Urban building energy performance prediction and retrofit analysis using data-driven machine learning approach

Stakeholders such as urban planners and energy policymakers use building energy performance modeling and analysis to develop strategic sustainable energy plans with the aim of reducing energy consumption and emissions from the built environment. However, inconsistent energy data and the lack of scalable building models create a gap between building energy modeling and traditional planning practices. An alternative approach is to conduct a large-scale energy usage survey, which is time-consuming. Similarly, existing studies rely on traditional machine learning or statistical approaches for calculating large-scale energy performance. This paper proposes a solution that employs a data-driven machine learning approach to predict the energy performance of urban residential buildings, using both ensemble-based machine learning and end-use demand segregation methods. The proposed methodology consists of five steps: data collection, archetype development, physics-based parametric modeling, machine learning modeling, and urban building energy performance analysis. The devised methodology is tested on the Irish residential building stock and generates a synthetic building dataset of one million buildings through the parametric modeling of 19 identified vital variables for four residential building archetypes. As a part of the machine learning modeling process, the study implemented an end-use demand segregation method, including heating, lighting, equipment, photovoltaic, and hot water, to predict the energy performance of buildings at an urban scale. Furthermore, the model's performance is enhanced by employing an ensemble-based machine learning approach, achieving 91% accuracy compared to the traditional approach's 76%. Accurate prediction of building energy performance enables stakeholders, including energy policymakers and urban planners, to make informed decisions when planning large-scale retrofit measures

Stromal expression of decorin, Semaphorin6D, SPARC, Sprouty1 and Tsukushi in developing prostate and decreased levels of decorin in prostate cancer.

BACKGROUND AND AIM: During prostate development, mesenchymal-epithelial interactions regulate organ growth and differentiation. In adult prostate, stromal-epithelial interactions are important for tissue homeostasis and also play a significant role in prostate cancer. In this study we have identified molecules that show a mesenchymal expression pattern in the developing prostate, and one of these showed reduced expression in prostate cancer stroma. METHODOLOGY AND PRINCIPAL FINDINGS: Five candidate molecules identified by transcript profiling of developmental prostate mesenchyme were selected using a wholemount in situ hybridisation screen and studied Decorin (Dcn), Semaphorin6D (Sema6D), SPARC/Osteonectin (SPARC), Sprouty1 (Spry-1) and Tsukushi (Tsku). Expression in rat tissues was evaluated using wholemount in situ hybridisation (postnatal day (P) 0.5) and immunohistochemistry (embryonic day (E) E17.5, E19.5; P0.5; P6; 28 & adult). Four candidates (Decorin, SPARC, Spry-1, Tsukushi) were immunolocalised in human foetal prostate (weeks 14, 16, 19) and expression of Decorin was evaluated on a human prostate cancer tissue microarray. In embryonic and perinatal rats Decorin, Semaphorin6D, SPARC, Spry-1 and Tsukushi were expressed with varying distribution patterns throughout the mesenchyme at E17.5, E19.5, P0.5 and P6.5. In P28 and adult prostates there was either a decrease in the expression (Semaphorin6D) or a switch to epithelial expression of SPARC, and Spry-1, whereas Decorin and Tsukushi were specific to mesenchyme/stroma at all ages. Expression of Decorin, SPARC, Spry-1 and Tsukushi in human foetal prostates paralleled that in rat. Decorin showed mesenchymal and stromal-specific expression at all ages and was further examined in prostate cancer, where stromal expression was significantly reduced compared with non-malignant prostate. CONCLUSION AND SIGNIFICANCE: We describe the spatio-temporal expression of Decorin, Semaphorin6D, SPARC, Spry-1 and Tsukushi in developing prostate and observed similar mesenchymal expression patterns in rat and human. Additionally, Decorin showed reduced expression in prostate cancer stroma compared to non-malignant prostate stroma

An evaluation of the completeness of the surveillance data in CIDR in 2012 and 2013. Technical report.

