Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: A review
By Mohammad Azhar Mat Daut, M. Hassan, H. Abdullah, H. A. Rahman, P. Abdullah, F. Hussin. 2017
300 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/e5f83522506dab770653edb9f777566f8e53a114
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Building and Construction
In Electrical and Electronic Engineering
In Renewable Energy, Sustainability and the Environment
Citations in the corpus
- A review of the-state-of-the-art in data-driven approaches for building energy prediction (2020, background): “[10] 2017 √ × √ √ × √ √ × × × × √ √ × [11] 2017 √ √ √ √ √ √ √ × × Commercial √ × × × [12] 2017 × × √ √ × √ √ Hybrid × × × × × × [13] 2017 √ × √ × × √ √ √ × √ √ × √ × [14] 2017 × × × √ × √ √ Hybrid Fuzzy time series; moving average and exponential smoothing, kNearest Neighbor (kNN) × × × × ×”