An Integer Linear Programming approach to minimize the cost of the refurbishment of a façade to improve the energy efficiency of a building

[EN] Buildings account 40% of the EU's total energy consumption. Therefore, they represent a key potential source of energy savings to fight, among others, against climate change. Furthermore, around 54% of the buildings in Spain date back before 1980, when no thermal regulation was available. The refurbishment of a façade of an old building is usually the most effective way to improve its energy efficiency, by adding layers to the external envelope in order to reduce its thermal transmittance. This paper deals with the problem of minimizing costs for the thermal refurbishment of a façade with thickness and thermal ransmittance bounds and with an intervention both on the opaque part (wall) and the transparent part (windows). Among thousands, even millions of combinations of materials and thicknesses for the different layers to be added to the opaque part, types of frame, and combinations of glasses and air chambers for the transparent part, the aim is to choose the one that minimizes the cost without violating any restriction imposed to the thermal refurbishment, in particular the current energy efficiency regulations in the zone. To optimally solve this problem, it will be modelled as an Integer Linear Programming problem with binary variables. The case study will be Building 1B of the School for Building Engineering of the Polytechnic University of Valencia, Spain. It was built in the late 1960s and has had a very inefficient energy consumption record. The optimal solution will be found among more than 6 million feasible solutions.Salandin, A.; Soler Fernández, D.; Bevivino, M. (2020). An Integer Linear Programming approach to minimize the cost of the refurbishment of a façade to improve the energy efficiency of a building. Mathematical Methods in the Applied Sciences. 43(14):8067-8088. https://doi.org/10.1002/mma.6029S806780884314Nearly zero‐energy buildingshttps://ec.europa.eu/energy/en/topics/energy‐efficiency/buildings/nearly‐zero‐energy‐buildings(accessed 27.12.2018).Building stock characteristicshttps://ec.europa.eu/energy/en/eu‐buildings‐factsheets‐topics‐tree/building‐stock‐characteristics(accessed 27.12.2018).Boletín Especial Censo2011Parque edificatorio Publicaciones del Ministerio de Fomento http://www.fomento.gob.es/MFOM.CP.Web/handlers/pdfhandler.ashx?idpub=BAW021(accessed 27.12.2018).Boosting Building Renovation.What Potential and Value for Europe? Study for the ITRE Committee 2016http://www.europarl.europa.eu/RegData/etudes/STUD/2016/587326/IPOL_STU(2016)587326_EN.pdf(accessed 27.12.2018).Directive (EU) 2018/844 of the European Parliament and of the Council of 30 May 2018 amending Directive 2010/31/EU on the energy performance of buildings and Directive 2012/27/EU on energy efficiency (Text with EEA relevance).https://eur‐lex.europa.eu/legal‐content/EN/TXT/?uri=celex:32018L0844(accessed 27.12.2018).How to Refurbish All Buildings by 2050 Final ReportJune 2012https://www.eui.eu/projects/think/documents/thinktopic/thinktopic72012.pdf(accessed 27.12.2018).2020 climate & energy package.https://ec.europa.eu/clima/policies/strategies/2020_en(accessed 27.12.2018).2030 climate & energy framework.https://ec.europa.eu/clima/policies/strategies/2030_en(accessed 27.12.2018).2050 low‐carbon economyhttps://ec.europa.eu/clima/policies/strategies/2050_en(accessed 27.12.2018).Lidberg, T., Gustafsson, M., Myhren, J. A., Olofsson, T., & Ödlund (former Trygg), L. (2018). Environmental impact of energy refurbishment of buildings within different district heating systems. Applied Energy, 227, 231-238. doi:10.1016/j.apenergy.2017.07.022Mickaitytė, A., Zavadskas, E. K., Kaklauskas, A., & Tupėnaitė, L. (2008). THE CONCEPT MODEL OF SUSTAINABLE BUILDINGS REFURBISHMENT. International Journal of Strategic Property Management, 12(1), 53-68. doi:10.3846/1648-715x.2008.12.53-68Passer, A., Ouellet-Plamondon, C., Kenneally, P., John, V., & Habert, G. (2016). The impact of future scenarios on building refurbishment