Smarter grid through collective intelligence: user awareness for enhanced performance

This paper examines the scenario of a university campus, and the impact on energy consumption of the awareness of building managers and users (lecturers, students and administrative staff).Peer ReviewedPostprint (published version

Material multimèdia per a l'aprenentatge autònom en l'àmbit de la construcció

Peer Reviewe

Contributions to rework prevention in construction projects

Literature usually suggests that construction organization can reduce the costs derived from rework implementing quality management systems. Most common challenges and obstacles that construction organizations face during the implementation process and use of quality management systems are related to "how" the information can be recorded in an effective way, and "what" can be done with the recorded information. The aim of this dissertation is to focus on improving the defects recording process in the construction industry, and to propose methods and tools to use defects recorded on-site to prevent and reduce rework in the construction industry. The dissertation starts with the development of a conceptual model used to characterize defects. The current model is based on previously existing models and their adaptation to the context of the Spanish residential building sector. The model is based on the enumeration of the parameters that allow characterizing defects. The final model includes 6 parameters, with a list of standardized words and their definitions. The pre-established vocabulary lists are based on existing classification systems proposed by recognised organisations, authors and research reports, but then adapted to the Spanish context. However, in terms of defects, no standardised list exists. For this reason a taxonomy of defects is further developed for the Spanish construction sector. The aforementioned taxonomy consists of 15 main categories and 19 subcategories. The dissertation continues with the development of a methodology to track defects in the construction industry and its implementation in an IT tool called MoBuild. The obtained tracking system is based on images and tags. The strengths the abovementioned tracking system is to record information in a structured way and enable further statistical analysis of the recorded information. The new approach implemented in the MoBuild application allows practitioners to reduce the time of the recording process, facilitating the implementation of quality management systems, such as ISO 9000 in construction organizations. Furthermore, research proposes a quantitative methodology for dealing with potential adverse quality risks during the pre-construction stages of residential buildings and other similar types of constructions. The strength of this methodology lies in the fact that it helps designers to explicitly consider on-site quality during the design process. Designers can compare several design alternatives during the design phase, and determine the corresponding overall quality risk levels of a construction project without their creative talents being restricted. The methodology is especially worthwhile for those less-experienced designers who lack the required skills and knowledge to recognize quality risks in developing optimal designs. The methodology also serves as an assessment tool for construction companies. It can be used to measure the potential quality risks of construction projects and its subsequent construction activities. The suggested methodology also allows construction companies to optimize their on-site performance in the quality domain during the planning and preparation stages. Finally, this dissertation analyses the quality perceived by the end users during the post-handover stage. Different statistical methods are used to demonstrate the usefulness of the recorded data for the construction organizations. The aim is to highlight the essential role that records play in the operation of a quality company, in particular by providing essential evidence of the operation of quality systems. The aforementioned statistical analysis determines the type of defects detected; the elements affected by defects; the areas where defects are detected; which subcontractors produce defects; the source of the detected defects; the origin of the detected defects and; the influence of the building type and its characteristics in the number of defects detected

Evaluación del rendimiento del control basado en redes neuronales para gestionar calderas mediante el modelo de edificio de orden reducido

There is a growing need to optimize the heating ventilation and air conditioning (HVAC) systems during building operations due to its high contribution to buildings' energy consumption and the willingness to meet the international energy and climate changes targets. Predictive and adaptive controls have arisen as proper tools to reduce the HVAC's energy consumption. They can predict future scenarios and determine the optimal strategy to manage HVAC systems. In this regard, control strategies based on neural networks (NN) to manage boilers and control the temperature setbacks are attracting significant attention. This study aims to use the reduced-order building descriptions as a benchmark model for building energy simulation to demonstrate an NN-based control's effectiveness in managing boilers in buildings. Reduced-order buildings will be simulated with different meteorological locations from various climate zones to determine if the proposed control system is more efficient than a schedule-based control or if certain zones have more potential to save energy. To carry out this analysis, a set of KPIs will be used to assess the performance of the proposed control and compare the results within the different scenarios and the baseline scenario, the scheduled-based control.Peer ReviewedObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraObjectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats SosteniblesPostprint (published version

Factors affecting rework costs in construction

Rework adversely impacts the performance of building projects. In this study, data were analyzed from 788 construction incidents in 40 Spanish building projects to determine the influence of project and managerial characteristics on rework costs. Finally, regression analysis was used to understand the relationship between the contributing factors, and to determine a model for rework prediction.Interestingly, the rework prediction model showed that only the original contract value (OCV) and the project location in relation to the company’s headquarters contribute to the regression model. The Project type, the Type of organization, the Type of contract and the original contract duration (OCD) which represents the magnitude and complexity of a project, were represented by the OCV. This model for rework prediction based on original project conditions enables strategies to be put in place prior to the start of construction, to minimize uncertainties and reduce the impact on project cost and schedule, and thus improve productivity.Peer ReviewedPostprint (author's final draft

Facilitating the implementation of neural network-based predictive control to optimize building heating operation

