Predicting Steam Turbine Power Generation: A Comparison of Long Short-Term Memory and Willans Line Model DOI Creative Commons
Mostafa Pasandideh, Matthew Taylor, Shafiqur Rahman Tito

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(2), P. 352 - 352

Published: Jan. 10, 2024

This study focuses on using machine learning techniques to accurately predict the generated power in a two-stage back-pressure steam turbine used paper production industry. In order by turbine, it is crucial consider time dependence of input data. For this purpose, long-short-term memory (LSTM) approach employed. Correlation analysis performed select parameters with correlation coefficient greater than 0.8. Initially, nine inputs are considered, and showcases superior performance LSTM method, an accuracy rate 0.47. Further refinement conducted reducing four based analysis, resulting improved 0.39. The comparison between method Willans line model evaluates efficacy former predicting power. root mean square error (RMSE) evaluation parameter assess prediction algorithm for generator’s By highlighting importance selecting appropriate techniques, high-quality data, utilising refinement, work demonstrates valuable estimating energy

Language: Английский

The effect of carbon dioxide emissions on the building energy efficiency DOI

Ji Min,

Gongxing Yan, Azher M. Abed

et al.

Fuel, Journal Year: 2022, Volume and Issue: 326, P. 124842 - 124842

Published: June 30, 2022

Language: Английский

Citations

95
Autonomous surface crack identification of concrete structures based on an improved one-stage object detection algorithm DOI

Pei‐Rong Wu,

Airong Liu,

Jiyang Fu

et al.

Engineering Structures, Journal Year: 2022, Volume and Issue: 272, P. 114962 - 114962

Published: Sept. 28, 2022

Language: Английский

Citations

73
A novel swarm intelligence: cuckoo optimization algorithm (COA) and SailFish optimizer (SFO) in landslide susceptibility assessment DOI
Rana Muhammad Adnan Ikram, Atefeh Ahmadi Dehrashid, Binqiao Zhang

et al.

Stochastic Environmental Research and Risk Assessment, Journal Year: 2023, Volume and Issue: 37(5), P. 1717 - 1743

Published: Jan. 29, 2023

Language: Английский

Citations

44
Isotherms, kinetics and thermodynamic mechanism of methylene blue dye adsorption on synthesized activated carbon DOI Creative Commons
Atef El Jery, Heba Saed Kariem Alawamleh,

Mustafa Humam Sami

et al.

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

Published: Jan. 10, 2024

The treatment of methylene blue (MB) dye wastewater through the adsorption process has been a subject extensive research. However, comprehensive understanding thermodynamic aspects solution is lacking. Previous studies have primarily focused on enhancing capacity dye. This study aimed to develop an environmentally friendly and cost-effective method for treating gain insights into thermodynamics kinetics optimization. An adsorbent with selective capabilities was synthesized using rice straw as precursor. Experimental were conducted investigate isotherms models under various conditions, aiming bridge gaps in previous research enhance mechanisms. Several isotherm models, including Langmuir, Temkin, Freundlich, Langmuir-Freundlich, applied theoretically describe mechanism. Equilibrium results demonstrated that calculated equilibrium (q

Language: Английский

Citations

35
Operation optimization for a CHP system using an integrated approach of ANN and simulation database DOI
Yue Cao, Hui Hu,

Ranjing Chen

et al.

Applied Thermal Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 125771 - 125771

Published: Jan. 1, 2025

Language: Английский

Citations

2
Research on sustainability evaluation of green building engineering based on artificial intelligence and energy consumption DOI Creative Commons
Yong Xiang, Yonghua Chen, Jiaojiao Xu

et al.

Energy Reports, Journal Year: 2022, Volume and Issue: 8, P. 11378 - 11391

Published: Sept. 14, 2022

A green building is a structure that avoids or eliminates negative environmental impacts and generates benefits through its design, construction, functioning. The use of ecologically friendly materials increases the quality life. overuse electronic equipment hinders achievement overall aim, even if smart buildings are beneficial stimulus for sustainability. Demand-side management energy consumption prediction connected depend on accurate estimates how much facility will need. While several approaches have been offered predicting use, each method has advantages disadvantages, there always room improvement. This paper suggests Artificial Intelligence-based Energy Management Model (AI-EMM) in building. Adaptable to human choices, it can act intelligently increase user comfort, safety, efficiency. One key components AI-EMM model universal infrared communication system subsystems identification monitoring internal exterior surroundings. Long Short-Term Memory (LSTM) models used enhance consumption. building's usage data analysed using suggested approach. For better interior climate, studies examining relationship between Heating, Ventilation, Air Conditioning (HVAC) should focus airside design optimization. According findings, economic gains environmentally sustainable coexist harmoniously. one whose characteristics preserve local environment. experimental outcome achieved high-performance ratio 94.3%, less 15.7%, accuracy 97.4%, level 95.7%, 97.1%.

Language: Английский

Citations

59
A Review on Sustainable Energy Sources Using Machine Learning and Deep Learning Models DOI Creative Commons
Ashok Bhansali,

Namala Narasimhulu,

Rocío Pérez de Prado

et al.

