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Manfred Morari - One of the best experts on this subject based on the ideXlab platform.

  • scenario based mpc for energy efficient Building climate control under weather and occupancy uncertainty
    European Control Conference, 2013
    Co-Authors: Xiaojing Zhang, Georg Schildbach, David Sturzenegger, Manfred Morari
    Abstract:

    Heating, ventilation and air conditioning (HVAC) systems regulate comfort levels in Buildings, but also consume a large amount of energy, which makes them an attractive target for efficiency improvements. In this paper, a novel technique called Randomized Model Predictive Control (RMPC) is investigated to improve the control of existing HVAC systems. RMPC uses weather and occupancy predictions to minimize the Building's energy consumption. It accounts for the prediction uncertainties by basing its control actions on a given number of sampled uncertainty scenarios. The main advantage of RMPC over existing methods is the absence of a probabilistic disturbance model. This makes the handling of uncertainties straightforward, even if they are non-Gaussian or non-additive. Moreover, the method of removing adverse samples after solving the initial control problem (RMPC-SR) can lead to a further improvement in the control performance, up to a saturation limit. Although theoretical bounds for choosing the sample sizes are available, our simulations show that only a fraction of these numbers is required for a good performance of RMPC and RMPC-SR. The performance of RMPC and RMPC-SR is investigated through extensive simulations on different models, based on empirically collected data. The results demonstrate that both techniques are attractive alternatives to other Model Predictive Control methods, because they show a higher energy saving potential, and are computationally tractable.

  • use of model predictive control and weather forecasts for energy efficient Building climate control
    Energy and Buildings, 2012
    Co-Authors: Frauke Oldewurtel, Alessandra Parisio, Colin N Jones, Dimitrios Gyalistras, Markus Gwerder, Vanessa Stauch, Beat Lehmann, Manfred Morari
    Abstract:

    This paper presents an investigation of how ModelPredictiveControl (MPC) and weatherpredictions can increase the energy efficiency in Integrated Room Automation (IRA) while respecting occupant comfort. IRA deals with the simultaneous control of heating, ventilation and air conditioning (HVAC) as well as blind positioning and electric lighting of a Building zone such that the room temperature as well as CO2 and luminance levels stay within given comfort ranges. MPC is an advanced control technique which, when applied to Buildings, employs a model of the Building dynamics and solves an optimization problem to determine the optimal control inputs. In this paper it is reported on the development and analysis of a Stochastic ModelPredictiveControl (SMPC) strategy for Buildingclimatecontrol that takes into account the uncertainty due to the use of weatherpredictions. As first step the potential of MPC was assessed by means of a large-scale factorial simulation study that considered different types of Buildings and HVAC systems at four representative European sites. Then for selected representative cases the control performance of SMPC, the impact of the accuracy of weatherpredictions, as well as the tunability of SMPC were investigated. The findings suggest that SMPC outperforms current control practice.

  • use of model predictive control and weather forecasts for energy efficient Building climate control
    Energy and Buildings, 2012
    Co-Authors: Frauke Oldewurtel, Alessandra Parisio, Colin N Jones, Dimitrios Gyalistras, Markus Gwerder, Vanessa Stauch, Beat Lehmann, Manfred Morari
    Abstract:

    This paper presents an investigation of how ModelPredictiveControl (MPC) and weatherpredictions can increase the energy efficiency in Integrated Room Automation (IRA) while respecting occupant comfort. IRA deals with the simultaneous control of heating, ventilation and air conditioning (HVAC) as well as blind positioning and electric lighting of a Building zone such that the room temperature as well as CO2 and luminance levels stay within given comfort ranges. MPC is an advanced control technique which, when applied to Buildings, employs a model of the Building dynamics and solves an optimization problem to determine the optimal control inputs. In this paper it is reported on the development and analysis of a Stochastic ModelPredictiveControl (SMPC) strategy for Buildingclimatecontrol that takes into account the uncertainty due to the use of weatherpredictions. As first step the potential of MPC was assessed by means of a large-scale factorial simulation study that considered different types of Buildings and HVAC systems at four representative European sites. Then for selected representative cases the control performance of SMPC, the impact of the accuracy of weatherpredictions, as well as the tunability of SMPC were investigated. The findings suggest that SMPC outperforms current control practice.

