A novel concept to measure envelope thermal transmittance and air infiltration using a combined simulation and experimental approach

This paper presents a novel method to determine building envelope thermal transmittance (known as U-values) and air infiltration rate by a combination of Energy modeling (DesignBuilder and EnergyPlus), regression models and genetic algorithm at quasi-steady state conditions. DesignBuilder is used to develop the thermal model of an office building, including physical building models, materials specification, occupancy schedules, detailed HVAC system and components for energy simulation purposes. Specifically, the simulation was carried out in EnergyPlus at diverse U-values and air infiltration rates to produce a large datasets. Subsequently, the results were used to generate a linear regression model to evaluate the associations of thermal demands with U-values and air infiltration rate. Genetic algorithm was then applied to obtain a set of U-values and air infiltration rate with the minimum difference between field measurement and model prediction. The calibrated U-values and air infiltration rate were employed as inputs in EnergyPlus to model one workday heat consumption. When compared with thermal demand from measured data, the accuracy of the calibrated model improved significantly

Virtual Organizations in Practice: A European Perspective

The paper reports results from a European Union (EU) project dedicated to Virtual Organization (VO) research. It aims to consolidate VO reference models and related modeling methodologies based on experiences acquired in thirty relevant EU funded research projects. The research reveals the complex reality of deployment and adoption of VO practices and identifies a number of organizational, legal, economic, socio-cultural, and technical challenges faced by VOs, presented in the form of open questions for the research community

Toward the Digital Construction Virtual Enterprise

The paper presents an overview of the methodology and findings of the ongoing OSMOS project.1 OSMOS aims to highlight and go some way to meeting the needs of the industry by providing a set of tools, models, APIs and techniques to support the construction “Virtual Enterprise” (VE). Key to the OSMOS approach is that the tools will allow companies (especially SMEs) to partake in a projectbased VE quickly and at a low entry-level. Through a combination of IDEF0 and UML modelling, within an iterative and incremental project methodology, the OSMOS consortium has elaborated a generic process model for the set-up and structuring of the construction Virtual Enterprise, which has formed the basis for the technical implementation of the tools and API. The tools, once designed, built, and made available for testing, have been evaluated within construction based case scenarios by endusers, and subsequently refined and re-tested. The resultant solution being offered through the OSMOS approach will involve some potential process changes within the companies wishing to take part in the VE, and the project aims to provide a proposed migration path to this end

Using Material and Energy Flow Analysis to Estimate Future Energy Demand at the City Level

Cities undergoing rapid growth encounter tremendous challenges, not only in terms of providing services to meet demand, but also in ensuring that development occurs in a sustainable way. This research evaluates the potential contribution of the material and energy flow analysis framework to predicting future energy flows and corresponding CO2 emissions in Riyadh, Saudi Arabia. The research presents a generic material and energy flow analysis model and applies it to the housing stock in Riyadh to estimate future energy demand and to assess associated effects. As the country starts to adopt sustainability measures and plan its transition from a fossil fuel-based energy system towards a renewable-based energy system, an understanding of future energy flows will allow early recognition of potential environmental impacts and provide information to enable accurate predictions of future demand for resources

Integrating building and urban semantics to empower smart water solutions

Current urban water research involves intelligent sensing, systems integration, proactive users and data-driven management through advanced analytics. The convergence of building information modeling with the smart water field provides an opportunity to transcend existing operational barriers. Such research would pave the way for demand-side management, active consumers, and demand-optimized networks, through interoperability and a system of systems approach. This paper presents a semantic knowledge management service and domain ontology which support a novel cloud-edge solution, by unifying domestic socio-technical water systems with clean and waste networks at an urban scale, to deliver value-added services for consumers and network operators. The web service integrates state of the art sensing, data analytics and middleware components. We propose an ontology for the domain which describes smart homes, smart metering, telemetry, and geographic information systems, alongside social concepts. This integrates previously isolated systems as well as supply and demand-side interventions, to improve system performance. A use case of demand-optimized management is introduced, and smart home application interoperability is demonstrated, before the performance of the semantic web service is presented and compared to alternatives. Our findings suggest that semantic web technologies and IoT can merge to bring together large data models with dynamic data streams, to support powerful applications in the operational phase of built environment systems

Past, present and future of information and knowledge sharing in the construction industry: Towards semantic service-based e-construction

