Optimizing Energy Efficiency and Sustainability: Building Energy Consumption Prediction with Broad Learning System and Alternating Direction Method of Multipliers DOI
Muideen Adegoke, Hafiz Alaka,

Saheed Ajayi

et al.

Published: Jan. 1, 2023

The importance of energy efficiency in buildings, with their substantial consumption and environmental impact, underscores the need for highly accurate use prediction models. These models are crucial informed decisions, sustainable living, eco-friendly practices.However, most studies rarely explore machine learning to predict building early design phase constructing energy-efficient buildings. Similarly, many classical limited datasets, risking poor generalization. To address these challenges, this paper proposes an alternating direction method multiplier based broad system algorithm (ADMM-BLS) annual prediction. First, we construct a novel objective function. Based on developed function, develop ADMM (BLS) compute optimal output weight decision making proposed ADMM-BLS contains feature-mapped nodes, enhancement weights, algorithm. We nodes feature extraction further improve features. function is designed guide model enhance decision-making. Also, utilized optimize thus enhancing decision-making.Several experiments performed using large real-life data set from residential buildings capture various types sizes. performances evaluated well-known quality metrics namely root mean square error (RMSE), absolute (MAE), coefficient determination (R2) (MSE). nine other widely used algorithms tasks implemented. results show that efficient predictive before construction study. This capability will enable decisions related practices, optimized construction.

Language: Английский

Predictive models of embodied carbon emissions in building design phases: Machine learning approaches based on residential buildings in China DOI
Xiaocun Zhang, Hailiang Chen, Jiayue Sun

et al.

Building and Environment, Journal Year: 2024, Volume and Issue: 258, P. 111595 - 111595

Published: April 29, 2024

Language: Английский

Citations

13
Data-Driven Insights into Climate Change Effects on Groundwater Levels Using Machine Learning DOI
Xueqiang Lu,

Zhewen Wang,

Menghao Zhao

et al.

Water Resources Management, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 27, 2025

Language: Английский

Citations

1
A GA-stacking ensemble approach for forecasting energy consumption in a smart household: A comparative study of ensemble methods DOI Creative Commons

Mahziyar Dostmohammadi,

Mona Zamani Pedram,

Siamak Hoseinzadeh

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 364, P. 121264 - 121264

Published: June 12, 2024

The considerable amount of energy utilized by buildings has led to various environmental challenges that adversely impact human existence. Predicting buildings' usage is commonly acknowledged as encouraging efficiency and enabling well-informed decision-making, ultimately leading decreased consumption. Implementing eco-friendly architectural designs paramount in mitigating consumption, particularly recently constructed structures. This study utilizes clustering analysis on the original dataset capture complex consumption patterns over periods. yields two distinct subsets represent low high an additional subset exclusively encompasses weekends, attributed specific behavior occupants. Ensemble models have become increasingly popular due advancements machine learning techniques. research three discrete algorithms, namely Artificial Neural Network (ANN), K-nearest neighbors (KNN), Decision Trees (DT). In addition, application employs more algorithms bagging boosting: Random Forest (RF), Extreme Gradient Boosting (XGB), (GBT). To augment accuracy predictions, a stacking ensemble methodology employed, wherein forecasts generated many are combined. Given obtained outcomes, thorough examination undertaken, encompassing techniques stacking, bagging, boosting, conduct comprehensive comparative study. It pertinent highlight technique consistently exhibits superior performance relative alternative methodologies across spectrum heterogeneous datasets. Furthermore, using genetic algorithm enables optimization combination base learners, resulting notable enhancement prediction accuracy. After implementing this technique, GA-Stacking demonstrated remarkable Mean Absolute Percentage Error (MAPE) scores. improvement observed was substantial, surpassing 90 percent for all subset-1, subset-2, subset-3, achieved R

Language: Английский

Citations

6
Efficiency improvement in energy consumption: A novel deep learning based model for leading a greener Economic recovery DOI
Zhiliang Chu, Wang Yizhu

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 108, P. 105427 - 105427

Published: April 25, 2024

Language: Английский

Citations

5
Artificial intelligence for deconstruction: Current state, challenges, and opportunities DOI Creative Commons
Habeeb Balogun,

Hafiz Alaka,

Eren Demir

et al.

Automation in Construction, Journal Year: 2024, Volume and Issue: 166, P. 105641 - 105641

Published: July 30, 2024

Artificial intelligence and its subfields, such as machine learning, robotics, optimisation, knowledge-based systems, reality capture extended reality, have brought remarkable advancements transformative changes to various industries, including the building deconstruction industry. Acknowledging AI's benefits for deconstruction, this paper aims investigate AI applications within domain. A systematic review of existing literature focused on planning, implementation post-implementation activities context was carried out. Furthermore, challenges opportunities were identified presented in paper. By offering insights into application key activities, paves way realising potential sector.

Language: Английский

Citations

4
Research on the Parameter Prediction Model for Fully Mechanized Mining Equipment Selection Based on RF-WOA-XGBoost DOI Creative Commons
Yue Wu,

Wenjie Sang,

Xiangang Cao

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(2), P. 732 - 732

Published: Jan. 13, 2025

Fully mechanized mining equipment is core to the coal process. The selection process for this type of complex and heavily relies on experts’ experience determining parameters. This paper proposes a fully parameter prediction model based Extreme Gradient Boosting Regression Trees (XGBoost), which developed mapping relationships among geological parameters, face conditions, parameters equipment. Feature performed feature importance ranking obtained through Random Forest (RF) method, thereby reducing complexity. Different optimization algorithms are used optimize hyperparameters XGBoost, results show that Whale Optimization Algorithm (WOA) outperforms other in terms convergence speed effectiveness. By comparing different algorithms, it found WOA-XGBoost achieves higher accuracy test set, with an average absolute error 0.0458, root mean square 0.1610, coefficient determination (R2) 0.9451. Finally, RF-WOA-XGBoost-based established, suitable lightly inclined faces. reduces input complexity, improves speed, minimizes reliance experts, ensures accuracy, providing effective reference

Language: Английский

Citations

0
An energy consumption prediction approach in smart cities by CNN-LSTM network improved with game theory and Namib Beetle Optimization (NBO) algorithm DOI

Meysam Chahardoli,

Nafiseh Osati Eraghi, Sara Nazari

et al.

The Journal of Supercomputing, Journal Year: 2025, Volume and Issue: 81(2)

Published: Jan. 20, 2025

Language: Английский

Citations

0
Rapid Estimation of Truck Cycle Time in Open-Pit Mine Haulage Based on Feature-Optimized Machine Learning DOI
Chengkai Fan, Na Zhang, Bei Jiang

et al.

Mining Metallurgy & Exploration, Journal Year: 2025, Volume and Issue: unknown

Published: March 18, 2025

Language: Английский

Citations

0
Interpretable building energy performance prediction using xgboost quantile regression DOI
Sinem Güler Kangallı Uyar, Bilge Kagan Ozbay, Berker Dal

et al.

Energy and Buildings, Journal Year: 2025, Volume and Issue: unknown, P. 115815 - 115815

Published: May 1, 2025

Language: Английский

Citations

0
Optimizing Energy Efficiency Through Building Orientation and Building Information Modelling (BIM) in Diverse Terrains: A Case Study in Pakistan DOI

Abdul Mateen Khan,

Muhammad Abubakar Tariq,

Zeshan Alam

et al.

Energy, Journal Year: 2024, Volume and Issue: unknown, P. 133307 - 133307

Published: Oct. 1, 2024

Language: Английский

Citations

3