Abstract

Proper completion of a public building construction project is dependent on the effectiveness of the construction supervision. However, it has become increasingly difficult to finish construction projects within the specified time, budget, and quality standards due to poor supervision practices. Approaches such as quantitative and qualitative research and descriptive research types were used in this study. Questionnaires survey were issued to 104 respondents, with 11 representing clients, 22 representing consultants, and 71 representing contractors. There were 33 factors identified, which were then divided into seven groups for further analysis. It was discovered that the top-ranked factors that influence construction supervision practice are: supervisors' lack of skills and experience, incompleteness and consistency in the design, lack of appropriate decision making, location of the project, material and equipment costs, a lack of construction materials supply, and effect of weather conditions. The Relative Importance Index was calculated to determine whether or not the parties agreed on the factors and ranked them according to their importance. Most respondents suggested that supervisors should receive proper training, and comprehensive and detailed drawings should be provided on time. In addition, it was discovered that the top three essential group elements affecting supervision practice of the public building project are supervisory, design, and management-related aspects.

Abstract

The necessity of dealing with uncertainties is growing in many different fields of science and engineering. Due to the constant development of computational capabilities, current solvers must satisfy both statistical accuracy and computational efficiency. The aim of this work is to introduce an asynchronous framework for Monte Carlo and Multilevel Monte Carlo methods to achieve such a result. The proposed approach presents the same reliability of state of the art techniques, and aims at improving the computational efficiency by adding a new level of parallelism with respect to existing algorithms: between batches, where each batch owns its hierarchy and is independent from the others. Two different numerical problems are considered and solved in a supercomputer to show the behavior of the proposed approach.

Abstract

Thermostatic load control systems are widespread in many countries. Since they provide heat for domestic hot water and space heating on a massive scale in the residential sector, the assessment of their energy performance and the effect of different control strategies requires simplified modeling techniques demanding a small number of inputs and low computational resources. Data-driven techniques are envisaged as one of the best options to meet these constraints. This paper presents a novel methodology consisting of the combination of an optimization algorithm, two auto-regressive models and a control loop algorithm able to virtually replicate the control of thermostatically driven systems. This combined strategy includes all the thermostatically controlled modes governed by the set point temperature and enables automatic assessment of the energy consumption impact of multiple scenarios. The required inputs are limited to available historical readings from smart thermostats and external climate data sources. The methodology has been trained and validated with data sets coming from a selection of 11 smart thermostats, connected to gas boilers, placed in several households located in north-eastern Spain. Important conclusions of the research are that these techniques can estimate the temperature decay of households when the space heating is off as well as the energy consumption needed to reach the comfort conditions. The results of the research also show that estimated median energy savings of 18.1% and 36.5% can be achieved if the usual set point temperature schedule is lowered by 1○C and 2○C, respectively.

Abstract

A bottom-up electricity characterisation methodology of the building stock at the local level is presented. It is based on the statistical learning analysis of aggregated energy consumption data, weather data, cadastre, and socioeconomic information. To demonstrate the validity of this methodology, the characterisation of the electricity consumption of the whole province of Lleida, located in northeast Spain, is implemented and tested. The geographical aggregation level considered is the postal code since it is the highest data resolution available through the open data sources used in the research work. The development and the experimental tests are supported by a web application environment formed by interactive user interfaces specifically developed for this purpose. The paper’s novelty relies on the application of statistical data methods able to infer the main energy performance characteristics of a large number of urban districts without prior knowledge of their building characteristics and with the use of solely measured data coming from smart meters, cadastre databases and weather forecasting services. A data-driven technique disaggregates electricity consumption in multiple uses (space heating, cooling, holidays and baseload). In addition, multiple Key Performance Indicators (KPIs) are derived from this disaggregated energy uses to obtain the energy characterisation of the buildings within a specific area. The potential reuse of this methodology allows for a better understanding of the drivers of electricity use, with multiple applications for the public and private sector.

Abstract

Enhancing energy efficiency has become a priority for the European Union. Several policies and initiatives aim to improve the energy performance of buildings and collect data of sufficient quality on the effect of energy efficiency policies on building stocks across Europe. Knowledge about the characteristics of the building stock and the usage of these buildings' occupants is essential for defining and assessing strategies for energy savings. Nowadays, dynamic measured data from the Advanced Metering Infrastructure (AMI), especially in electricity consumption, combined with location-based data, like weather, cadastre, social or economic conditions, should be available for a significant part of the building stocks in Europe. Combinedly, this enormous set of data contains the characteristics of how buildings and their occupants consume energy. In this document, a bottom-up electricity characterisation methodology of the building stock at the local level is presented. It is based on the statistical analysis of aggregated energy consumption data, weather data, cadastre, and socioeconomic information. For validation purposes, the characterisation of the electricity consumption over Lleida (Spain) province is performed. The geographical aggregation level considered is the postal code (more detailed than LAU level 2, formerly NUTS level 5), due to it is the highest resolution available through the Spanish Distribution System Operators (DSOs) data portal. Besides, a web application to visualise the results of the characterisation has also been developed. The major novelty is the use of high-frequency consumption data from most consumers in each analysis area without considering any Building Energy Simulation (BES) model that considers performance or energy use assumptions. For this purpose, a data-driven technique is used to disaggregate consumption due to multiple components (heating, cooling, holiday and baseload). In addition, multiple Key Performance Indicators (KPIs) are derived from these components to obtain the characterisation results. The potential reuse of this methodology allows for a better understanding of the drivers of electricity use, with multiple applications for the public and private sectors. This study has been executed in the frame of the Energy & Location Applications of the ELISE (European Location Interoperability Solutions for e-Government) action of the ISA 2 (Interoperability solutions for public administrations, businesses and citizens) Programme.

