Multi-objective optimization of the HVAC (heating, ventilation, and air conditioning) system performance

Wei, Xiupeng; Kusiak, Andrew; Li, Mingyang; Tang, Fan; Zeng, Yaohui

doi:10.1016/j.energy.2015.02.024

Cited by 120 publications

(36 citation statements)

References 27 publications

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“…Wei at al. carried out a multi-objective optimization of a heating, ventilation and air conditioning (HVAC) system performance using a data-driven approach [33,34]. The multi-layer perceptron ensemble approach was used to build accurate models that considered both energy consumption and local environmental conditions such as air quality and CO 2 levels.…”

Section: Baseline Energy Modellingmentioning

confidence: 99%

Development and application of a machine learning supported methodology for measurement and verification (M&V) 2.0

Gallagher

Leahy

O’Donovan

et al. 2018

Energy and Buildings

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The foundations of all methodologies for the measurement and verification (M&V) of energy savings are based on the same five key principles: accuracy, completeness, conservatism, consistency and transparency. The most widely accepted methodologies tend to generalise M&V so as to ensure applicability across the spectrum of energy conservation measures (ECM's). These do not provide a rigid calculation procedure to follow. This paper aims to bridge the gap between high-level methodologies and the practical application of modelling algorithms, with a particular focus on the industrial buildings sector. This is achieved with the development of a novel, machine learning supported methodology for M&V 2.0 which enables accurate and reliable quantification of savings.A novel and computationally efficient feature selection algorithm and powerful machine learning regression algorithms are employed to maximise the effectiveness of available data. The baseline period energy consumption is modelled using artificial neural networks, support vector machines, k-nearest neighbours and multiple ordinary least squares regression. Improved knowledge discovery and an expanded boundary of analysis allow more complex energy systems be analysed, thus increasing the applicability of M&V. A case study in a large biomedical manufacturing facility is used to demonstrate the methodology's ability to accurately quantify the savings under real-world conditions. The ECM was found to result in 604,527 kWh of energy savings with 57% uncertainty at a confidence interval of 68%. 20 baseline energy models are developed using an exhaustive approach with the optimal model being used to quantify savings. The range of savings estimated with each model are presented and the acceptability of uncertainty is reviewed. The case study demonstrates the ability of the methodology to perform M&V to an acceptable standard in challenging circumstances.

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Section: Baseline Energy Modellingmentioning

confidence: 99%

Development and application of a machine learning supported methodology for measurement and verification (M&V) 2.0

Gallagher

Leahy

O’Donovan

et al. 2018

Energy and Buildings

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show abstract

“…As outlined in the following, current research on predictive building control strategies achieves high increases of performance by relying on predictive models learned from sensor data. [Wei et al 2015] optimizes the operation of a multi-zone Heating, Ventilation and Air Conditioning (HVAC) system for room temperature and energy consumption, taking relative humidity, room temperature and indoor CO 2 levels as the input. Compared to seven other regression models, a neural network ensemble performed best.…”

Section: Related Work Relation To Earlier Workmentioning

confidence: 99%

Cyber-Physical System for Energy-Efficient Stadium Operation

Schmidt

Schülke²,

Venturi³

et al. 2018

ACM Trans. Cyber-Phys. Syst.

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The environmental impacts of medium to large scale buildings receive substantial attention in research, industry, and media. This paper studies the energy savings potential of a commercial soccer stadium during day-to-day operation. Buildings of this kind are characterized by special purpose system installations like grass heating systems and by event-driven usage patterns. This work presents a methodology to holistically analyze the stadium's characteristics and integrate its existing instrumentation into a Cyber-Physical System, enabling to deploy different control strategies flexibly. In total, seven different strategies for controlling the studied stadium's grass heating system are developed and tested in operation. Experiments in winter season 2014/2015 validated the strategies' impacts within the real operational setup of the Commerzbank Arena, Frankfurt, Germany. With 95% confidence, these experiments saved up to 66% of median daily weather-normalized energy consumption. Extrapolated to an average heating season, this corresponds to savings of 775 MWh and 148 t of CO 2 emissions. In winter 2015/2016 an additional predictive nighttime heating experiment targeted lower temperatures, which increased the savings to up to 85%, equivalent to 1 GWh (197 t CO 2 ) in an average winter. Beyond achieving significant energy savings, the different control strategies also met the target temperature levels to the satisfaction of the stadium's operational staff. While the case study constitutes a significant part, the discussions dedicated to the transferability of this work to other stadiums and other building types show that the concepts and the approach are of general nature. Furthermore, this work demonstrates the first successful application of Deep Belief Networks to regress and predict the thermal evolution of building systems.

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“…However, not too many researches were found applying those complex controllers for HVAC to the ACSs in data centers. Since the multi-objective optimization problem to be solved in this paper is a non-convex and nonlinear problem, random optimization approaches such as simulated annealing [44], genetic algorithm (GA) [45], particle-swarm optimization (PSO) [46,47], and evolutionary algorithm (EA) [48] are commonly utilized. The non-dominated sorting genetic algorithm II (NSGA-II) [49] is applied in this paper for multi-objective optimization.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Energy Saving Control for Air Conditioning System in Data Center

Yao

Huang

2019

Energies

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A multi-objective optimization scheme is proposed to save energy for a data center air conditioning system (ACS). Since the air handling units (AHU) and chillers are the most energy consuming facilities, the proposed energy saving control scheme aims to maximize the saved energy for these two facilities. However, the rack intake air temperature tends to increase if the energy saving control scheme applied to AHU and chillers is conducted inappropriately. Both ACS energy consumption and rack intake air temperature stabilization are set as two objectives for multi-objective optimization. The non-dominated sorting genetic algorithm II (NSGA-II) is utilized to solve the multi-objective optimization problem. In order for the NSGA-II to evaluate fitness functions that are both the ACS total power consumption and AHU outlet cold air temperature deviations from a specified range, neural network models are utilized. Feedforward neural networks are utilized to learn the power consumption models for both chillers and AHUs as well as the AHU outlet cold air temperature based on the recorded data collected in the field. The effectiveness and efficiency of the proposed energy saving control scheme is verified through practical experiments conducted on a campus data center ACS.

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Multi-objective optimization of the HVAC (heating, ventilation, and air conditioning) system performance

Cited by 120 publications

References 27 publications

Development and application of a machine learning supported methodology for measurement and verification (M&V) 2.0

Development and application of a machine learning supported methodology for measurement and verification (M&V) 2.0

Cyber-Physical System for Energy-Efficient Stadium Operation

Multi-Objective Optimization of Energy Saving Control for Air Conditioning System in Data Center

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