Potential of explainable artificial intelligence in advancing renewable energy: challenges and prospects
Modern machine learning (ML) techniques are making inroads in every aspect of renewable
energy for optimization and model prediction. The effective utilization of ML techniques for …
energy for optimization and model prediction. The effective utilization of ML techniques for …
[HTML][HTML] AI analytics for carbon-neutral city planning: A systematic review of applications
Artificial intelligence (AI) has become a transformative force across various disciplines,
including urban planning. It has unprecedented potential to address complex challenges. An …
including urban planning. It has unprecedented potential to address complex challenges. An …
[HTML][HTML] An interpretable multi-stage forecasting framework for energy consumption and CO2 emissions for the transportation sector
The transportation sector is deemed one of the primary sources of energy consumption and
greenhouse gases throughout the world. To realise and design sustainable transport, it is …
greenhouse gases throughout the world. To realise and design sustainable transport, it is …
Energy consumption prediction and household feature analysis for different residential building types using machine learning and SHAP: Toward energy-efficient …
US residential buildings account for a significant share of national energy consumption,
highlighting their potential for energy-savings. Accurately predicting building energy …
highlighting their potential for energy-savings. Accurately predicting building energy …
[HTML][HTML] Development of advanced machine learning for prognostic analysis of drying parameters for banana slices using indirect solar dryer
In this study, eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting (LightGBM)
algorithms were used to model-predict the drying characteristics of banana slices with an …
algorithms were used to model-predict the drying characteristics of banana slices with an …
Performance comparison on improved data-driven building energy prediction under data shortage scenarios in four perspectives: Data generation, incremental …
G Li, L Zhan, X Fang, J Gao, C Xu, X He, J Deng… - Energy, 2024 - Elsevier
Accurate building energy predictions (BEPs) are crucial for maintaining a built environment's
sustainability and energy systems. Many data-driven BEPs rely heavily on sufficient data …
sustainability and energy systems. Many data-driven BEPs rely heavily on sufficient data …
Toward improved urban building energy modeling using a place-based approach
Urban building energy models present a valuable tool for promoting energy efficiency in
building design and control, as well as for managing urban energy systems. However, the …
building design and control, as well as for managing urban energy systems. However, the …
Calibrating thermal sensation vote scales for different short-term thermal histories using ensemble learning
The urban heat island effect intensifies, leading to increased thermal exposure for city
residents. Variations in thermal sensation are observed among individuals with different …
residents. Variations in thermal sensation are observed among individuals with different …
Deep Learning for Anomaly Detection in Time-Series Data: An Analysis of Techniques, Review of Applications, and Guidelines for Future Research
Industries are generating massive amounts of data due to increased automation and
interconnectedness. As data from various sources becomes more available, the extraction of …
interconnectedness. As data from various sources becomes more available, the extraction of …
Predicting occupant energy consumption in different indoor layout configurations using a hybrid agent-based modeling and machine learning approach
Accurately predicting occupant energy consumption in buildings is essential for optimizing
energy management and promoting sustainability. However, gathering reliable stochastic …
energy management and promoting sustainability. However, gathering reliable stochastic …