Utilization of intelligent control algorithms for thermal comfort optimization and energy saving

Nowak, Mariusz; Urbaniak, Andrzej

doi:10.1109/carpathiancc.2011.5945862

Cited by 21 publications

(8 citation statements)

References 2 publications

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“…Nowak and Urbaniak [106] modeled a room of a building in MATLAB/Simulink. The mathematical simulation model included physical parameters of construction elements like that of walls, floor and roof considering information on the room geometry, thermal properties of the room materials, thermal resistance of the room, heater characteristics (temperature of hot air, flow rate), air-conditioning characteristics and initial room temperature.…”

Section: Intrusive Data Nonintrusive Datamentioning

confidence: 99%

“…A hierarchical structure was developed by Nowak and Urbaniak in their works of [106][107][108][109][110]76,77] which comprised of four layers of the hierarchy, formulating a set of partial goals viz., direct control layer (responsible for security controlled process; classic PID and fuzzy logic), supervisory control layer (responsible for calculating fixed points for direct control layer; Predictive Control), optimization layer (responsible for realization of the three indexes to estimate thermal comfort) and planning layer (optimizing climate comfort maximization against energy consumption minimization).…”

Section: Intrusive Data Nonintrusive Datamentioning

confidence: 99%

“…In [106], predictive control algorithms were formulated defined by the process model related to control purposes, where the occupants thermal comfort sensation was given by PMV calculations. PMV values were calculated by using a nonlinear mathematical model of thermal comfort which was included in the cost function.…”

Section: Intrusive Data Nonintrusive Datamentioning

confidence: 99%

See 2 more Smart Citations

A review on modeling and simulation of building energy systems

Harish

Kumar

2016

Renewable and Sustainable Energy Reviews

540

186

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Section: Intrusive Data Nonintrusive Datamentioning

confidence: 99%

Section: Intrusive Data Nonintrusive Datamentioning

confidence: 99%

See 1 more Smart Citation

A review on modeling and simulation of building energy systems

Harish

Kumar

2016

Renewable and Sustainable Energy Reviews

540

186

View full text Add to dashboard Cite

“…They obtained the initial knowledge-base required by fuzzy logic controller from human experts and control engineering knowledge which they subsequently tuned by a genetic algorithm. In [17], a hierarchical structure for the control of an HVAC system using the Model Predictive Control (MPC) algorithms and fuzzy control algorithms has been proposed. The main task of the proposed hierarchical control system is to provide thermal comfort and minimize energy consumption.…”

Section: Related Workmentioning

confidence: 99%

Smart air-conditioning control by wireless sensors

Aftab¹,

Chau²,

Armstrong³

2013

Proceedings of the Fourth International Conference on Future Energy Systems

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One of the most prominent applications of smart technology for energy saving is in buildings, in particular, for optimizing heating, ventilation, and air-conditioning (HVAC) systems. Traditional HVAC systems rely on wired temperature regulators and thermostats installed at fixed locations, which are both inconvenient for deployment and ineffective to cope with dynamic changes in the thermal behavior of buildings. New generation of wireless sensors are increasingly becoming popular due to their convenience and versatility for sophisticated monitoring and control of smart buildings. However, there also emerge new challenges on how to effectively harness the potential of wireless sensors. First, wireless sensors are energy-constrained, because they are often powered by batteries. Extending the battery lifetime, therefore, is a paramount concern. The second challenge is to ensure that the wireless sensors can work in uncertain environments with minimal human supervision as they can be dynamically displaced in new environments. Therefore, in this paper, we study a fundamental problem of optimizing the trade-off between the battery lifetime and the effectiveness of HVAC remote control in the presence of uncertain (even adversarial) fluctuations in room temperature. We provide an effective offline algorithm for deciding the optimal control decisions of wireless sensors, and a 2-competitive online algorithm that is shown to attain performance close to offline optimal through extensive simulation studies. The implication of this work is to shed light on the fundamental trade-off optimization in wireless sensor controlling HVAC systems.

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“…In the same scenario, a more complex model simulating more thermal masses was developed in Refs , to perform thermal comfort analysis and energy performance predictions for different heating and cooling systems and various control strategies by including all the different components of the HVAC system (i.e., fans, pumps, networks, TUs, etc.). Moreover, the Matlab‐Simulink tool was often used to implement dynamic models able to predict the energy consumption and thermal performance of buildings with their systems included . Furthermore, lumped node building element models, where construction elements were broken up into different number of elements with uniform temperature, were developed .…”

Section: Dynamic Simulation Approaches At Single‐building Levelmentioning

confidence: 99%

Uses of dynamic simulation to predict thermal‐energy performance of buildings and districts: a review

Castaldo

Pisello

2017

WIREs Energy & Environment

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The design of sustainable constructions also resilient to climate change has become a challenging issue given the increasing greenhouse emissions rate imputable to the built environment in urban areas. In this context, dynamic simulation models represent a suitable tool to support the design from very preliminary phases, since they allow an accurate prediction of the constructions requirements, their environmental performance, and indoor comfort conditions for their occupants. Therefore, starting from specific inputs, that is, weather conditions, construction technologies, materials, energy systems, operation settings, occupancy, and so forth, it is possible to estimate the realistic building energy performance. Moreover, the calibration procedures allow making the model even more representative of the field conditions of a construction. Given the massive progress carried out by the scientific community during the last decades, this paper presents a comprehensive review of the different building dynamic simulation approaches and available tools. While previous review studies focused on single separated aspects of dynamic simulations approaches, that is, calibration methods, software, simulation of single building energy systems, the aim of the present review is to propose a more holistic approach by investigating the recent scientific progress in simulating realistic dense urban environments. In this view, the review focus bridges the gap between the simulation at single-building level and simulation at the increasingly important neighborhood scale by showing the multiple benefits deriving from using dynamic simulation tools at district level, for a more reliable investigation of building performance in their urban context, where more than 50% of the global population worldwide currently lives.

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Utilization of intelligent control algorithms for thermal comfort optimization and energy saving

Cited by 21 publications

References 2 publications

A review on modeling and simulation of building energy systems

A review on modeling and simulation of building energy systems

Smart air-conditioning control by wireless sensors

Uses of dynamic simulation to predict thermal‐energy performance of buildings and districts: a review

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