Design and implementation of a data-driven intelligent water heating system for an island community: A case study

Gough, Matthew; Rakhsia, Kush; Amaro, Hugo; Castro, Rui; Catalào, João P. S.

doi:10.1016/j.enconman.2023.117007

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…These measures can be aligned with utility demandresponse programmes, which reduce energy costs during off-peak hours. Additionally, advanced machine learning algorithms can forecast hot water use patterns and adjust the operation schedule of electrical water boilers accordingly, resulting in additional energy savings [104][105][106]. Control tactics and design have a considerable impact on the forecast of energy consumption by electrical water boilers in residential buildings.…”

Section: Design and Control Strategy Influence On Energy Consumption ...mentioning

confidence: 99%

“…Gough et al presented a smart residential water heating device using machine learning (ML) that optimises water boilers' operation by predicting hot water demand using machine learning [106]. This forecasting mechanism predicts hot water volume and consumers' unpredictable behaviour using only non-intrusive data, which is a major challenge.…”

Section: Authors Machine Learning Algorithm Aim and Goalsmentioning

confidence: 99%

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Electric Water Boiler Energy Prediction: State-of-the-Art Review of Influencing Factors, Techniques, and Future Directions

Kachalla,

Ghiaus

2024

Energies

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Accurate and efficient prediction of electric water boiler (EWB) energy consumption is significant for energy management, effective demand response, cost minimisation, and robust control strategies. Adequate tracking and prediction of user behaviour can enhance renewable energy mini-grid (REMD) management. Fulfilling these demands for predicting the energy consumption of electric water boilers (EWB) would facilitate the establishment of a new framework that can enhance precise predictions of energy consumption trends for energy efficiency and demand management, which necessitates this state-of-the-art review. This article first reviews the factors influencing the prediction of energy consumption of electric water boilers (EWB); subsequently, it conducts a critical review of the current approaches and methods for predicting electric water boiler (EWB) energy consumption for residential building applications; after that, the performance evaluation methods are discussed. Finally, research gaps are ascertained, and recommendations for future work are summarised.

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Section: Design and Control Strategy Influence On Energy Consumption ...mentioning

confidence: 99%

Section: Authors Machine Learning Algorithm Aim and Goalsmentioning

confidence: 99%