Next Article in Journal
A Sub-Zonal PMV-Based HVAC and Façade Control System for Curtain Wall Buildings
Previous Article in Journal
Smart Energy Management for Unlocking Demand Response in the Residential Sector
Article Menu

Article Versions

Export Article

Open AccessProceedings
Proceedings 2018, 2(15), 1133; https://doi.org/10.3390/proceedings2151133

Forecasting Algorithms and Optimization Strategies for Building Energy Management & Demand Response

School of Engineering, Cardiff University, Cardiff CF24 3AA, UK
Presented at the Sustainable Places 2018 (SP 2018), Aix-les Bains 73100, France 27–29 June 2018.
*
Author to whom correspondence should be addressed.
Published: 27 August 2018
PDF [204 KB, uploaded 27 August 2018]

Abstract

In this paper, we look at the key forecasting algorithms and optimization strategies for the building energy management and demand response management. By conducting a combined and critical review of forecast learning algorithms and optimization models/algorithms, current research gaps and future research directions and potential technical routes are identified. To be more specific, ensemble/hybrid machine learning algorithms and deep machine learning algorithms are promising in solving challenging energy forecasting problems while large-scale and distributed optimization algorithms are the future research directions for energy optimization in the context of smart buildings and smart grids.
Keywords: building energy management; demand response; demand-side management; energy forecasting; energy optimization building energy management; demand response; demand-side management; energy forecasting; energy optimization
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Meng, F.; Weng, K.; Shallal, B.; Chen, X.; Mourshed, M. Forecasting Algorithms and Optimization Strategies for Building Energy Management & Demand Response. Proceedings 2018, 2, 1133.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Proceedings EISSN 2504-3900 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top