Machine learning in concrete science: applications, challenges, and best practices
Concrete, as the most widely used construction material, is inextricably connected with
human development. Despite conceptual and methodological progress in concrete science …
human development. Despite conceptual and methodological progress in concrete science …
Data-driven prediction and optimization toward net-zero and positive-energy buildings: A systematic review
SN Mousavi, MG Villarreal-Marroquín… - Building and …, 2023 - Elsevier
Recent advances toward sustainable cities have promoted the concept of near-zero energy
consumption. A Positive Energy Building (PEB) model has been developed by the European …
consumption. A Positive Energy Building (PEB) model has been developed by the European …
Low temperature phase change materials for thermal energy storage: Current status and computational perspectives
Latent heat based thermal energy storage technology is quite promising due to its
reasonable cost and high energy storage capacity. This technology is partially developed …
reasonable cost and high energy storage capacity. This technology is partially developed …
Forecasting energy demand of PCM integrated residential buildings: A machine learning approach
M Zhussupbekov, SA Memon, SA Khawaja… - Journal of Building …, 2023 - Elsevier
Forecasting energy demand has become an essential element for energy stakeholders in
planning and reducing the energy consumption of buildings. Machine learning techniques …
planning and reducing the energy consumption of buildings. Machine learning techniques …
Investigation on the thermal control and performance of PCM–porous media-integrated heat sink systems: Deep neural network modelling employing experimental …
Phase change material (PCM)-based heat sinks can offer reliable and effective thermal
management (TM) solutions for increasingly sophisticated applications. A critical aspect of …
management (TM) solutions for increasingly sophisticated applications. A critical aspect of …
Application of machine learning algorithms to model soil thermal diffusivity
Soil thermal diffusivity (k) is an important thermal property that significantly affects ground
energy storage and heat transfer. Direct measurements of soil thermal diffusivity are …
energy storage and heat transfer. Direct measurements of soil thermal diffusivity are …
Energy consumption predictions by genetic programming methods for PCM integrated building in the tropical savanna climate zone
The development of energy-efficient buildings by considering early-stage design parameters
can help reduce buildings' energy consumption. Machine learning tools are getting popular …
can help reduce buildings' energy consumption. Machine learning tools are getting popular …
Coupling PCM wallboard utilization with night Ventilation: Energy efficiency and overheating risk in office buildings under climate change impact
The rising population and increasing thermal comfort expectations are expected to
exacerbate the already high HVAC use in offices, which unavoidably places a strain on …
exacerbate the already high HVAC use in offices, which unavoidably places a strain on …
Predicting energy performances of buildings' envelope wall materials via the random forest algorithm
Purpose Numerous simulation software has been used to evaluate energy performance with
12% of the research focusing on long-term energy consumption prediction. This paper aims …
12% of the research focusing on long-term energy consumption prediction. This paper aims …
A machine learning methodology for the diagnosis of phase change material-based thermal management systems
Phase change materials (PCM) have received significant interest in various thermal energy
storage and management applications due to their ample latent heat during the phase …
storage and management applications due to their ample latent heat during the phase …