The paper reviews product data technology initiatives in the construction sector and provides a synthesis of related ICT industry needs. A comparison between (a) the data centric characteristics of Product Data Technology (PDT) and (b) ontology with a focus on semantics, is given, highlighting the pros and cons of each approach. The paper advocates the migration from data-centric application integration to ontology-based business process support, and proposes inter-enterprise collaboration architectures and frameworks based on semantic services, underpinned by ontology-based knowledge structures. The paper discusses the main reasons behind the low industry take up of product data technology, and proposes a preliminary roadmap for the wide industry diffusion of the proposed approach. In this respect, the paper stresses the value of adopting alliance-based modes of operation

Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression

The variability of renewable energy resources, due to characteristic weather fluctuations, introduces uncertainty in generation output that are greater than the conventional energy reserves the grid uses to deal with the relatively predictable uncertainties in demand. The high variability of renewable generation makes forecasting critical for optimal balancing and dispatch of generation plants in a smarter grid. The challenge is to improve the accuracy and the confidence level of forecasts at a reasonable computational cost. Ensemble methods such as random forest (RF) and extra trees (ET) are well suited for predicting stochastic photovoltaic (PV) generation output as they reduce variance and bias by combining several machine learning techniques while improving the stability; i.e. generalisation capabilities. This paper investigated the accuracy, stability and computational cost of RF and ET for predicting hourly PV generation output, and compared their performance with support vector regression (SVR), a supervised machine learning technique. All developed models have comparable predictive power and are equally applicable for predicting hourly PV output. Despite their comparable predictive power, ET outperformed RF and SVR in terms of computational cost. The stability and algorithmic efficiency of ETs make them an ideal candidate for wider deployment in PV output forecasting

A smart forecasting approach to district energy management

This study presents a model for district-level electricity demand forecasting using a set of Artificial Neural Networks (ANNs) (parallel ANNs) based on current energy loads and social parameters such as occupancy. A comprehensive sensitivity analysis is conducted to select the inputs of the ANN by considering external weather conditions, occupancy type, main income providers’ employment status and related variables for the fuel poverty index. Moreover, a detailed parameter tuning is conducted using various configurations for each individual ANN. The study also demonstrates the strength of the parallel ANN models in different seasons of the years. In the proposed district level energy forecasting model, the training and testing stages of parallel ANNs utilise dataset of a group of six buildings. The aim of each individual ANN is to predict electricity consumption and the aggregated demand in sub-hourly time-steps. The inputs of each ANN are determined using Principal Component Analysis (PCA) and Multiple Regression Analysis (MRA) methods. The accuracy and consistency of ANN predictions are evaluated using Pearson coefficient and average percentage error, and against four seasons: winter, spring, summer, and autumn. The lowest prediction error for the aggregated demand is about 4.51% for winter season and the largest prediction error is found as 8.82% for spring season. The results demonstrate that peak demand can be predicted successfully, and utilised to forecast and provide demand-side flexibility to the aggregators for effective management of district energy systems

Predictive modelling for solar thermal energy systems: A comparison of support vector regression, random forest, extra trees and regression trees

Predictive analytics play an important role in the management of decentralised energy systems. Prediction models of uncontrolled variables (e.g., renewable energy sources generation, building energy consumption) are required to optimally manage electrical and thermal grids, making informed decisions and for fault detection and diagnosis. The paper presents a comprehensive study to compare tree-based ensemble machine learning models (random forest – RF and extra trees – ET), decision trees (DT) and support vector regression (SVR) to predict the useful hourly energy from a solar thermal collector system. The developed models were compared based on their generalisation ability (stability), accuracy and computational cost. It was found that RF and ET have comparable predictive power and are equally applicable for predicting useful solar thermal energy (USTE), with root mean square error (RMSE) values of 6.86 and 7.12 on the testing dataset, respectively. Amongst the studied algorithms, DT is the most computationally efficient method as it requires significantly less training time. However, it is less accurate (RMSE = 8.76) than RF and ET. The training time of SVR was 1287.80 ms, which was approximately three times higher than the ET training time

District heating and cooling optimization and enhancement – towards integration of renewables, storage and smart grid

District heating and cooling (DHC) systems are attracting increased interest for their low carbon potential. However, most DHC systems are not operating at the expected performance level. Optimization and Enhancement of DHC networks to reduce (a) fossil fuel consumption, CO2 emission, and heat losses across the network, while (b) increasing return on investment, form key challenges faced by decision makers in the fast developing energy landscape. While the academic literature is abundant of research based on field experiments, simulations, optimization strategies and algorithms etc., there is a lack of a comprehensive review that addresses the multi-faceted dimensions of the optimization and enhancement of DHC systems with a view to promote integration of smart grids, energy storage and increased share of renewable energy. The paper focuses on four areas: energy generation, energy distribution, heat substations, and terminal users, identifying state-of-the-art methods and solutions, while paving the way for future research