  • energy efficient Building climate control using stochastic model predictive control and weather predictions
    American Control Conference, 2010
    Co-Authors: Frauke Oldewurtel, Alessandra Parisio, Colin N Jones, Manfred Morari, Dimitrios Gyalistras, Markus Gwerder, Vanessa Stauch, Beat Lehmann, Katharina Wirth
    Abstract:

    One of the most critical challenges facing society today is climate change and thus the need to realize massive energy savings. Since Buildings account for about 40% of global final energy use, energy efficient Building climate control can have an important contribution. In this paper we develop and analyze a Stochastic Model Predictive Control (SMPC) strategy for Building climate control that takes into account weather predictions to increase energy efficiency while respecting constraints resulting from desired occupant comfort. We investigate a bilinear model under stochastic uncertainty with probabilistic, time varying constraints. We report on the assessment of this control strategy in a large-scale simulation study where the control performance with different Building variants and under different weather conditions is studied. For selected cases the SMPC approach is analyzed in detail and shown to significantly outperform current control practice.

Alain Guiavarch - One of the best experts on this subject based on the ideXlab platform.

  • eco design of Buildings using thermal simulation and life cycle assessment
    Journal of Cleaner Production, 2013
    Co-Authors: Bruno Peuportier, Stephane Thiers, Alain Guiavarch
    Abstract:

    Energy efficient Building are designed to minimize heating, cooling and lighting energy loads, so that attention is now paid on the energy consumption related to inhabitants (e.g. use of appliances) and life cycle issues: fabrication of materials, construction, maintenance, dismantling and waste treatment. In order to study these aspects, both in new construction and renovation projects, thermal simulation has been linked to life cycle assessment. Until now, such studies consider a conventional occupants' behaviour, or use monitoring results to adapt the model. The approach proposed here complements life cycle assessment with a sensitivity study in order to account for the variability in real occupancy scenarios, but does not require monitoring results so that it can be performed during the design phase. Application of this method is illustrated by a case study regarding two attached passive houses built in France. The results show the essential influence of occupants on the performance, but varying the occupancy scenario does not modify the ranking between the compared alternatives. Checking the robustness of life cycle assessment results increases the relevance of this method as a design aid.

Frauke Oldewurtel - One of the best experts on this subject based on the ideXlab platform.

  • use of model predictive control and weather forecasts for energy efficient Building climate control
    Energy and Buildings, 2012
    Co-Authors: Frauke Oldewurtel, Alessandra Parisio, Colin N Jones, Dimitrios Gyalistras, Markus Gwerder, Vanessa Stauch, Beat Lehmann, Manfred Morari
    Abstract:

    This paper presents an investigation of how ModelPredictiveControl (MPC) and weatherpredictions can increase the energy efficiency in Integrated Room Automation (IRA) while respecting occupant comfort. IRA deals with the simultaneous control of heating, ventilation and air conditioning (HVAC) as well as blind positioning and electric lighting of a Building zone such that the room temperature as well as CO2 and luminance levels stay within given comfort ranges. MPC is an advanced control technique which, when applied to Buildings, employs a model of the Building dynamics and solves an optimization problem to determine the optimal control inputs. In this paper it is reported on the development and analysis of a Stochastic ModelPredictiveControl (SMPC) strategy for Buildingclimatecontrol that takes into account the uncertainty due to the use of weatherpredictions. As first step the potential of MPC was assessed by means of a large-scale factorial simulation study that considered different types of Buildings and HVAC systems at four representative European sites. Then for selected representative cases the control performance of SMPC, the impact of the accuracy of weatherpredictions, as well as the tunability of SMPC were investigated. The findings suggest that SMPC outperforms current control practice.

