Now showing 1 - 10 of 27
  • Publication
    Feature Assessment in Data-Driven Models for Unlocking Building Energy Flexibility
    Data-driven approaches are playing an increased role in building automation. This can, in part, be attributed to building operation and energy management system data becoming more readily accessible. A particular application is models to allow predictive control harnessing building energy flexibility, which is of interest to different stakeholders including; energy utilities, aggregators and end-users. Given the possibility of thousands of data features, feature selection becomes a critical part of the model development process. This paper considers various filter, wrapper and embedded methods applied in conjunction with three predictors in addressing the problem of constructing a suitable data-driven model to facilitate predictive control and provision of energy flexibility in a large commercial building. The feature selection algorithms are generally shown to significantly reduce model evaluation time and, in some cases, increase model accuracy. A random forest model with embedded feature selection was found to be the optimal solution in terms of model accuracy.
  • Publication
    Quantification and characterization of energy flexibility in the residential building sector
    (International Building Performance Association, 2019-09-04) ; ; ; ; ;
    Demand response can enable residential consumers to take advantage of control signals and/or financial incentives to adjust the use of their resources at strategic times. These resources usually refer to energy consumption, locally distributed electricity generation, and energy storage. The building structural mass has an inherent potential either to modify consumption or to be used as a storage medium. In this paper, the energy flexibility potential of a residential building thermal mass for the winter design day is investigated. Various active demand response strategies are assessed using two flexibility indicators: the storage efficiency and storage capacity. Using simulation, it is shown that the available capacity and efficiency associated with active demand response actions depend on thermostat setpoint modulation, demand response event duration, heating system rated power and current consumption.
  • Publication
    Data-Driven Predictive Control for Commercial Buildings with Multiple Energy Flexibility Sources
    Data-Driven Predictive Control, representing the building as a cyber-physical system, shows promising potential in harnessing energy flexibility for demand side management, where the efforts in developing a physics-based model can be significant. Here, predictive control using random forests is applied in a case study closed-loop simulation of a large office building with multiple energy flexibility sources, thereby testing the suitability of the technique for such buildings. Further, consideration is given to the feature selection and feature engineering process. The results show that the data-driven predictive control, under a dynamic grid signal, is capable of minimising energy consumption or energy cost.
  • Publication
    Self-Learning Control Algorithms for Energy Systems Integration in the Residential Building Sector
    This paper provides a research plan focusing on the application of self-learning techniques for energy systems integration in the residential building sector. Demand response is becoming increasingly important in the evolution of the power grid since demand no longer necessarily determines system supply but is now more closely constrained by generation profiles. Demand response can offer energy flexibility services across wholesale and balancing markets. Different applications have focused on the Internet of Things in demand response to assist customers, aggregators and utility companies to manage the energy consumption and energy usage through the adjustment of consumer behaviour. Even though there is extensive work in the literature regarding the potential of the commercial and the residential building sectors to provide flexibility, to date there is no standardised framework to evaluate this flexibility in a customer-Tailored way. At the same time, demand response events may affect occupant comfort expectations hindering the utilisation of flexibility that building energy systems can provide. In this research, the integration of machine learning algorithms into building control systems is investigated, in order to unify the monitoring and control of the separate systems under a holistic approach. This will allow the operation of the systems to be optimised with respect to reducing their energy consumption and their environmental footprint in tandem with the maximisation of flexibility, while maintaining occupant comfort.
      339Scopus© Citations 4
  • Publication
    State of Technology Review of Civilian UAVs
    (Bentham Science Publishers, 2016) ; ;
    Background: Unmanned Aerial Vehicle (UAV) technology has exploded in recent years. Presently UAVs are beginning to be major in roads into geographical mapping, site inspection, agriculture, and search and rescue. Methods: This paper reviewed patents and papers worldwide related to both hardware and software for the construction and deployment of UAVs and is intended to provide a snapshot of currently available UAV technologies, as well as to identify recent trends and future opportunities in affiliated hardware and software. Results: Basic components related to self-designed units are explained (e.g. platform selection, autopilot control comparison and sensor selection), and current applications and research areas are discussed. Since autonomous navigation is a key technology in UAV applications, concepts about this are also explained. Conclusions: Both in the self-designed and commercial markets, UAV components are becoming modularized. By following a standard components list, it is no longer difficult to make a customised UAV. In this way, commercial products are becoming cheaper and more standardized in their performance. Current limitations of UAVs has also become more readily detectible. Extending the flight time, improving autonomous navigation abilities, and enriching the payload capacity will be the future research focus to address these limitations.
