[HTML][HTML] An overview of machine learning applications for smart buildings
The efficiency, flexibility, and resilience of building-integrated energy systems are
challenged by unpredicted changes in operational environments due to climate change and …
challenged by unpredicted changes in operational environments due to climate change and …
[HTML][HTML] Building energy prediction models and related uncertainties: A review
Building energy usage has been an important issue in recent decades, and energy
prediction models are important tools for analysing this problem. This study provides a …
prediction models are important tools for analysing this problem. This study provides a …
[HTML][HTML] Smart city and cyber-security; technologies used, leading challenges and future recommendations
C Ma - Energy Reports, 2021 - Elsevier
Today, some cities around the world have tended to use new technologies and become
smart city. New technologies improve the quality of citizens' life. However, the use of any …
smart city. New technologies improve the quality of citizens' life. However, the use of any …
[HTML][HTML] Short-term electricity load forecasting with machine learning
E Aguilar Madrid, N Antonio - Information, 2021 - mdpi.com
An accurate short-term load forecasting (STLF) is one of the most critical inputs for power
plant units' planning commitment. STLF reduces the overall planning uncertainty added by …
plant units' planning commitment. STLF reduces the overall planning uncertainty added by …
[HTML][HTML] Enhancing building energy efficiency using a random forest model: A hybrid prediction approach
The building envelope considerably influences building energy consumption. To enhance
the energy efficiency of buildings, this paper proposes an approach to predict building …
the energy efficiency of buildings, this paper proposes an approach to predict building …
[HTML][HTML] Modeling energy demand—a systematic literature review
PA Verwiebe, S Seim, S Burges, L Schulz… - Energies, 2021 - mdpi.com
In this article, a systematic literature review of 419 articles on energy demand modeling,
published between 2015 and 2020, is presented. This provides researchers with an …
published between 2015 and 2020, is presented. This provides researchers with an …
[HTML][HTML] Current status, challenges, and prospects of data-driven urban energy modeling: A review of machine learning methods
Urban energy modeling is essential in planning electricity generation and efficiently
managing electric power systems. Various urban energy models were developed for several …
managing electric power systems. Various urban energy models were developed for several …
Optimization of operational strategy for ice thermal energy storage in a district cooling system based on model predictive control
Thermal energy storage (TES) has been widely applied in buildings to shift air-conditioning
peak loads and to reduce operating costs by using time-of-use (ToU) tariffs. Meanwhile, TES …
peak loads and to reduce operating costs by using time-of-use (ToU) tariffs. Meanwhile, TES …
Data-driven approach to predicting the energy performance of residential buildings using minimal input data
To achieve carbon neutrality, the South Korean government has been retrofitting existing
buildings to reduce their energy consumption. However, existing buildings often lack …
buildings to reduce their energy consumption. However, existing buildings often lack …
[HTML][HTML] Data-driven tools for building energy consumption prediction: A review
The development of data-driven building energy consumption prediction models has gained
more attention in research due to its relevance for energy planning and conservation …
more attention in research due to its relevance for energy planning and conservation …