Consumer cost savings, improved thermal comfort, and reduced peak air conditioning demand through pre-cooling in Australian housing

Naderi, Shayan; Heslop, Simon; Chen, Dong; MacGill, Iain; Pignatta, Gloria

doi:10.1016/j.enbuild.2022.112172

Cited by 14 publications

(1 citation statement)

References 31 publications

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“…The authors of [ 45 ] considered optimum pre-cooling to minimise voltage violation problems in a low voltage distribution network by shifting the air conditioning load to off-peak hours. It was concluded by the authors of [ 46 ] that the advantages of pre-cooling often rely on thermal comfort perceptions and the capacity of the air conditioning units. However, the aforementioned research papers assume that users can respond flexibly to changes in temperature within a given range.…”

Section: Introductionmentioning

confidence: 99%

Optimum Energy Management for Air Conditioners in IoT-Enabled Smart Home

Philip

Islam

Phillips³

et al. 2022

Sensors

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This paper addresses the optimal pre-cooling problem for air conditioners (AC) used in Internet of Things (IoT)-enabled smart homes while ensuring that user-defined thermal comfort can be achieved. The proposed strategy utilises renewable energy generation periods and moves some of the air conditioning loads to these periods to reduce the electricity demand. In particular, we propose a multi-stage approach which maximises the utilisation of renewable energy at the first stage to satisfy air conditioning loads, and then schedules residual energy consumption of these loads to low price periods at the second stage. The proposed approach is investigated for the temperature and renewable generation data of NSW, Australia, over the period 2012−2013. It is shown that the approach developed can significantly reduce the energy consumption and cost associated with AC operation for nearly all days in summer when cooling is required. Specifically, the proposed approach was found to achieve a 24% cost saving in comparison to the no pre-cooling case for the highest average temperature day in January, 2013. The analysis also demonstrated that the proposed scheme performed better when the thermal insulation levels in the smart home are higher. However, the optimal pre-cooling scheme can still achieve reduced energy costs under lower thermal insulation conditions compared to the no pre-cooling case.

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Section: Introductionmentioning

confidence: 99%