Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Machine Learning for Building Energy Forecasting
https://orcid.org/https://orcid.org/0000-0001-6017-289X
https://orcid.org/https://orcid.org/0000-0001-7443-4723
In recent years, Artificial Intelligence (AI) in general and Machine Learning (ML) techniques in specific terms have been proposed for forecasting of building energy consumption and performance. This chapter provides a substantial review on the four main ML approaches including artificial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance.
Machine Learning for Building Energy Forecasting
https://orcid.org/https://orcid.org/0000-0001-6017-289X
https://orcid.org/https://orcid.org/0000-0001-7443-4723
In recent years, Artificial Intelligence (AI) in general and Machine Learning (ML) techniques in specific terms have been proposed for forecasting of building energy consumption and performance. This chapter provides a substantial review on the four main ML approaches including artificial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance.
Machine Learning for Building Energy Forecasting
https://orcid.org/https://orcid.org/0000-0001-6017-289X
https://orcid.org/https://orcid.org/0000-0001-7443-4723
In recent years, Artificial Intelligence (AI) in general and Machine Learning (ML) techniques in specific terms have been proposed for forecasting of building energy consumption and performance. This chapter provides a substantial review on the four main ML approaches including artificial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance.
Machine Learning for Building Energy Forecasting
Green Energy,Technology
Seyedzadeh, Saleh (Autor:in) / Pour Rahimian, Farzad (Autor:in)
Data-Driven Modelling of Non-Domestic Buildings Energy Performance ; Kapitel: 4 ; 41-76
16.01.2021
36 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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