The IMBPC HVAC system: A complete MBPC solution for existing HVAC systems

Ruano, A. E.; Pesteh, Shabnam; Silva, Sérgio; Duarte, H.; Mestre, Gonçalo; Ferreira, Pedro; Khosravani, H.; Horta, Ricardo

doi:10.1016/j.enbuild.2016.03.043

Cited by 47 publications

(24 citation statements)

References 24 publications

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“…Regression models created from a recorded two week period indicate average estimated electricity savings of 12-17%. [Ruano et al 2016] uses neural networks and multi-objective optimization for HVAC operation to minimize economic cost while ensuring user comfort. The study takes into account indoor temperatures, schedule information, cost, and weather variables.…”

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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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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“…Artificial neural networks are trained with data and the understanding of the system is irrelevant [20]. Ruano et al (2016) investigated a predictive control in which a neural network predicts building and system behaviour and is optimised by including weather forecasts [21]. "Hybrid control techniques" deal with a combination of soft and hard control approaches [20].…”

Section: State Of Researchmentioning

confidence: 99%

Economic Multiple Model Predictive Control for HVAC Systems—A Case Study for a Food Manufacturer in Germany

et al. 2018

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The following paper describes an economical, multiple model predictive control (EMMPC) for an air conditioning system of a confectionery manufacturer in Germany. The application consists of a packaging hall for chocolate bars, in which a new local conveyor belt air conditioning system is used and thus the temperature and humidity limits in the hall can be significantly extended. The EMMPC calculates the optimum energy or cost humidity and temperature set points in the hall. For this purpose, time-discrete state space models and an economic objective function with which it is possible to react to flexible electricity prices in a cost-optimised manner are created. A possible future electricity price model for Germany with a flexible Renewable Energies levy (EEG levy) was used as a flexible electricity price. The flexibility potential is determined by variable temperature and humidity limits in the hall, which are oriented towards the comfort field for easily working persons, and the building mass. The building mass of the created room model is used as a thermal energy store. Considering the electricity price and weather forecasts as well as an internal, production plan-dependent load forecasts, the model predictive controller directly controls the heating and cooling register and the humidifier of the air conditioning system.

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“…These interfaces and the data acquisition use a small IOT platform, described in Section 4. The details of the control algorithm, as well as other results by IMBPC HVAC achieved in real-time control can be seen in [9], and are briefly summarized here.…”

Section: Softwarementioning

confidence: 99%

“…Despite the large number of publications on this topic, only a few report MBPC applications for existing buildings under normal occupancy conditions; one of the first was a previous work by the authors [8]. To the best of our knowledge, the first attempt towards a marketable MBPC HVAC solution was presented in our recent work [9].…”

Section: Introductionmentioning

confidence: 99%

“…During recent years, HVAC control has gradually changed from dealing with temperature regulation to being about occupants' thermal comfort [6,8,9,[15][16][17], mainly due to the fact that temperature is not the only factor affecting thermal comfort and that a constant temperature is not required for humans to achieve thermal comfort, and to the consequent potential for energy savings.…”

Section: Softwarementioning

confidence: 99%

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Wireless Sensors and IoT Platform for Intelligent HVAC Control

et al. 2018

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Abstract:Energy consumption of buildings (residential and non-residential) represents approximately 40% of total world electricity consumption, with half of this energy consumed by HVAC systems. Model-Based Predictive Control (MBPC) is perhaps the technique most often proposed for HVAC control, since it offers an enormous potential for energy savings. Despite the large number of papers on this topic during the last few years, there are only a few reported applications of the use of MBPC for existing buildings, under normal occupancy conditions and, to the best of our knowledge, no commercial solution yet. A marketable solution has been recently presented by the authors, coined the IMBPC HVAC system. This paper describes the design, prototyping and validation of two components of this integrated system, the Self-Powered Wireless Sensors and the IOT platform developed. Results for the use of IMBPC in a real building under normal occupation demonstrate savings in the electricity bill while maintaining thermal comfort during the whole occupation schedule.

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The IMBPC HVAC system: A complete MBPC solution for existing HVAC systems

Cited by 47 publications

References 24 publications

Cyber-Physical System for Energy-Efficient Stadium Operation

Cyber-Physical System for Energy-Efficient Stadium Operation

Economic Multiple Model Predictive Control for HVAC Systems—A Case Study for a Food Manufacturer in Germany

Wireless Sensors and IoT Platform for Intelligent HVAC Control

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