[HTML][HTML] Physical energy and data-driven models in building energy prediction: A review
The difficulty in balancing energy supply and demand is increasing due to the growth of
diversified and flexible building energy resources, particularly the rapid development of …
diversified and flexible building energy resources, particularly the rapid development of …
[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 of the-state-of-the-art in data-driven approaches for building energy prediction
Building energy prediction plays a vital role in developing a model predictive controller for
consumers and optimizing energy distribution plan for utilities. Common approaches for …
consumers and optimizing energy distribution plan for utilities. Common approaches for …
Building thermal load prediction through shallow machine learning and deep learning
Building thermal load prediction informs the optimization of cooling plant and thermal energy
storage. Physics-based prediction models of building thermal load are constrained by the …
storage. Physics-based prediction models of building thermal load are constrained by the …
[HTML][HTML] A building energy consumption prediction model based on rough set theory and deep learning algorithms
L Lei, W Chen, B Wu, C Chen, W Liu - Energy and Buildings, 2021 - Elsevier
The efficient and accurate prediction of building energy consumption can improve the
management of power systems. In this paper, the rough set theory was used to reduce the …
management of power systems. In this paper, the rough set theory was used to reduce the …
DeST 3.0: A new-generation building performance simulation platform
Buildings contribute to almost 30% of total energy consumption worldwide. Developing
building energy modeling programs is of great significance for lifecycle building …
building energy modeling programs is of great significance for lifecycle building …
Practical issues in implementing machine-learning models for building energy efficiency: Moving beyond obstacles
Implementing machine-learning models in real applications is crucial to achieving intelligent
building control and high energy efficiency. Over the past few decades, numerous studies …
building control and high energy efficiency. Over the past few decades, numerous studies …
Scenario-based prediction of climate change impacts on building cooling energy consumption with explainable artificial intelligence
In this paper, we present a newly developed eXplainable artificial intelligence (XAI) model to
analyze the impacts of climate change on the cooling energy consumption (E c) in buildings …
analyze the impacts of climate change on the cooling energy consumption (E c) in buildings …
[HTML][HTML] Evaluation of the impact of input uncertainty on urban building energy simulations using uncertainty and sensitivity analysis
E Prataviera, J Vivian, G Lombardo, A Zarrella - Applied Energy, 2022 - Elsevier
The energy consumption of cities is increasing fast due to growing global population and
rapid urbanization. Urban Building Energy Models (UBEMs) are promising tools to simulate …
rapid urbanization. Urban Building Energy Models (UBEMs) are promising tools to simulate …
[HTML][HTML] Predicting energy consumption for residential buildings using ANN through parametric modeling
E Elbeltagi, H Wefki - Energy Reports, 2021 - Elsevier
Controlling buildings energy consumption is a great practical significance. During early
design stage, accurate and rapid prediction of energy consumption could provide a …
design stage, accurate and rapid prediction of energy consumption could provide a …