  • use of model predictive control and weather forecasts for energy efficient Building climate control
    Energy and Buildings, 2012
    Co-Authors: Frauke Oldewurtel, Alessandra Parisio, Colin N Jones, Dimitrios Gyalistras, Markus Gwerder, Vanessa Stauch, Beat Lehmann, Manfred Morari
    Abstract:

    This paper presents an investigation of how ModelPredictiveControl (MPC) and weatherpredictions can increase the energy efficiency in Integrated Room Automation (IRA) while respecting occupant comfort. IRA deals with the simultaneous control of heating, ventilation and air conditioning (HVAC) as well as blind positioning and electric lighting of a Building zone such that the room temperature as well as CO2 and luminance levels stay within given comfort ranges. MPC is an advanced control technique which, when applied to Buildings, employs a model of the Building dynamics and solves an optimization problem to determine the optimal control inputs. In this paper it is reported on the development and analysis of a Stochastic ModelPredictiveControl (SMPC) strategy for Buildingclimatecontrol that takes into account the uncertainty due to the use of weatherpredictions. As first step the potential of MPC was assessed by means of a large-scale factorial simulation study that considered different types of Buildings and HVAC systems at four representative European sites. Then for selected representative cases the control performance of SMPC, the impact of the accuracy of weatherpredictions, as well as the tunability of SMPC were investigated. The findings suggest that SMPC outperforms current control practice.

  • experimental analysis of model predictive control for an energy efficient Building heating system
    Applied Energy, 2011
    Co-Authors: Jan Siroky, Jiri Cigler, Frauke Oldewurtel, Samuel Privara
    Abstract:

    Low energy Buildings have attracted lots of attention in recent years. Most of the research is focused on the Building construction or alternative energy sources. In contrary, this paper presents a general methodology of minimizing energy consumption using current energy sources and minimal retrofitting, but instead making use of advanced control techniques. We focus on the analysis of energy savings that can be achieved in a Building heating system by applying model predictive control (MPC) and using weather predictions. The basic formulation of MPC is described with emphasis on the Building control application and tested in a two months experiment performed on a real Building in Prague, Czech Republic.

  • energy efficient Building climate control using stochastic model predictive control and weather predictions
    American Control Conference, 2010
    Co-Authors: Frauke Oldewurtel, Alessandra Parisio, Colin N Jones, Manfred Morari, Dimitrios Gyalistras, Markus Gwerder, Vanessa Stauch, Beat Lehmann, Katharina Wirth
    Abstract:

    One of the most critical challenges facing society today is climate change and thus the need to realize massive energy savings. Since Buildings account for about 40% of global final energy use, energy efficient Building climate control can have an important contribution. In this paper we develop and analyze a Stochastic Model Predictive Control (SMPC) strategy for Building climate control that takes into account weather predictions to increase energy efficiency while respecting constraints resulting from desired occupant comfort. We investigate a bilinear model under stochastic uncertainty with probabilistic, time varying constraints. We report on the assessment of this control strategy in a large-scale simulation study where the control performance with different Building variants and under different weather conditions is studied. For selected cases the SMPC approach is analyzed in detail and shown to significantly outperform current control practice.

Irene Poli - One of the best experts on this subject based on the ideXlab platform.

  • optimised design of energy efficient Building facades via evolutionary neural networks
    Energy and Buildings, 2011
    Co-Authors: Giovanni Zemella, Davide De March, Matteo Borrotti, Irene Poli
    Abstract:

    Abstract Buildings are required to be more and more energy efficient, in order to comply with restrictive requirements of Building regulations and energy certifications. Optimisation algorithms have shown to be effective in identifying good solutions for the design of efficient Building services. In this article Evolutionary Neural Network Design (ENN-Design) has been adopted to drive the design of a typical facade module for an office Building. This application is significant, since facades play a major role in the definition of the energy performance of Buildings. Both single-objective and multi-objective optimisations have been carried out. The aim of the article is to introduce an innovative approach for improving the performance of Building envelopes by means of a reasonable amount of calculation time.