      3331Scopus© Citations 58
  • Publication
    TRUSS Training in Reducing Uncertainty in Structural Safety: D5.2 Final Report: WP5 - Rail and Road Infrastructure
    This deliverable reports on the outputs of eight Early Stage Researchers (ESR7-ESR14) in work package, WP5 (Rail and Road Infrastructure), under the supervision of academic and industrial experts during the three years of their projects within the EU TRUSS (Training in Reducing Uncertainty in Structural Safety, 2015-2018) Innovative Training Network (ITN) programme ( Two types of infrastructure are analysed in WP5: bridges (ESR7-ESR12) and pavements (ESR13-ESR14). The first six projects aim to reduce uncertainty in bridge safety. They address areas of work such as bridge condition assessment (ESR7), probabilistic modelling of bridge damage using damage indicators (ESR8), railway bridge condition monitoring and fault diagnostics (ESR9), condition assessment based on measured vibration level (ESR10), the use of optical fibre distributed sensing for monitoring (ESR11), and the use of displacement and velocity measurements for damage localisation (ESR12). The last two projects are on uncertainty in pavement safety, where ESR13 considers the use of truck sensors for road pavement performance and asset management and ESR14 investigates the possibility of using unmanned aerial vehicles and photogrammetry method for road and bridge inspections. Generally, the areas of work developed in this work package are vehicle-infrastructure interaction, traffic load modelling, road materials, uncertainty modelling, reliability analysis, field measurement and Structural Health Monitoring (SHM) of bridges.
  • Publication
    An Intelligent Knowledge-based Energy Retrofit Recommendation System for Residential Buildings at an Urban Scale
    Buildings play a significant role in driving the urban demand and supply of energy. Research conducted in the urban buildings sector indicates that there is a considerable potential to achieve significant reductions in energy consumption and greenhouse gas emissions. These reductions are possible through retrofitting existing buildings into more efficient and sustainable buildings. Building retrofitting poses a huge challenge for owners and city planners because they usually lack expertise and resources to identify and evaluate cost-effective energy retrofit strategies. This paper proposes a new methodology based on machine learning algorithms to develop an intelligent knowledge-based recommendation system which has the ability to recommend energy retrofit measures. The proposed methodology is based on the following four steps: archetypes development, knowledge-base development, recommendation system development and building retrofitting or performance analysis. A case study of Irish buildings dataset shows that the proposed system can provide effective energy retrofits recommendation and improve building energy performance.
  • Publication
    Computer science identity and sense of belonging: a case study in Ireland
    The study described in this paper investigates the role that gender plays in making the decision to study Computer Science in University College Dublin in Ireland (background influences) and investigates whether there is a difference in the perceived sense of belonging between the genders. The aim is to improve diversity and sense of belonging amongst Computer Science students, in order to ensure that our school is an inclusive space, where anyone can feel a sense of belonging regardless their gender.
      445Scopus© Citations 19
  • Publication
    Controlled Natural Ventilation Coupled with Passive PCM System to Improve the Cooling Energy Performance in Office Buildings
    The building sector is responsible for consuming one-third of the global final energy use. In office buildings, high internal heat gains increase the cooling energy use. Thermal energy storage (TES) is a promising technology to decrease the cooling energy use, to achieve a low-carbon future, and to increase thermal comfort if properly designed. An appropriate use of the passive PCM system and natural ventilation can provide long-term energy and thermal comfort benefits. Many factors influence the efficient use of passive PCM system in buildings, such as outdoor/indoor boundary conditions, and HVAC control strategies. In office buildings, the PCM passive system integrated into the building envelope has the potential to regulate the indoor air temperature by absorbing high internal heat gains during day, however, discharging the PCM during night to work efficiently for the next day remains a challenging design criterion. The passive PCM system would not work efficiently if charging/discharging cycle is not completed. Whole-building energy simulation tools and numerical models are essential to deal with this issue. The present study is aimed at defining cooling energy savings in office buildings located in temperate climates applying PCM and natural ventilation passive technologies. A reference small office building was chosen and PCM panels with optimised melting temperature together with different natural ventilation control strategies were applied to an office building model. EnergyPlus airflow network capability was used to calculate the natural ventilation potential induced by wind and buoyancy effects. Simulation results have shown cooling energy savings from 8% to 15%. In addition, natural ventilation could increase the efficiency of PCM by 8%.
  • Publication
    A data-driven approach for multi-scale building archetypes development
    Globally the building sector accounts for a significant portion of the overall energy demand and greenhouse gas emissions of any country. The most common approach for the collection of modeling and benchmarking data that can be used for predictions of energy performance at a national or urban scale is through classification of the building stock into representative archetypes. Developing such building archetypes is a complex task due to the difficulties associated with gathering detailed geometric and non-geometric data at an urban scale. Although existing databases and projects provide a valuable overview of a building stock, the information about buildings’ physical descriptions are not regularly updated. Moreover, these databases cover only the national top-level archetypes and lack crucial information related to city or district scale building stocks. The use of national scale archetypes requires many assumptions that may not hold true for energy modeling at urban or district scale. This paper proposes a multi-scale (national, city, county and district) archetype development methodology using different data-driven approaches. The methodology consists of following five steps: 1) data collection, 2) segmentation, 3) characterization, 4) quantification, and 5) modeling results. We developed a test case based on the available building stock data of Ireland. The test case used previously developed archetype geometries coupled with the parameters determined by the characterization process to calculate annual energy use (kWh) of buildings at a multiple-scales. The resulting archetypes at national, city, county and district scale are analyzed and compared against one another. The results indicate that significant differences occur in terms of energy modeling results when national scale archetypes are used to simulate the energy performance of buildings at the local scale. These multi-scale building archetypes will aid local authorities and city planners when analyzing energy efficiency and consequently, help to improve sustainable energy policy decisions.
      665Scopus© Citations 53