A review on applications of ANN and SVM for building electrical energy consumption forecasting

By A. S. Ahmad, M. Hassan, M. Abdullah, H. A. Rahman, F. Hussin, H. Abdullah, R. Saidur. 2014

891 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Management Science and Operations Research, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/ea951d6d9833a8b5c85def35d34f540c5b4771cb

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. Benjamin C. M. Fung
2. Ying Sun
3. F. Haghighat

In Management Science and Operations Research

1. Benjamin C. M. Fung
2. Philip S. Yu

In Renewable Energy, Sustainability and the Environment

1. F. Haghighat

Citations in the corpus

• MLaaS: Machine Learning as a Service (2015, methodology): “Predictive modeling approaches include: Artificial Neural Networks for energy consumption [2], Support Vector Machines for energy consumption [2] and KNearest Neighbors for wind power [3].”
• A review of the-state-of-the-art in data-driven approaches for building energy prediction (2020, background): “18 The advantages and disadvantages of ANN have been described in References [8] and [12].”
• A review of machine learning in building load prediction (2021, positive): “The paper written by Ahmad et al. [15] only focused on Artificial Neural Network (ANN) and Support Vector Machine (SVM).”