liu

et al.

Symmetry, Journal Year: 2024, Volume and Issue: 16(8), P. 977 - 977

Published: Aug. 1, 2024

The accurate prediction of urban road collapses is paramount importance for public safety and infrastructure management. However, the complex variable nature subsidence mechanisms, coupled with inherent noise non-stationarity in data, poses significant challenges to development precise real-time models. To address these challenges, this paper develops an Adaptive Difference Least Squares Support Vector Regression (AD-LSSVR) model. AD-LSSVR model employs a difference transformation process input output effectively reducing enhancing stability. This extracts trends features from leveraging symmetrical characteristics within it. Additionally, parameters were optimized using grid search cross-validation techniques, which systematically explore parameter space evaluate performance multiple subsets ensuring both precision generalizability selected parameters. Moreover, sliding window method was employed data sparsity anomalies, robustness adaptability experimental results demonstrate superior predicting collapse timing, highlighting its effectiveness handling nonlinear data.

Language: Английский

Citations

1
Experimental studies on the cooling and heating performance of a highly emissive coating DOI Creative Commons
Zhuo Yang,

Zhangran Yang,

Zihan Zhang

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(19), P. e38233 - e38233

Published: Sept. 21, 2024

Language: Английский

Citations

1
Developing an integrated prediction model for daylighting, thermal comfort, and energy consumption in residential buildings based on the stacking ensemble learning algorithm DOI
Hainan Yan,

Guohua Ji,

Shuqi Cao

et al.

Building Simulation, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 10, 2024

Language: Английский

Citations

0