Simple neural network (NN) architecture is a reliable tool to transform reactive rule-based systems into predictive systems. Thermal comfort is of utmost importance in office buildings, which need the activation of heating systems at an optimal time. A high-performance NN predictive system requires a large training dataset. This can limit system efficiency due to the lack of enough historical data derived from thermal controllers. To address this issue, we generated, trained and tested a dataset of eight sizes using a calibrated building model. A set of key performance indicators (KPIs) was improved by studying the output performance. The effect of normalization and standardization preprocessing techniques on NN prediction ability was studied. Learning curves showed that a minimum of 1–4 months of data are required to obtain enough accuracy. Two heating seasons provide the optimal data size to calibrate the NN properly with high prediction accuracy. The results also revealed that building data from =two years slightly improve NN performance. The most accurate results in KPIs 90%) were obtained with preprocessed data. The effect of preprocessing on large training patterns was less than that of training patterns <100. Finally, NN model performance was less accurate in cold climate zonesThe authors gratefully acknowledge the support by Catalan agency AGAUR through their research group support program (2017SGR00227)Peer ReviewedPostprint (published version

High-Capacity Cells and Batteries for Electric Vehicles

The automotive sector is rapidly accelerating its transformation towards electric mobility, and electric vehicle (EV) sales have been increasing year after year since the beginning of the decade. Due to their overall performance, lithium-ion batteries currently dominate the electric vehicle market. Each year, car manufacturers launch new models, increasing the average capacity of electric vehicle batteries. This is achieved, in part, through making bigger batteries, which lead to an increase in the vehicle cost, weight and use of more critical raw materials. Although prices are lowering, Li-ion batteries still do not have enough energy density to substantially decrease the weight of vehicles, and EVs are around 50% heavier than common internal combustion engine vehicles (ICEV); thus, using high-energy cells is an intriguing possibility that we are being called on to explore. This Special Issue aims to evaluate several issues concerning high-capacity batteriesPeer ReviewedObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantPostprint (published version

Modelling indoor air carbon dioxide concentration using grey-box models

Predictive control is the strategy that has the greatest reported benefits when it is implemented in a building energy management system. Predictive control requires low-order models to assess different scenarios and determine which strategy should be implemented to achieve a good compromise between comfort, energy consumption and energy cost. Usually, a deterministic approach is used to create low-order models to estimate the indoor CO2 concentration using the differential equation of the tracer-gas mass balance. However, the use of stochastic differential equations based on the tracer-gas mass balance is not common. The objective of this paper is to assess the potential of creating predictive models for a specific room using for the first time a stochastic grey-box modelling approach to estimate future CO2 concentrations. First of all, a set of stochastic differential equations are defined. Then, the model parameters are estimated using a maximum likelihood method. Different models are defined, and tested using a set of statistical methods. The approach used combines physical knowledge and information embedded in the monitored data to identify a suitable parametrization for a simple model that is more accurate than commonly used deterministic approaches. As a consequence, predictive control can be easily implemented in energy management systems.Peer ReviewedPostprint (author's final draft

U-value time series analyses: Evaluating the feasibility of in-situ short-lasting IRT tests for heavy multi-leaf walls

A gap in standardization of quantitative infrared thermography (IRT) directly leads to a lack of measurement pattern for determining in-situ U-values of heavy multi-leaf walls. Three groups of causal factors might influence the estimation of this build quality indicator: operating conditions, thermophysical properties and technical conditions. Focusing on the last one, previous studies underlined the difficulties of measuring below 3¿h. In contrast to active IRT, no algorithms have been found to process images, despite playing an important role in the effectiveness and robustness of IRT. The traditional approach involves analysing from 120 to 7200 thermograms with a data acquisition interval of 1¿min up to 1¿s respectively. The aim of this paper was to critically assess the test duration that is traditionally used. Six real heavy multi-leaf walls were tested under a stationary regime as a stochastic process of underlying data. For the first time, a research based on two U-value time series analyses (statistical tests and a signal modelling technique by MATLAB) demonstrated the feasibility of short-lasting IRT tests. Moreover, this research posed an innovative data management tool to automate this non-destructive testing (NDT) in mid-term, stopping IRT tests in real time once the right level of accuracy was achieved.Postprint (author's final draft

Implementation of predictive control in a commercial building energy management system using neural networks

Most existing commercial building energy management systems (BEMS) are reactive rule-based. This means that an action is produced when an event occurs. In consequence, these systems cannot predict future scenarios and anticipate events to optimize building operation. This paper presents the procedure of implementing a predictive control strategy in a commercial BEMS for boilers in buildings, and describes the results achieved. The proposed control is based on a neural network that turns on the boiler each day at the optimum time, according to the surrounding environment, to achieve thermal comfort levels at the beginning of the working day. The control strategy presented in this paper is compared with the current control strategy implemented in BEMS that is based on scheduled on/off control. The control strategy was tested during one heating season and a set of key performance indicators were used to assess the benefits of the proposed control strategy. The results showed that the implementation of predictive control in a BEMS for building boilers can reduce the energy required to heat the building by around 20% without compromising the user’s comfort.Peer ReviewedPostprint (author's final draft