A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Machine Learning Models for Prediction of Building Energy Performance
https://orcid.org/https://orcid.org/0000-0001-6017-289X
https://orcid.org/https://orcid.org/0000-0001-7443-4723
This chapter investigates the accuracy of most popular ML models in the prediction of building heating and cooling loads carrying out specific tuning for each ML model and using two simulated building energy data. The use of grid search coupled with cross-validation method in examination of the model parameters is demonstrated. Furthermore, sensitivity analysis techniques are used to evaluate the importance of input variables on the performance of ML models. The accuracy and time complexity of models in predicting heating and cooling loads are demonstrated.
Machine Learning Models for Prediction of Building Energy Performance
https://orcid.org/https://orcid.org/0000-0001-6017-289X
https://orcid.org/https://orcid.org/0000-0001-7443-4723
This chapter investigates the accuracy of most popular ML models in the prediction of building heating and cooling loads carrying out specific tuning for each ML model and using two simulated building energy data. The use of grid search coupled with cross-validation method in examination of the model parameters is demonstrated. Furthermore, sensitivity analysis techniques are used to evaluate the importance of input variables on the performance of ML models. The accuracy and time complexity of models in predicting heating and cooling loads are demonstrated.
Machine Learning Models for Prediction of Building Energy Performance
https://orcid.org/https://orcid.org/0000-0001-6017-289X
https://orcid.org/https://orcid.org/0000-0001-7443-4723
This chapter investigates the accuracy of most popular ML models in the prediction of building heating and cooling loads carrying out specific tuning for each ML model and using two simulated building energy data. The use of grid search coupled with cross-validation method in examination of the model parameters is demonstrated. Furthermore, sensitivity analysis techniques are used to evaluate the importance of input variables on the performance of ML models. The accuracy and time complexity of models in predicting heating and cooling loads are demonstrated.
Machine Learning Models for Prediction of Building Energy Performance
Green Energy,Technology
Seyedzadeh, Saleh (author) / Pour Rahimian, Farzad (author)
Data-Driven Modelling of Non-Domestic Buildings Energy Performance ; Chapter: 5 ; 77-98
2021-01-16
22 pages
Article/Chapter (Book)
Electronic Resource
English
A Review on Application of Machine Learning in Building Performance Prediction
| Springer Verlag | 2022