Machine Learning Models for Prediction of Building Energy Performance

2021 ◽  
pp. 77-98
Author(s):  
Saleh Seyedzadeh ◽  
Farzad Pour Rahimian
Keyword(s):  
Machine Learning ◽  
Building Energy ◽  
Energy Performance ◽  
Learning Models ◽  
Building Energy Performance ◽  
Machine Learning Models

Practical issues in implementing machine-learning models for building energy efficiency: Moving beyond obstacles

2021 ◽  
Vol 143 ◽  
pp. 110929
Author(s):  
Zeyu Wang ◽  
Jian Liu ◽  
Yuanxin Zhang ◽  
Hongping Yuan ◽  
Ruixue Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
Building Energy ◽  
Building Energy Efficiency ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals Tuning machine learning models for prediction of building energy loads

2019 ◽  
Vol 47 ◽  
pp. 101484 ◽  
Author(s):  
Saleh Seyedzadeh ◽  
Farzad Pour Rahimian ◽  
Parag Rastogi ◽  
Ivan Glesk
Keyword(s):  
Machine Learning ◽  
Building Energy ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals Building Energy Information: Demand and Consumption Prediction with Machine Learning Models for Sustainable and Smart Cities

2019 ◽  
Author(s):  
Sina Faizollahzadeh ardabili ◽  
Amir Mosavi ◽  
Annamária R. Várkonyi-Kóczy
Keyword(s):  
Machine Learning ◽  
Energy Demand ◽  
Smart Cities ◽  
Building Energy ◽  
Learning Models ◽  
Information Demand ◽  
Building Energy Systems ◽  
Consumption Prediction ◽  
Energy Information ◽  
Machine Learning Models

Building energy consumption plays an essential role in urban sustainability. The prediction of the energy demand is also of particular importance for developing smart cities and urban planning. Machine learning has recently contributed to the advancement of methods and technologies to predict demand and consumption for building energy systems. This paper presents a state of the art of machine learning models and evaluates the performance of these models. Through a systematic review and a comprehensive taxonomy, the advances of machine learning are carefully investigated and promising models are introduced.

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