Multiple regression models to predict the annual energy consumption in the Spanish banking sector

By A. Aranda, G. Ferreira, M. D. Mainar-Toledo, Sabina Scarpellini, Eva Llera Sastresa. 2012

138 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/c42a1aed6d6c3d31d2546633d578a7670eb174ec

How researchers see this paper

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

In Environmental Engineering

1. Zhanyong Li
2. D. Yan
3. Yeonsook Heo
4. W. Tian
5. Hyongdoo Jang
6. Cheolsoo Park
7. Xiaohang Feng
8. Erkan Topal

In Renewable Energy, Sustainability and the Environment

1. Hyongdoo Jang
2. Erkan Topal
3. Gokhan Aydin

Citations in the corpus

• Energy consumption modeling using artificial neural networks: The case of the world’s highest consumers (2016, methodology): “In this study, the real value of total data is used rather than per capita-based data as recommended by Pao and Tsai (2011), Friedl and Getzner (2003), and Aranda et al. (2012).”
• A review of uncertainty analysis in building energy assessment (2018, background): “[164] Aranda A, Ferreira G, Mainar-Toledo MD, Scarpellini S, Llera Sastresa E.”
• Hourly cooling load forecasting using time-indexed ARX models with two-stage weighted least squares regression (2014, background): “In particular, cooling load depends on many external factors of a cooling system, such as outdoor weather parameters, indoor equipment usage, and indoor human activities [4,5].”