A comprehensive overview on the data driven and large scale based approaches for forecasting of building energy demand: A review

By Tanveer Ahmad, Huanxin Chen, Yabin Guo, Jiangyu Wang. 2018

314 citations recorded by the source corpus.

Subfields: Building and Construction, Electrical and Electronic Engineering, Environmental Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Xiaofeng Guo
2. Elyes Nefzaoui
3. Patrice Chatellier
4. Mathieu Bourdeau
5. X. Zhai
6. Ying Sun

Citations in the corpus

• Explainable Artificial Intelligence (XAI) (2022, neutral): “While these techniques contribute significantly to the XAI landscape, ongoing research explores novel approaches and the integration of multiple methods to enhance both the depth and breadth of model explanations.”
• A review of the-state-of-the-art in data-driven approaches for building energy prediction (2020, unknown): “[15] 2018 √ √ √ √ × √ √ × × √ √ × √ √ [16] 2018 √ × √ × √ √ √ × Clustering × × × × × [17] 2018 × × × √ × √ √ × Clustering × × Urban and rural × ×”
• Modeling and forecasting building energy consumption: A review of data-driven techniques (2019, methodology): “Ensemble models have gained in interest for BECMF in the past few years.”