Comparative study of continuous hourly energy consumption forecasting strategies with small data sets to support demand management decisions in buildings-Reference-Cited by-同舟云学术

Comparative study of continuous hourly energy consumption forecasting strategies with small data sets to support demand management decisions in buildings

Published:2022-09-02 Issue:12 Volume:10 Page:4694-4707
ISSN:2050-0505
Container-title:Energy Science & Engineering
language:en
Short-container-title:Energy Science & Engineering

Author:

Mariano‐Hernández D.¹²^ORCID,Hernández‐Callejo L.²^ORCID,Solís M.³,Zorita‐Lamadrid A.⁴,Duque‐Pérez O.⁴,Gonzalez‐Morales L.⁵,Alonso‐Gómez V.⁶,Jaramillo‐Duque A.⁷,Santos García F.⁸

Affiliation:

1. Área de Ingeniería, Instituto Tecnológico de Santo Domingo Santo Domingo Dominican Republic

2. ADIRE‐ITAP, Departamento Ingeniería Agrícola y Forestal Universidad de Valladolid, Campus Universitario Duques de Soria Soria Spain

3. Tecnológico de Costa Rica Cartago Costa Rica

4. ADIRE‐ITAP, Departamento de Ingeniería Eléctrica Universidad de Valladolid Valladolid Spain

5. Departamento de Ingeniería Eléctrica, Electrónica y Telecomunicaciones—DEET Facultad de Ingeniería, Universidad de Cuenca Ecuador

6. Departamento de Física, Universidad de Valladolid Campus Universitario Duques de Soria Soria Spain

7. Grupo de Investigación en Manejo Eficiente de la Energía (GIMEL), Departamento de Ingeniería Eléctrica Universidad de Antioquia Medellín Colombia

8. Área de Ciencias Básicas, Instituto Tecnológico de Santo Domingo Santo Domingo Dominican Republic

Funder

CYTED Ciencia y Tecnología para el Desarrollo

Publisher

Wiley

Subject

General Energy,Safety, Risk, Reliability and Quality

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ese3.1298

Reference35 articles.

1. Analysis of electricity consumption profiles in public buildings with dimensionality reduction techniques

2. Data-driven hybrid model and operating algorithm to shave peak demand costs of building electricity

3. A review of strategies for building energy management system: Model predictive control, demand side management, optimization, and fault detect & diagnosis

4. Hourly energy consumption prediction of an office building based on ensemble learning and energy consumption pattern classification

5. BassiA ShenoyA SharmaA SigurdsonH GlossopC ChanJH. Building energy consumption forecasting: a comparison of gradient boosting models.ACM Int Conf Proc Ser. June 2021.doi:10.1145/3468784.3470656

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