strategies towards plus energy buildings. Energy and Buildings, 124, 153-163. doi:10.1016/j.enbuild.2016.04.008Energy efficiency in buildings.https://www.buildingtechnologies.siemens.com/bt/global/en/building‐knowledge/pages/energy‐efficiency.aspx(accessed 27.12.2018).Baglivo, C., & Congedo, P. M. (2015). Design method of high performance precast external walls for warm climate by multi-objective optimization analysis. Energy, 90, 1645-1661. doi:10.1016/j.energy.2015.06.132Baglivo, C., Congedo, P. M., D’Agostino, D., & Zacà, I. (2015). Cost-optimal analysis and technical comparison between standard and high efficient mono-residential buildings in a warm climate. Energy, 83, 560-575. doi:10.1016/j.energy.2015.02.062Corgnati, S. P., Fabrizio, E., Filippi, M., & Monetti, V. (2013). Reference buildings for cost optimal analysis: Method of definition and application. Applied Energy, 102, 983-993. doi:10.1016/j.apenergy.2012.06.001U‐values in Europe.https://www.eurima.org/u‐values‐in‐europe(accessed 27.12.2018).CTE.Código Técnico de la Edificación (Spanish Technical Building Act). Documento Básico de Ahorro de Energía (Basic Document for Energy Saving). Version of 2013 with comments of 2016.http://www.codigotecnico.org/images/stories/pdf/ahorroEnergia/DccHE.pdf(accessed 27.12.2018).Sherali, H. D., & Driscoll, P. J. (2000). Evolution and state-of-the-art in integer programming. Journal of Computational and Applied Mathematics, 124(1-2), 319-340. doi:10.1016/s0377-0427(00)00431-3Kurnitski, J., Saari, A., Kalamees, T., Vuolle, M., Niemelä, J., & Tark, T. (2013). Cost optimal and nearly zero energy performance requirements for buildings in Estonia. Estonian Journal of Engineering, 19(3), 183. doi:10.3176/eng.2013.3.02Congedo, P. M., Baglivo, C., D’Agostino, D., & Zacà, I. (2015). Cost-optimal design for nearly zero energy office buildings located in warm climates. Energy, 91, 967-982. doi:10.1016/j.energy.2015.08.078Sambou, V., Lartigue, B., Monchoux, F., & Adj, M. (2009). Thermal optimization of multilayered walls using genetic algorithms. Energy and Buildings, 41(10), 1031-1036. doi:10.1016/j.enbuild.2009.05.007Di Perna, C., Stazi, F., Casalena, A. U., & D’Orazio, M. (2011). Influence of the internal inertia of the building envelope on summertime comfort in buildings with high internal heat loads. Energy and Buildings, 43(1), 200-206. doi:10.1016/j.enbuild.2010.09.007Privitera, G., Day, A. R., Dhesi, G., & Long, D. (2011). Optimising the installation costs of renewable energy technologies in buildings: A Linear Programming approach. Energy and Buildings, 43(4), 838-843. doi:10.1016/j.enbuild.2010.12.003Ashouri, A., Fux, S. S., Benz, M. J., & Guzzella, L. (2013). Optimal design and operation of building services using mixed-integer linear programming techniques. Energy, 59, 365-376. doi:10.1016/j.energy.2013.06.053Lindberg, K. B., Doorman, G., Fischer, D., Korpås, M., Ånestad, A., & Sartori, I. (2016). Methodology for optimal energy system design of Zero Energy Buildings using mixed-integer linear programming. Energy and Buildings, 127, 194-205. doi:10.1016/j.enbuild.2016.05.039Ogunjuyigbe, A. S. O., Ayodele, T. R., & Oladimeji, O. E. (2016). Management of loads in residential buildings installed with PV system under intermittent solar irradiation using mixed integer linear programming. Energy and Buildings, 130, 253-271. doi:10.1016/j.enbuild.2016.08.042Soler, D., Salandin, A., & Micó, J. C. (2018). Lowest thermal transmittance of an external wall under budget, material and thickness restrictions: An integer linear programming approach. Energy and Buildings, 158, 222-233. doi:10.1016/j.enbuild.2017.09.078Salandin, A., & Soler, D. (2018). Computing the minimum construction cost of a building’s external wall taking into account its energy efficiency. Journal of Computational and Applied Mathematics, 338, 199-211. doi:10.1016/j.cam.2018.02.003Generador de Precios de Elementos de la Construcción CYPE Ingenieros S.A. España 2017http://www.generadordeprecios.info(accessed 27.12.2018).Wolfram Mathematica http://www.wolfram.com/mathematica(accessed 27.12.2018)

