Evaluation of advanced control strategies for building energy systems

Stoffel, Phillip; Maier, Laura; Kümpel, Alexander; Schreiber, Thomas; Müller, Dirk

doi:10.1016/j.enbuild.2022.112709

Cited by 29 publications

(10 citation statements)

References 82 publications

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“…In [11], Stoffel et al conduct a comparative study of advanced operating strategies including different MPC, reinforement learning as well as approximate MPC approaches. The approaches are applied to the example of a ventilation system and compared with regard to energy savings as well as qualitative criteria.…”

Section: Approximate Mpcmentioning

confidence: 99%

Systematic Development of application-oriented Operating Strategies at the Example of an Industrial Heating Supply System

Theisinger,

Frank,

Weigold

2024

Preprint

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The ongoing challenge to ensure a sustainable and affordable energy supply forces industrial companies to implement appropriate measures. These measures typically lead to an increased system complexity, which has to be addressed during operation. Whereas existing approaches from the field of supervisory and optimal control appear to be capable of mastering this issue, these are still not adopted in industry. Therefore, this work presents a procedure model for the systematic development of application-oriented operating strategies for industrial energy supply systems. The procedure model combines research approaches from the fields of sequencing control and approximate MPC to extract rule-based operating strategies. By splitting the procedure model into five phases, expert knowledge can be integrated in a target-oriented manner. Subsequently to the description, the procedure model is exemplary applied to the example of an industrial heating supply system. Throughout an optimization study, the developed operating strategy is compared to a MPC strategy as well as a baseline strategy. Whereas the conventional MPC approach is the upper bound with regard to optimality, the developed operating strategy is able to generate comparable results. Compared to the baseline strategy, a relative reduction in operating expenses of 5.4 % to 37.0 % are achieved in this specific use case.

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Section: Approximate Mpcmentioning

confidence: 99%

Systematic Development of application-oriented Operating Strategies at the Example of an Industrial Heating Supply System

Theisinger,

Frank,

Weigold

2024

Preprint

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“…These methods typically involve the use of qualitative information, rules, or heuristics derived from prior knowledge or observations of the system's behavior. They can be classified into three main categories: qualitative simulation methods [12,13], graph theory methods [14,15], and expert system methods [16,17]. However, these methods may struggle to adapt…”

Section: Introductionmentioning

confidence: 99%

Process monitoring in hybrid electric vehicles based on dynamic nonlinear method

Wang,

Deprizon,

Kit

et al. 2024

J Appl Eng Science

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Highway third-level faults can significantly deteriorate the reliability and performance of hybrid electric vehicle (HEV) powertrains. This study presents a novel process monitoring method aimed at addressing this issue. We propose a multivariate statistical method based on dynamic nonlinear improvement, namely dynamic neural component analysis (DNCA). This method does not require the establishment of precise analytical models; instead, it only necessitates acquiring data from HEV powertrains. Through numerical simulation and real HEV experiments, we demonstrate the effectiveness of this approach in monitoring highway third-level faults. The testing outcomes demonstrate that DNCA outperforms traditional dynamic methods like dynamic principal component analysis (DPCA), conventional nonlinear methods such as kernel PCA (KPCA) and NCA, as well as traditional dynamic nonlinear methods like DKPCA.

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“…In addition, advanced controls differ from traditional controls in terms of control logic. Advanced control incorporates dynamic and adaptive strategies that utilize realtime data and are optimized using advanced algorithms, which enables personalized adjustments, proactive decision-making and continuous optimization [17] to achieve higher levels of energy efficiency and occupant comfort in SEE buildings [18,19]. Advanced controls play a key role in enabling SEE buildings to achieve these goals by providing the intelligence and automation for energy management in buildings [20,21].…”

Section: Introductionmentioning

confidence: 99%

Advanced controls on energy reliability, flexibility and occupant-centric control for smart and energy-efficient buildings

Liu

Zhang

Sun

et al. 2023

Energy and Buildings

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Evaluation of advanced control strategies for building energy systems

Cited by 29 publications

References 82 publications

Systematic Development of application-oriented Operating Strategies at the Example of an Industrial Heating Supply System

Systematic Development of application-oriented Operating Strategies at the Example of an Industrial Heating Supply System

Process monitoring in hybrid electric vehicles based on dynamic nonlinear method

Advanced controls on energy reliability, flexibility and occupant-centric control for smart and energy-efficient buildings

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