Model predictive control of building energy systems with thermal energy storage in response to occupancy variations and time-variant electricity prices

Lee, Doyun; Ooka, Ryozo; Ikeda, Sakuji; Choi, Wonjun; Kwak, Young Hoon

doi:10.1016/j.enbuild.2020.110291

Cited by 42 publications

(9 citation statements)

References 39 publications

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“…Settlement period (day, month, season); Holiday ; Seasonal variations (Lee et al 2020;Aslam et al 2022) Fuel cost is one of the main aspects of electricity generation, and fuel price changes thus have significant effects on spot electricity prices (Ohler et al 2020). At the same time, power demand is closely related to weather factors.…”

Section: Time Effectmentioning

confidence: 99%

Carbon tax vs. carbon market: Which one could better help China’s coal-based electricity industry achieve carbon neutrality?

Song

et al. 2023

Preprint

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This paper aims to investigate which carbon pricing mechanism, carbon tax or carbon market, could better promote the carbon neutrality of China’s coal-based electricity industry that indeed exerts significant impacts on climate change and industrial prosperity. By building a stochastic electricity price model, we reveal the price shock of both carbon tax and carbon market under different carbon pricing goals. Our key empirical results are as follows. First, both carbon tax and carbon market could significantly trigger the price volatility of coal-based electricity industry, while the shock impact of carbon market on such industry’s carbon emission is more significant than that of carbon tax. Second, by both carbon pricing mechanisms, carbon emission could be reduced up to 20%, and a key premise of achieving this goal is keeping carbon price at the level of 100 yuan/ton. Third, the volatility range of electricity price, which is policy-led, could not manifest the incentive impact of economic instruments on carbon emission reduction of coal-based electricity industry. Further, policy allows the upper limit of floating electricity price up to 15%. In general, we straighten out the linkage of carbon pricing tools and coal-based electricity costs, thereby constructing a carbon pricing mechanism for coal-based electricity industry to step into China’s carbon neutrality as soon as possible.

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Section: Time Effectmentioning

confidence: 99%

Carbon tax vs. carbon market: Which one could better help China’s coal-based electricity industry achieve carbon neutrality?

Song

et al. 2023

Preprint

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“…On the other hand, various energy saving frameworks have been built upon the use of model predictive control techniques, which deployed both modeling and simulation to design optimized building energy systems. This is the case of Reference [60], where a model predictive control scheme is developed to optimize energy saving in commercial buildings and detect abnormal energy usage. This was possible by considering end‐users' variations and time‐variant electricity prices.…”

Section: Related Workmentioning

confidence: 99%

Smart power consumption abnormality detection in buildings using micromoments and improved K‐nearest neighbors

et al. 2021

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Anomaly detection in energy consumption is a crucial step towards developing efficient energy saving systems, diminishing overall energy expenditure and reducing carbon emissions. Therefore, implementing powerful techniques to identify anomalous consumption in buildings and providing this information to end‐users and managers is of significant importance. Accordingly, two novel schemes are proposed in this paper; the first one is an unsupervised abnormality detection based on one‐class support vector machine, namely UAD‐OCSVM, in which abnormalities are extracted without the need of annotated data; the second is a supervised abnormality detection based on micromoments (SAD‐M2), which is implemented in the following steps: (i) normal and abnormal power consumptions are defined and assigned; (ii) a rule‐based algorithm is introduced to extract the micromoments representing the intent‐rich moments, in which the end‐users make decisions to consume energy; and (iii) an improved K‐nearest neighbors model is introduced to automatically classify consumption footprints as normal or abnormal. Empirical evaluation conducted in this framework under three different data sets demonstrates that SAD‐M2 achieves both a highest abnormality detection performance and real‐time processing capability with considerably lower computational cost in comparison with other machine learning methods. For instance, up to 99.71% accuracy and 99.77% F1 score have been achieved using a real‐world data set collected at the Qatar University energy lab.

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“…The most cited paper included in commercial buildings was published by Braun et al [49] and Henze et al [53] on the optimal control of cold TES to reduce the electricity demand used for cooling in commercial buildings. The use of artificial intelligence and the application of demand side management strategies still represents the trend of the recent studies on TES applied to commercial buildings [81][82][83][84][85][86][87].…”

Section: Buildingsmentioning

confidence: 99%

Recent developments of thermal energy storage applications in the built environment: A bibliometric analysis and systematic review

Borri

Zsembinszki

Cabeza

2021

Applied Thermal Engineering

100

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Model predictive control of building energy systems with thermal energy storage in response to occupancy variations and time-variant electricity prices

Cited by 42 publications

References 39 publications

Carbon tax vs. carbon market: Which one could better help China’s coal-based electricity industry achieve carbon neutrality?

Carbon tax vs. carbon market: Which one could better help China’s coal-based electricity industry achieve carbon neutrality?

Smart power consumption abnormality detection in buildings using micromoments and improved K‐nearest neighbors

Recent developments of thermal energy storage applications in the built environment: A bibliometric analysis and systematic review

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