Privacy-Preserving Outsourcing of Large-Scale Nonlinear Programming to the Cloud

The increasing massive data generated by various sources has given birth to big data analytics. Solving large-scale nonlinear programming problems (NLPs) is one important big data analytics task that has applications in many domains such as transport and logistics. However, NLPs are usually too computationally expensive for resource-constrained users. Fortunately, cloud computing provides an alternative and economical service for resource-constrained users to outsource their computation tasks to the cloud. However, one major concern with outsourcing NLPs is the leakage of user's private information contained in NLP formulations and results. Although much work has been done on privacy-preserving outsourcing of computation tasks, little attention has been paid to NLPs. In this paper, we for the first time investigate secure outsourcing of general large-scale NLPs with nonlinear constraints. A secure and efficient transformation scheme at the user side is proposed to protect user's private information; at the cloud side, generalized reduced gradient method is applied to effectively solve the transformed large-scale NLPs. The proposed protocol is implemented on a cloud computing testbed. Experimental evaluations demonstrate that significant time can be saved for users and the proposed mechanism has the potential for practical use.Comment: Ang Li and Wei Du equally contributed to this work. This work was done when Wei Du was at the University of Arkansas. 2018 EAI International Conference on Security and Privacy in Communication Networks (SecureComm

Discretization of the Region of Interest

[EN]The meccano method was recently introduced to construct simultaneously tetrahedral meshes and volumetric parameterizations of solids. The method requires the information of the solid geometry that is defined by its surface, a meccano, i.e., an outline of the solid defined by connected polyhedral pieces, and a tolerance that fixes the desired approximation of the solid surface. The method builds an adaptive tetrahedral mesh of the solid (physical domain) as a deformation of an appropriate tetrahedral mesh of the meccano (parametric domain). The main stages of the procedure involve an admissible mapping between the meccano and the solid boundaries, the nested Kossaczký’s refinement, and our simultaneous untangling and smoothing algorithm. In this chapter, we focus on the application of the method to build tetrahedral meshes over complex terrain, that is interesting for simulation of environmental processes. A digital elevation map of the terrain, the height of the domain, and the required orography approximation are given as input data. In addition, the geometry of buildings or stacks can be considered. In these applications, we have considered a simple cuboid as meccano.Ministerio de Economía y Competitividad, Gobierno de España; Fondos FEDER; Departamento de Educación, Junta de Castilla y León; CONACYT-SENER, Fondo Sectorial CONACYT SENER HIDROCARBUROS

Second best toll and capacity optimisation in network: solution algorithm and policy implications