Cabeza, Luisa F. - One of the best experts on this subject based on the ideXlab platform.

  • Palm oil-based bio-PCM for energy efficient Building applications: Multipurpose thermal investigation and life cycle assessment
    Elsevier, 2020
    Co-Authors: Fabiani Claudia, Pisello, Anna Laura, Barbanera Marco, Cabeza, Luisa F.
    Abstract:

    This study aims at investigating the potential use of a bio-based phase change material, i.e. expired palm oil from the food industry, as a more sustainable alternative to petrochemical-based organic PCMs. To this purpose, thermogravimetric analysis (TGA) and isoconversional methods (Starink and Miura-Maki methods) are applied and the main thermo-physical properties of the blend are investigated by means of differential scanning calorimetry (DSC) and extensive thermal monitoring in a controlled realistic environment. Finally, a life cycle assessment is used to evaluate the environmental impact of the bio-based material in comparison to the more common petrochemical-based application. Kinetic analysis results indicate the two dimensional phase boundary reaction model as the most reliable scheme for describing the oxidation of palm oil, with an activation energy of about 73 kJ · mol−1. The DSC and the thermal monitoring procedure, showed two separate melting peaks in the ambient temperature range, which globally guarantee a melting enthalpy of about 50 kJ · kg−1, i.e. of the same order of magnitude of the first developed PCMs. Results from the life cycle analysis reveal that the expired palm oil can be considered a promising material for bio-based latent applications. Globally, the palm oil has proved itself as a promising, low cost, and environmentally friendly alternative for passive thermal storage solutions (e.g. Building envelope applications) where stability across multiple thermal cycles, low health risks, and low leakage are crucial parameters to be addressed

  • Palm oil-based bio-PCM for energy efficient Building applications: Multipurpose thermal investigation and life cycle assessment
    'Elsevier BV', 2020
    Co-Authors: Fabiani Claudia, Pisello, Anna Laura, Barbanera Marco, Cabeza, Luisa F.
    Abstract:

    This study aims at investigating the potential use of a bio-based phase change material, i.e. expired palm oil from the food industry, as a more sustainable alternative to petrochemical-based organic PCMs. To this purpose, thermogravimetric analysis (TGA) and isoconversional methods (Starink and Miura-Maki methods) are applied and the main thermo-physical properties of the blend are investigated by means of differential scanning calorimetry (DSC) and extensive thermal monitoring in a controlled realistic environment. Finally, a life cycle assessment is used to evaluate the environmental impact of the bio-based material in comparison to the more common petrochemical-based application. Kinetic analysis results indicate the two dimensional phase boundary reaction model as the most reliable scheme for describing the oxidation of palm oil, with an activation energy of about 73 kJ · mol−1. The DSC and the thermal monitoring procedure, showed two separate melting peaks in the ambient temperature range, which globally guarantee a melting enthalpy of about 50 kJ · kg−1, i.e. of the same order of magnitude of the first developed PCMs. Results from the life cycle analysis reveal that the expired palm oil can be considered a promising material for bio-based latent applications. Globally, the palm oil has proved itself as a promising, low cost, and environmentally friendly alternative for passive thermal storage solutions (e.g. Building envelope applications) where stability across multiple thermal cycles, low health risks, and low leakage are crucial parameters to be addressed.This work was partially funded by the Ministerio de Ciencia, Innovación y Universidades de España (RTI2018-093849-B-C31). Dr. Cabeza would like to thank the Catalan Government for the quality accreditation given to her research group GREiA (2017 SGR 1537). GREiA is a certified agent TECNIO in the category of technology developers from the Government of Catalonia. This work is partially supported by ICREA under the ICREA Academia programme. Authors from University of Perugia thank Fondazione cassa di Risparmio di Perugia for supporting the investigation about biomaterials within the project SOS CITTÁ 2018.0499.026