Artificial Intelligence in Smart Cities—Applications, Barriers, and Future Directions: A Review
As urbanization continues to pose new challenges for cities around the world, the concept of
smart cities is a promising solution, with artificial intelligence (AI) playing a central role in this …
smart cities is a promising solution, with artificial intelligence (AI) playing a central role in this …
Leveraging Artificial Intelligence to Bolster the Energy Sector in Smart Cities: A Literature Review
As Smart Cities development grows, deploying advanced technologies, such as the Internet
of Things (IoT), Cyber–Physical Systems, and particularly, Artificial Intelligence (AI) …
of Things (IoT), Cyber–Physical Systems, and particularly, Artificial Intelligence (AI) …
Ensemble learning method for classification: Integrating data envelopment analysis with machine learning
Q An, S Huang, Y Han, Y Zhu - Computers & Operations Research, 2024 - Elsevier
In classification tasks with large sample sets, the use of a single classifier carries the risk of
overfitting. To overcome this issue, an ensemble of classifier models has often been shown …
overfitting. To overcome this issue, an ensemble of classifier models has often been shown …
Forecasting model of building energy consumption based on parallel Kriging sampling algorithm
D Zhao, X You - Sustainable Energy Technologies and Assessments, 2024 - Elsevier
Parallel Kriging sampling approach (PEIGF) with minimum computing resources is proposed
to promote the speed of building energy consumption. In each iteration of PEIGF, the initial …
to promote the speed of building energy consumption. In each iteration of PEIGF, the initial …
Thermostat Anchors: Do Temperature Scale Characteristics Affect the Selection of Temperature Setpoints for Residential Homes?
Characteristics of scales, such as the labels that are used on scales, have been shown to
affect judgments. The scale-dependency hypothesis predicts specific effects of the …
affect judgments. The scale-dependency hypothesis predicts specific effects of the …
Comparative Analysis between Intelligent Machine Committees and Hybrid Deep Learning with Genetic Algorithms in Energy Sector Forecasting: A Case Study on …
T Conte, R Oliveira - Energies, 2024 - mdpi.com
Global environmental impacts such as climate change require behavior from society that
aims to minimize greenhouse gas emissions. This includes the substitution of fossil fuels …
aims to minimize greenhouse gas emissions. This includes the substitution of fossil fuels …
A hybrid evolutionary and machine learning approach for smart building: Sustainable building energy management design
W Li, X Xu - Sustainable Energy Technologies and Assessments, 2024 - Elsevier
Household energy usage can be actively managed by home users through the smart grid,
utilizing the Home Energy Management (HEM) system. Within the framework of a demand …
utilizing the Home Energy Management (HEM) system. Within the framework of a demand …
Deep Learning Method to Analyze the Bi-LSTM Model for Energy Consumption Forecasting in Smart Cities
S Balasubramaniyan, PK Kumar… - 2023 International …, 2023 - ieeexplore.ieee.org
Smart cities and IoT solutions are improving urban efficiency, resource optimization, and
public safety by using modern technologies. Deep residual Bi-LSTM (Long Short-Term …
public safety by using modern technologies. Deep residual Bi-LSTM (Long Short-Term …