This paper looks at the first and second-best jointly optimal toll and road capacity investment problems from both policy and technical oriented perspectives. On the technical side, the paper investigates the applicability of the constraint cutting algorithm for solving the second-best problem under elastic demand which is formulated as a bilevel programming problem. The approach is shown to perform well despite several problems encountered by our previous work in Shepherd and Sumalee (2004). The paper then applies the algorithm to a small sized network to investigate the policy implications of the first and second-best cases. This policy analysis demonstrates that the joint first best structure is to invest in the most direct routes while reducing capacities elsewhere. Whilst unrealistic this acts as a useful benchmark. The results also show that certain second best policies can achieve a high proportion of the first best benefits while in general generating a revenue surplus. We also show that unless costs of capacity are known to be low then second best tolls will be affected and so should be analysed in conjunction with investments in the network

A Fast Algorithm for Robust Regression with Penalised Trimmed Squares

The presence of groups containing high leverage outliers makes linear regression a difficult problem due to the masking effect. The available high breakdown estimators based on Least Trimmed Squares often do not succeed in detecting masked high leverage outliers in finite samples. An alternative to the LTS estimator, called Penalised Trimmed Squares (PTS) estimator, was introduced by the authors in \cite{ZiouAv:05,ZiAvPi:07} and it appears to be less sensitive to the masking problem. This estimator is defined by a Quadratic Mixed Integer Programming (QMIP) problem, where in the objective function a penalty cost for each observation is included which serves as an upper bound on the residual error for any feasible regression line. Since the PTS does not require presetting the number of outliers to delete from the data set, it has better efficiency with respect to other estimators. However, due to the high computational complexity of the resulting QMIP problem, exact solutions for moderately large regression problems is infeasible. In this paper we further establish the theoretical properties of the PTS estimator, such as high breakdown and efficiency, and propose an approximate algorithm called Fast-PTS to compute the PTS estimator for large data sets efficiently. Extensive computational experiments on sets of benchmark instances with varying degrees of outlier contamination, indicate that the proposed algorithm performs well in identifying groups of high leverage outliers in reasonable computational time.Comment: 27 page

A Regularized Graph Layout Framework for Dynamic Network Visualization

Many real-world networks, including social and information networks, are dynamic structures that evolve over time. Such dynamic networks are typically visualized using a sequence of static graph layouts. In addition to providing a visual representation of the network structure at each time step, the sequence should preserve the mental map between layouts of consecutive time steps to allow a human to interpret the temporal evolution of the network. In this paper, we propose a framework for dynamic network visualization in the on-line setting where only present and past graph snapshots are available to create the present layout. The proposed framework creates regularized graph layouts by augmenting the cost function of a static graph layout algorithm with a grouping penalty, which discourages nodes from deviating too far from other nodes belonging to the same group, and a temporal penalty, which discourages large node movements between consecutive time steps. The penalties increase the stability of the layout sequence, thus preserving the mental map. We introduce two dynamic layout algorithms within the proposed framework, namely dynamic multidimensional scaling (DMDS) and dynamic graph Laplacian layout (DGLL). We apply these algorithms on several data sets to illustrate the importance of both grouping and temporal regularization for producing interpretable visualizations of dynamic networks.Comment: To appear in Data Mining and Knowledge Discovery, supporting material (animations and MATLAB toolbox) available at http://tbayes.eecs.umich.edu/xukevin/visualization_dmkd_201

Distributed model predictive control of linear systems with coupled constraints based on collective neurodynamic optimization

© Springer Nature Switzerland AG 2018. Distributed model predictive control explores an array of local predictive controllers that synthesize the control of subsystems independently yet they communicate to efficiently cooperate in achieving the closed-loop control performance. Distributed model predictive control problems naturally result in sequential distributed optimization problems that require real-time solution. This paper presents a collective neurodynamic approach to design and implement the distributed model predictive control of linear systems in the presence of globally coupled constraints. For each subsystem, a neurodynamic model minimizes its cost function using local information only. According to the communication topology of the network, neurodynamic models share information to their neighbours to reach consensus on the optimal control actions to be carried out. The collective neurodynamic models are proven to guarantee the global optimality of the model predictive control system