Abstract

Presento un algoritmo que define una función generadora de secuencias eventualmente periódicas, con los valores del ciclo elegibles y empezadas con cualquier número entero.

Abstract

This deliverable report focuses on the main Uncertainty Quanti cation (UQ) results obtained within the EXAscale Quanti cation of Uncertainties for Technology and Science Simulation (ExaQUte) project. Details on the turbulent wind inlet generator, that enables the supply of random, yet steady, wind velocity boundary conditions during run-time, are given in section 2. This enables the developed UQ workflow, whose results are presented on the basis of the Commonwealth Advisory Aeronautical Council (CAARC) as described in Deliverable 7.1. Finally, the completed UQ workflow and the results are evaluated from an application-driven wind engineering point of view. Thereby, the significance of the developed methods and the obtained results are discussed and their applicability in practical wind-engineering applications is tested through a complete test-run of the UQ workflow.

Abstract

This deliverable presents the activities performed on the ExaQUte project task 4.5 Development of interface to fast local storage. The activities have been focused in two aspects: reduction of the storage space used by applications and design and implementation of an interface that optimizes the use of fast local storage by MPI simulations involved in the project applications. In the rst case, for one of the environments involved in the project (PyCOMPSs) the default behavior is to keep all intermediate les until the end of the execution, in case these les are reused later by any additional task. In the case of the other environment (HyperLoom), all les are deleted by default. To unify these two behaviours, the calls \delete object" and \detele le"have been added to the API and a  ag \keep" that can be set to true to keep the les and objects that maybe needed later on. We are reporting results on the optimization of the storage needed by a small case of the project application that reduces the storage needed from 25GB to 350MB. The second focus has been on the de nition of an interface that enables the optimization of the use of local storage disk. This optimization focuses on MPI simulations that may be executed across multiple nodes. The added annotation enables to de ne access patters of the processes in the MPI simulations, with the objective of giving hints to the runtime of where to allocate the di erent MPI processes and reduce the data transfers, as well as the storage usage.

Abstract

Interpretable and scalable data-driven methodologies providing high granularity baseline predictions of energy use in buildings are essential for the accurate measurement and verification of energy renovation projects and have the potential of unlocking considerable investments in energy efficiency worldwide. Bayesian methodologies have been demonstrated to hold great potential for energy baseline modelling, by providing richer and more valuable information using intuitive mathematics. This paper proposes a Bayesian linear regression methodology for hourly baseline energy consumption predictions in commercial buildings. The methodology also enables a detailed characterization of the analyzed buildings through the detection of typical electricity usage profiles and the estimation of the weather dependence. The effects of different Bayesian model specifications were tested, including the use of different prior distributions, predictor variables, posterior estimation techniques, and the implementation of multilevel regression. The approach was tested on an open dataset containing two years of electricity meter readings at an hourly frequency for 1578 non-residential buildings. The best performing model specifications were identified, among the ones tested. The results show that the methodology developed is able to provide accurate high granularity baseline predictions, while also being intuitive and explainable. The building consumption characterization provides actionable information that can be used by energy managers to improve the performance of the analyzed facilities.

Abstract

The present work exposes the design of optimization procedures both with the “Particle Swarm Optimization” (PSO) algorithm and the “Genetic Algorithm” (GA) for the design of reinforced concrete frames, making comparisons in cost, weight of the structure and predicted damage. The optimization procedures are built up using the “Idealized Smeared Reinforcement” (ISR) analogy for each element of the structural model frames considered for this work. Two different numerical structural plane-frame models were created for the application and comparison of the performance of the optimization design procedures hereby proposed. The optimization procedures were mono-objective with a cost-objective function, taking on account steel reinforcement and concrete for the cost computation. Two different design approaches were carried on for this work, one proposing asymmetrical reinforcement for columns and the other with symmetrical reinforcement. In order to compute the damage indices considered for this study a non-linear Pushover structural analysis is performed. Results show that asymmetrical reinforcement in columns may reduce concrete volumes, although such reduction in material might not be quite proportional with construction cost, given that asymmetric reinforcement in columns is more expensive than symmetrical, per unit-cost. The bigger the structure, the more likely is to obtain lighter structures by using asymmetrical reinforcement. Regarding damage of the structure, results show that when using asymmetrical reinforcement in columns, it is more likely to obtain smaller values for the expected damage with no great difference on the estimated collapse Safety Factors for the seismic loads. In general, the proposed methodology hereby proposed enhances quite good optima results, requiring only a few adjustments of clash-free and slap reinforcement after the optimization procedure terminates. When designing reinforced concrete frames with asymmetric reinforcement in columns, an increase in construction costs of as much as $25\%$ as that obtained for symmetric reinforcement could be enhanced. In general, with the proposed methodology to optimally design reinforced concrete frames, savings of as much as $20\%$ in construction costs from an initial structural proposal can be reached.