Machine Learning for Building Energy Forecasting

2021 ◽  
pp. 41-76
Author(s):  
Saleh Seyedzadeh ◽  
Farzad Pour Rahimian
Keyword(s):  
Machine Learning ◽  
Building Energy ◽  
Energy Forecasting

scholarly journals Evaluation of Urban-Scale Building Energy-Use Models and Tools—Application for the City of Fribourg, Switzerland

2021 ◽  
Vol 13 (4) ◽  
pp. 1595
Author(s):  
Valeria Todeschi ◽  
Roberto Boghetti ◽  
Jérôme H. Kämpf ◽  
Guglielmina Mutani
Keyword(s):  
Machine Learning ◽  
Energy Use ◽  
Residential Buildings ◽  
Building Energy ◽  
Energy Performance ◽  
Space Heating ◽  
Engineering Model ◽  
Building Energy Use ◽  
The Impact ◽  
The City

Building energy-use models and tools can simulate and represent the distribution of energy consumption of buildings located in an urban area. The aim of these models is to simulate the energy performance of buildings at multiple temporal and spatial scales, taking into account both the building shape and the surrounding urban context. This paper investigates existing models by simulating the hourly space heating consumption of residential buildings in an urban environment. Existing bottom-up urban-energy models were applied to the city of Fribourg in order to evaluate the accuracy and flexibility of energy simulations. Two common energy-use models—a machine learning model and a GIS-based engineering model—were compared and evaluated against anonymized monitoring data. The study shows that the simulations were quite precise with an annual mean absolute percentage error of 12.8 and 19.3% for the machine learning and the GIS-based engineering model, respectively, on residential buildings built in different periods of construction. Moreover, a sensitivity analysis using the Morris method was carried out on the GIS-based engineering model in order to assess the impact of input variables on space heating consumption and to identify possible optimization opportunities of the existing model.

Smart Building Energy Management: Load Profile Prediction using Machine Learning

2021 ◽  
Author(s):  
G. Revati ◽  
J. Hozefa ◽  
S. Shadab ◽  
A. Sheikh ◽  
S. R. Wagh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Management ◽  
Building Energy ◽  
Load Profile ◽  
Smart Building ◽  
Building Energy Management

Machine learning models for renewable energy forecasting

2020 ◽  
Vol 23 (1) ◽  
pp. 171-180
Author(s):  
Kusum Tharani ◽  
Neeraj Kumar ◽  
Vishal Srivastava ◽  
Sakshi Mishra ◽  
M. Pratyush Jayachandran
Keyword(s):  
Machine Learning ◽  
Renewable Energy ◽  
Learning Models ◽  
Energy Forecasting ◽  
Machine Learning Models

scholarly journals Application of Machine Learning for Predicting Building Energy Use at Different Temporal and Spatial Resolution under Climate Change in USA

Buildings ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 139 ◽  
Author(s):  
Rezvan Mohammadiziazi ◽  
Melissa M. Bilec
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Energy Use ◽  
Regression Tree ◽  
Building Energy ◽  
Gradient Boosting ◽  
Change Models ◽  
Building Energy Use ◽  
Extreme Gradient Boosting ◽  
Single Regression

Given the urgency of climate change, development of fast and reliable methods is essential to understand urban building energy use in the sector that accounts for 40% of total energy use in USA. Although machine learning (ML) methods may offer promise and are less difficult to develop, discrepancy in methods, results, and recommendations have emerged that requires attention. Existing research also shows inconsistencies related to integrating climate change models into energy modeling. To address these challenges, four models: random forest (RF), extreme gradient boosting (XGBoost), single regression tree, and multiple linear regression (MLR), were developed using the Commercial Building Energy Consumption Survey dataset to predict energy use intensity (EUI) under projected heating and cooling degree days by the Intergovernmental Panel on Climate Change (IPCC) across the USA during the 21st century. The RF model provided better performance and reduced the mean absolute error by 4%, 11%, and 12% compared to XGBoost, single regression tree, and MLR, respectively. Moreover, using the RF model for climate change analysis showed that office buildings’ EUI will increase between 8.9% to 63.1% compared to 2012 baseline for different geographic regions between 2030 and 2080. One region is projected to experience an EUI reduction of almost 1.5%. Finally, good data enhance the predicting ability of ML therefore, comprehensive regional building datasets are crucial to assess counteraction of building energy use in the face of climate change at finer spatial scale.

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