[HTML][HTML] A review on occupancy prediction through machine learning for enhancing energy efficiency, air quality and thermal comfort in the built environment
The occupants' presence, activities, and behaviour can significantly impact the building's
performance and energy efficiency. Currently, heating, ventilation, and air-conditioning …
performance and energy efficiency. Currently, heating, ventilation, and air-conditioning …
A review on optimal energy management in commercial buildings
The rising cost and demand for energy have prompted the need to devise innovative
methods for energy monitoring, control, and conservation. In addition, statistics show that …
methods for energy monitoring, control, and conservation. In addition, statistics show that …
[HTML][HTML] Integrated climate-responsive thermal load ML model and cost/embodied energy estimate from a preliminary building design
M Abdolvand, A Nezhad, M Bambach… - Energy and …, 2024 - Elsevier
This paper proposes a framework applicable to preliminary building design that cohesively
integrates material cost estimation, embodied energy assessment, and the prediction of the …
integrates material cost estimation, embodied energy assessment, and the prediction of the …
Design of ensemble forecasting models for home energy management systems
The increasing levels of energy consumption worldwide is raising issues with respect to
surpassing supply limits, causing severe effects on the environment, and the exhaustion of …
surpassing supply limits, causing severe effects on the environment, and the exhaustion of …
An intelligent data-driven approach for electrical energy load management using machine learning algorithms
Data-driven electrical energy efficiency management is the emerging trend in electrical
energy forecasting and management. This fusion of data science, artificial intelligence, and …
energy forecasting and management. This fusion of data science, artificial intelligence, and …
Advances in Deep Learning Techniques for Short-term Energy Load Forecasting Applications: A Review
R Chandrasekaran, SK Paramasivan - Archives of Computational Methods …, 2024 - Springer
Today, the majority of the leading power companies place a significant emphasis on
forecasting the electricity load in the balance of power and administration. Meanwhile, since …
forecasting the electricity load in the balance of power and administration. Meanwhile, since …
Machine learning applications for smart building energy utilization: a survey
Abstract The United Nations launched sustainable development goals in 2015 that include
goals for sustainable energy. From global energy consumption, households consume 20 …
goals for sustainable energy. From global energy consumption, households consume 20 …
Spatial Model for Energy Consumption of LEED-Certified Buildings
J Kim, SY Moon, D Jang - Sustainability, 2023 - mdpi.com
In this research endeavor, we undertook a comprehensive examination of the factors
influencing the energy consumption of LEED-certified buildings, employing both a general …
influencing the energy consumption of LEED-certified buildings, employing both a general …
Prediction of energy consumption for variable customer portfolios including aleatoric uncertainty estimation
O Mey, A Schneider, O Enge-Rosenblatt… - … on Power Science …, 2021 - ieeexplore.ieee.org
Using hourly energy consumption data recorded by smart meters, retailers can estimate the
day-ahead energy consumption of their customer portfolio. Deep neural networks are …
day-ahead energy consumption of their customer portfolio. Deep neural networks are …
Sustainable EnergySense: a predictive machine learning framework for optimizing residential electricity consumption
M Al-Rajab, S Loucif - Discover Sustainability, 2024 - Springer
In a world where electricity is often taken for granted, the surge in consumption poses
significant challenges, including elevated CO2 emissions and rising prices. These issues …
significant challenges, including elevated CO2 emissions and rising prices. These issues …