[HTML][HTML] Challenges of artificial intelligence development in the context of energy consumption and impact on climate change
With accelerating climate change and rising global energy consumption, the application of
artificial intelligence (AI) and machine learning (ML) has emerged as a crucial tool for …
artificial intelligence (AI) and machine learning (ML) has emerged as a crucial tool for …
Tree-based machine learning models with optuna in predicting impedance values for circuit analysis
The transmission characteristics of the printed circuit board (PCB) ensure signal integrity
and support the entire circuit system, with impedance matching being critical in the design of …
and support the entire circuit system, with impedance matching being critical in the design of …
Flood subsidence susceptibility map** using elastic-net classifier: new approach
In light of recent improvements in flood susceptibility map** using machine learning
models, there remains a lack of research focusing on employing ensemble algorithms like …
models, there remains a lack of research focusing on employing ensemble algorithms like …
Prediction of energy use intensity of urban buildings using the semi-supervised deep learning model
Prediction of building energy performance is a critical strategy for building energy
management. Extant studies established city-scale prediction models only based on …
management. Extant studies established city-scale prediction models only based on …
[HTML][HTML] Sparse dynamic graph learning for district heat load forecasting
Accurate heat load forecasting is crucial for the efficient operation and management of
district heating systems. This study introduces a novel Sparse Dynamic Graph Neural …
district heating systems. This study introduces a novel Sparse Dynamic Graph Neural …
[HTML][HTML] Interactive effects of hyperparameter optimization techniques and data characteristics on the performance of machine learning algorithms for building energy …
B Si, Z Ni, J Xu, Y Li, F Liu - Case Studies in Thermal Engineering, 2024 - Elsevier
Metamodeling is a promising technique for alleviating the computational burden of building
energy simulation. Although various machine learning (ML) algorithms have been applied …
energy simulation. Although various machine learning (ML) algorithms have been applied …
Biomass supply chain resilience: Integrating demand and availability predictions into routing decisions using machine learning
Biomass sources have the potential to mitigate carbon emissions as a renewable source
while reducing waste and residues. Seasonality and disruption risks are some of the …
while reducing waste and residues. Seasonality and disruption risks are some of the …
Forecasting operation of a chiller plant facility using data-driven models
In recent years, data-driven models have enabled accurate prediction of chiller power
consumption and chiller coefficient of performance (COP). This study evaluates the usage of …
consumption and chiller coefficient of performance (COP). This study evaluates the usage of …
Predicting the energy consumption of a VRF heat pump using manufacturer performance data and limited experimentation for dynamic data collection
K Oh, EJ Kim - Energy and Buildings, 2024 - Elsevier
The variable refrigerant flow (VRF) air conditioner is widely used because it can control
indoor air conditioning units individually, allowing for efficient energy use. However …
indoor air conditioning units individually, allowing for efficient energy use. However …
Analysis of the integration of drift detection methods in learning algorithms for electrical consumption forecasting in smart buildings
Buildings are currently among the largest consumers of electrical energy with considerable
increases in CO2 emissions in recent years. Although there have been notable advances in …
increases in CO2 emissions in recent years. Although there have been notable advances in …