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

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 m...

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Bibliographic Details
Published in:Applied sciences 2021-09, Vol.11 (17), p.7991
Main Authors: Cordeiro-Costas, Moisés, Villanueva, Daniel, Eguía-Oller, Pablo
Format: Article
Language:English
Subjects:
Accuracy
Alternative energy sources
battery management system
building performance
Control algorithms
Cost control
Deep learning
demand response
Electric power demand
Electric vehicles
electrical energy storage
Electricity
electricity demand prediction
Emissions
Emissions control
Energy consumption
energy cost
Energy demand
Energy efficiency
Energy resources
Greenhouse effect
Greenhouse gases
Humidity
Learning algorithms
Linear programming
Machine learning
Mathematical models
Multilayer perceptrons
Neural networks
Office buildings
Optimization
Optimization techniques
Power management
Renewable resources
Storage
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app11177991