Passive versus active learning in operation and adaptive maintenance of Heating, Ventilation, and Air Conditioning

Baldi, Simone; Zhang, Fan; Quang, Thuan Le; Endel, Petr; Holub, Ondřej

doi:10.1016/j.apenergy.2019.113478

Cited by 19 publications

(5 citation statements)

References 53 publications

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“…Wang et al [35] have proposed a new method called event-driven optimal control for HVAC systems, in which optimization actions are triggered by events instead of a clock. Simone Baldi et al [36] have proposed a holistic framework for HVAC systems with energy-aware and comfort-driven maintenance. Baldi et al [37] have proposed a switched self-tuning approach to solve a multiple-mode feedback-based optimal control problem for HVAC systems.…”

Section: Related Workmentioning

confidence: 99%

HVAC Optimal Control with the Multistep-Actor Critic Algorithm in Large Action Spaces

Huang

Chen

et al. 2020

Mathematical Problems in Engineering

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We propose an optimization method, named as the Multistep-Actor Critic (MAC) algorithm, which uses the value-network and the action-network, where the action-network is based on the deep Q-network (DQN). The proposed method is intended to solve the problem of energy conservation optimization of heating, ventilating, and air-conditioning (HVAC) system in a large action space, principally for the cases with high computation and convergence time. The method employs the multistep action-network and search tree to generate the original state and then selects the optimal state based on the value-network for the original and the adjacent states. The results from the application of the MAC algorithm to a simulation problem on the TRNSYS system, where the simulation problem is referring to a real supertall building in Hong Kong, have shown that the proposed MAC algorithm balances control actions between different HVAC subsystems. Further, it substantially saves the computational time while maintaining a good energy conservation performance.

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Section: Related Workmentioning

confidence: 99%

HVAC Optimal Control with the Multistep-Actor Critic Algorithm in Large Action Spaces

Huang

Chen

et al. 2020

Mathematical Problems in Engineering

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“…Towards the optimal use of energy resources and a sustainable environment, the deployment of methodologies related to energy efficiency in the industry has been promoted in recent decades [1]. In addition, proper maintenance of industrial equipment reduces energy waste and production costs in the industrial sector [2,3]. Load monitoring leads to an understanding of the energy consumption of specific appliances/equipment, which allows implementing energy efficiency methodologies as well as early detection of anomalies and measurement of equipment degeneration [4].…”

Section: Introductionmentioning

confidence: 99%

Data-driven energy prediction modeling for both energy efficiency and maintenance in smart manufacturing systems

Bermeo-Ayerbe

Ocampo‐Martínez

Díaz-Rozo³

2022

Energy

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“…In first-principles models, the governing laws of physics are used to derive a set of mathematical equations of the system. [3][4][5][6] These models are also utilized in model-based control strategies such as model predictive control (MPC). [7] Although first-principles models have good extrapolation capabilities, they are generally difficult to develop and maintain because of uncertainty in the physical parameters and unmeasured physical states.…”

Section: Introductionmentioning

confidence: 99%

A hybrid machine learning approach integrating recurrent neural networks with subspace identification for modelling HVAC systems

2022

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This paper addresses the problem of system identification for heating, ventilation, and air conditioning (HVAC) systems using a relatively small amount of data for the zone under consideration, by leveraging larger datasets for similar zones. To this end, a hybrid machine learning approach is developed where a pre‐trained recurrent neural network (RNN) model, trained on a large amount of data from a representative zone, is leveraged to build models for the other zones using a smaller amount of data. This is achieved by developing a hybrid model that integrates the pre‐trained RNN model with the models built using the subspace identification (SubID) technique to predict the residuals (differences between the real outputs and the predicted outputs from the pre‐trained RNN model) in the other zones. The effectiveness of the proposed hybrid approach is shown using real data collected from a multi‐zone fitness centre. The results demonstrate the superior performance of the hybrid approach over the cases where individual RNN and SubID models are directly developed using only the data from the zones in question.

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Passive versus active learning in operation and adaptive maintenance of Heating, Ventilation, and Air Conditioning

Cited by 19 publications

References 53 publications

HVAC Optimal Control with the Multistep-Actor Critic Algorithm in Large Action Spaces

HVAC Optimal Control with the Multistep-Actor Critic Algorithm in Large Action Spaces

Data-driven energy prediction modeling for both energy efficiency and maintenance in smart manufacturing systems

A hybrid machine learning approach integrating recurrent neural networks with subspace identification for modelling HVAC systems

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