Explainable long-term building energy consumption prediction using QLattice

Author:

Wenninger Simon,Kaymakci Can,Wiethe Christian

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Mechanical Engineering,General Energy,Building and Construction

Reference66 articles.

1. Energy systems for climate change mitigation: a systematic review;Kang;Appl Energy,2020

2. Boden T, Andres R, Marland G. Global, Regional, and National Fossil-Fuel CO2 Emissions (1751 - 2014) (V. 2017): Environmental System Science Data Infrastructure for a Virtual Ecosystem; Carbon Dioxide Information Analysis Center (CDIAC), Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); 2017.

3. European Environment Agency (EEA). Europe’s state of the environment 2020: change of direction urgently needed to face climate change challenges, reverse degradation and ensure future prosperity. [June 07, 2020]; Available from: https://www.eea.europa.eu/highlights/soer2020-europes-environment-state-and-outlook-report.

4. Somu N, M R GR, Ramamritham K. A hybrid model for building energy consumption forecasting using long short term memory networks. Appl Energy 2020;261:114131.

5. European Parliament and the Council. Directive 2002/91/EC of the European Parliament and of the Council of 16 December 2002 on the energy performance of buildings; 2002.

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