Energies, Journal Year: 2023, Volume and Issue: 16(17), P. 6236 - 6236

Published: Aug. 28, 2023

Today, methodologies based on learning models are utilized to generate precise conversion techniques for renewable sources. The methods Computational Intelligence (CI) considered an effective way instruments. energy-related complexities of developing such dependent the vastness data sets and number parameters needed be covered, both which need carefully examined. most recent significant researchers in field learning-based approaches challenges addressed this article. There several different Deep Learning (DL) Machine (ML) that solar, wind, hydro, tidal energy A new taxonomy is formed process evaluating effectiveness strategies described literature. This survey evaluates advantages drawbacks existing helps find approach overcome issues methods. In study, various systems source energies like hydro power, evaluated using ML DL approaches.

Language: Английский

Citations

31
Novel Neural Network Optimized by Electrostatic Discharge Algorithm for Modification of Buildings Energy Performance DOI Open Access
Arash Mohammadi Fallah,

Ehsan Ghafourian,

Ladan Shahzamani Sichani

et al.

Sustainability, Journal Year: 2023, Volume and Issue: 15(4), P. 2884 - 2884

Published: Feb. 5, 2023

Proper analysis of building energy performance requires selecting appropriate models for handling complicated calculations. Machine learning has recently emerged as a promising effective solution solving this problem. The present study proposes novel integrative machine model predicting two parameters residential buildings, namely annual thermal demand (DThE) and weighted average discomfort degree-hours (HDD). is feed-forward neural network (FFNN) that optimized via the electrostatic discharge algorithm (ESDA) analyzing characteristics finding their optimal contribution to DThE HDD. According results, proposed an double-target can predict required with superior accuracy. Moreover, further verify efficiency ESDA, was compared three similar optimization techniques, atom search (ASO), future (FSA), satin bowerbird (SBO). Considering Pearson correlation indices 0.995 0.997 (for HDD, respectively) obtained ESDA-FFNN versus 0.992 0.938 ASO-FFNN, 0.926 0.895 FSA-FFNN, 0.994 SBO-FFNN, ESDA provided higher accuracy training. Subsequently, by collecting weights biases FFNN, formulas were developed easier computation HDD in new cases. It posited engineers experts could consider use along investigating buildings.

Language: Английский

Citations

28
Multilayer Perceptron and Their Comparison with Two Nature-Inspired Hybrid Techniques of Biogeography-Based Optimization (BBO) and Backtracking Search Algorithm (BSA) for Assessment of Landslide Susceptibility DOI Creative Commons
Hossein Moayedi, Peren Jerfi Canatalay, Atefeh Ahmadi Dehrashid

et al.

Land, Journal Year: 2023, Volume and Issue: 12(1), P. 242 - 242

Published: Jan. 12, 2023

Regarding evaluating disaster risks in Iran’s West Kurdistan area, the multi-layer perceptron (MLP) neural network was upgraded with two novel techniques: backtracking search algorithm (BSA) and biogeography-based optimization (BBO). Utilizing 16 landslide conditioning elements such as elevation (aspect), plan (curve), profile (curvature), geology, NDVI (land use), slope (degree), stream power index (SPI), topographic wetness (TWI), rainfall, sediment transport (STI), 504 landslides target variables, a large geographic database is constructed. Applying techniques mentioned above to synthesis of MLP results suggested BBO-MLP BSA-MLP ensembles. As accuracy standards, we benefit from mean absolute error, square area under receiving operating characteristic curve assess utilized models, have also designed scoring system. The MLP’s increases thanks application BBO BSA algorithms. Comparing BSA, find that former achieves higher average ranks (20, 15, 14). A further finding showed superior at maximizing MLP.

Language: Английский

Citations

23
Research on key technologies of high energy efficiency and low power consumption of new data acquisition equipment of power Internet of Things based on artificial intelligence DOI Creative Commons
Xing Li,

Haiping Zhao,

Yiming Feng

et al.

International Journal of Thermofluids, Journal Year: 2024, Volume and Issue: 21, P. 100575 - 100575

Published: Jan. 21, 2024

Energy efficiency is a critical problem that drives consideration of smart cities and urban areas' development. security the environment face enormous problems because dramatic rise in energy consumption brought on by rising population levels widespread use new data-collecting technologies. Traditional grids can be updated with IoT-based metering (SM) advanced infrastructure (AMI) technologies revealing previously hidden information about electrical power implementing communication system between utilities consumers during transaction process. The distribution city environments are strongly supported Internet Things (IoT) Artificial Intelligence (AI). Hence, this paper suggests IoT AI-assisted Smart Metering System (IoT-AI-SMS) as data acquisition equipment for predicting cities. taken from Efficiency Datasets to examine cities' consumption. This research offers Recurrent Neural Network (RNN) load forecasting using meter data. technique allows training single model all participating meters without exchanging local information. Considering customers' needs, developed scheduled controllable loads offered optimal dispatch distributed generation grid.

Language: Английский

Citations

15