Continuously Learning Prediction Models for Smart Domestic Hot Water Management

Domestic hot water (DHW) consumption represents a significant portion of household energy usage, prompting the exploration of smart heat pump technology to efficiently meet DHW demands while minimizing energy waste. This paper proposes an innovative investigation of models using deep learning and co...

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Bibliographic Details
Published inEnergies (Basel) Vol. 17; no. 18; p. 4734
Main Authors Bayle, Raphaël, Reyboz, Marina, Lomet, Aurore, Cook, Victor, Mermillod, Martial
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2024
Subjects
Algorithms
Analysis
Computational linguistics
continual learning
Control systems
Data mining
Deep learning
domestic hot water management
Knowledge
Language processing
Learning strategies
Machine learning
Methods
Natural language interfaces
Neural networks
smart energy management
Time series
transformer
France
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Summary:Domestic hot water (DHW) consumption represents a significant portion of household energy usage, prompting the exploration of smart heat pump technology to efficiently meet DHW demands while minimizing energy waste. This paper proposes an innovative investigation of models using deep learning and continual learning algorithms to personalize DHW predictions of household occupants’ behavior. Such models, alongside a control system that decides when to heat, enable the development of a heat-pumped-based smart DHW production system, which can heat water only when needed and avoid energy loss due to the storage of hot water. Deep learning models, and attention-based models particularly, can be used to predict time series efficiently. However, they suffer from catastrophic forgetting, meaning that when they dynamically learn new patterns, older ones tend to be quickly forgotten. In this work, the continuous learning of DHW consumption prediction has been addressed by benchmarking proven continual learning methods on both real dwelling and synthetic DHW consumption data. Task-per-task analysis reveals, among the data from real dwellings that do not present explicit distribution changes, a gain compared to the non-evolutive model. Our experiment with synthetic data confirms that continual learning methods improve prediction performance.
ISSN:1996-1073
1996-1073
DOI:10.3390/en17184734