Next Article in Journal
Adaptative Cover to Achieve Thermal Comfort in Open Spaces of Buildings: Experimental Assessment and Modelling
Next Article in Special Issue
Optimization of Water Pressure of a Distribution Network within the Water–Energy Nexus
Previous Article in Journal
Ellipsoidal Path Planning for Unmanned Aerial Vehicles
 
 
Article

Optimization of the Electrical Demand of an Existing Building with Storage Management through Machine Learning Techniques

Industrial Engineering School, Universidade de Vigo, Rúa Maxwell s/n, 36310 Vigo, Spain
*
Author to whom correspondence should be addressed.
Academic Editors: Enrico Cagno, Pooya Davari, Edris Pouresmaeil and Mohsen Soltani
Appl. Sci. 2021, 11(17), 7991; https://doi.org/10.3390/app11177991
Received: 27 July 2021 / Revised: 25 August 2021 / Accepted: 27 August 2021 / Published: 29 August 2021
(This article belongs to the Special Issue 5th Anniversary of Energy Section—Recent Advances in Energy)
Accurate prediction from electricity demand models is helpful in controlling and optimizing building energy performance. The application of machine learning techniques to adjust the electrical consumption of buildings has been a growing trend in recent years. Battery management systems through the machine learning models allow a control of the supply, adapting the building demand to the possible changes that take place during the day, increasing the users’ comfort, and ensuring greenhouse gas emission reduction and an economic benefit. Thus, an intelligent system that defines whether the storage system should be charged according to the electrical needs of that moment and the prediction of the subsequent periods of time is defined. Favoring consumption in the building in periods when energy prices are cheaper or the renewable origin is preferable. The aim of this study was to obtain a building electrical energy demand model in order to be combined with storage devices with the purpose of reducing electricity expenses. Specifically, multilayer perceptron neural network models were applied, and the battery usage optimization is obtained through mathematical modelling. This approach was applied to a public office building located in Bangkok, Thailand. View Full-Text
Keywords: battery management system; building performance; demand response; electrical energy storage; electricity demand prediction; energy cost; machine learning; neural networks battery management system; building performance; demand response; electrical energy storage; electricity demand prediction; energy cost; machine learning; neural networks
Show Figures

Figure 1

MDPI and ACS Style

Cordeiro-Costas, M.; Villanueva, D.; Eguía-Oller, P. Optimization of the Electrical Demand of an Existing Building with Storage Management through Machine Learning Techniques. Appl. Sci. 2021, 11, 7991. https://doi.org/10.3390/app11177991

AMA Style

Cordeiro-Costas M, Villanueva D, Eguía-Oller P. Optimization of the Electrical Demand of an Existing Building with Storage Management through Machine Learning Techniques. Applied Sciences. 2021; 11(17):7991. https://doi.org/10.3390/app11177991

Chicago/Turabian Style

Cordeiro-Costas, Moisés, Daniel Villanueva, and Pablo Eguía-Oller. 2021. "Optimization of the Electrical Demand of an Existing Building with Storage Management through Machine Learning Techniques" Applied Sciences 11, no. 17: 7991. https://doi.org/10.3390/app11177991

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

Article Access Map by Country/Region

1
Back to TopTop