A review of data-driven approaches for prediction and classification of building energy consumption

By Yixuan Wei, Xingxing Zhang, Yong-lin Shi, L. Xia, S. Pan, Jinshun Wu, Mengjie Han, Xiaoyun Zhao. 2018

719 citations recorded by the source corpus.

Subfields: Building and Construction, Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/0dc3489175c395e2039129e276b40887f364598e

How researchers see this paper

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

In Electrical and Electronic Engineering

1. Ali Fiaz
2. Ali Ouni
3. Mohamed Adel Serhani
4. Salah Bouktif

Citations in the corpus

• A review of the-state-of-the-art in data-driven approaches for building energy prediction (2020, neutral): “[15] 2018 √ √ √ √ × √ √ × × √ √ × √ √ [16] 2018 √ × √ × √ √ √ × Clustering × × × × × [17] 2018 × × × √ × √ √ × Clustering × × Urban and rural × ×”
• Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches † (2018, background): “Data-driven approaches have addressed variety of energy prediction and load forecasting tasks and thus has attracted significant research attention [39].”
• A review of uncertainty analysis in building energy assessment (2018, background): “This literature review follows the concept-centric principle proposed by Webster and Watson [40] and also incorporates the strengths of reviews published in the field of building energy analysis [41-43].”