HIV is an important public health issue, and warrants prioritisation for surveillance. Accurate and complete surveillance information on new HIV diagnoses is essential, in order to monitor the epidemiology of HIV over time, and evaluate the effect of prevention strategies and interventions. This report describes the completeness of the HIV enhanced surveillance system in Ireland over a two year period from January 1st 2012 to December 31st 2013. It is the first report to examine the completeness of HIV reporting since it became a notifiable disease in Ireland, and was included in the national Computerised Infectious Disease Reporting System, CIDR. Data were extracted from CIDR on 23rd July 2014 and were correct at the time of publication

Environmental and energy performance assessment of buildings using scenario modelling and fuzzy analytic network process

A well-recognised gap exists between measured and predicted building energy performance. Some practical assessment approaches offer the potential to reduce this gap using multiple indicators that evaluate building performance. Such approaches rely on subjective analysis of indicators’ relative weights but are typically limited to a fixed assessment structure. Scenario modelling is one method that enables flexible and multi-granular environmental and energy performance assessment by coupling building function with other pivotal aspects of building operation. However, this method weighs all performance criteria equally. The objective of this paper is to empower building managers with enhanced environmental and energy performance assessment by integrating scenario modelling with a Fuzzy Analytic Network Process. Scenario modelling decomposes environmental and energy performance assessment into a set of flexible mappings between performance indicators and multi-granular building objects while Fuzzy Analytic Network Process enables calculation of relative weights by encapsulating ambiguity in domain expertise and complex interactions among often conflicting criteria. A case study demonstrated the engineering value of this approach. The sports centre obtained an operational score of 56.9 out of 100, or level 4 of 6 (i.e. very good) in terms of operational performance classification using calculated relative weights and intermediate results for eight carefully-identified indicators. When compared to an equivalent assessment using equally weighted criteria, the proposed approach enables more informative and targeted evaluations. With these results, building managers can quickly identify inefficient areas of building operation and improve energy consumption while maintaining building function. The approach is applicable for a wide range of buildings.Science Foundation IrelandNatural Science Foundation of Hubei Province, Chin

Neutral Current Minimization Control for Solid State Transformers under Unbalanced Loads in Distribution Systems

This paper analyses the neutral current reduction performance of a three phase four leg solid state transformer (SST) under different degrees of unbalanced load. Several kinds of control strategies are presented, the neutral current elimination controls which rely on phase shifting, voltage amplitude and phase shifting & voltage amplitude combination control. A neutral current minimization control which ensures the SST output voltages complies with the EN 50160 output voltage unbalance standard is also developed. These control approaches simply build on the balanced voltage control providing voltage references which slightly unbalanced the voltage amplitude and phase angle or both. The effectiveness of the proposed strategies is validated through tests on a downscaled prototype. Simulation results for the neutral current minimization control of the SST applied to a real urban distribution network with distributed loads are presented. The results of this analysis show that overall the neutral current minimization results in an energy saving from both reduced losses in the distribution cables and reduced power consumption in the load.Update citation details during checkdate report - A

Design of VSC Connected Low Frequency AC Offshore Transmission with Long HVAC Cables

Low frequency ac transmission (LFAC) has been proposed as an alternative to high voltage dc transmission for medium distance (80-150 km) offshore wind farms. Long HVAC cables and their associated low frequency resonance, connected to voltage source converters (VSC), provide technical challenges for the control of the offshore voltage. This paper provides the design of the offshore voltage 'grid forming control' to maintain a stable offshore voltage accounting for the connection of a long HVAC cable connected to the VSC. Simulations are performed on an LFAC test system to examine the influence of controller parameters and the associated design tradeoffs between the selection of dq controller time constants and voltage control bandwidth. The LFAC system design and control is then validated in a hardware experiment where the proposed controller operates in a real-Time hardware-in-The-loop experiment.Science Foundation IrelandUniversity College DublinElectricity Research Centre, University College Dubli

GIS-Based Residential Building Energy Modeling at District Scale

BSO 2018: 4th IBPSA-England Conference on Building Simulation and Optimization, Cambridge, United Kingdom, 11-12 September 2018Urban planners often develop strategic sustainable energy planning processes that aim to minimize the overall energy consumption and CO2 emissions of buildings. Planning at such scales could be informed by the use of building energy modeling approaches. However, due to inconsistencies in available urban energy data and a lack of scalable building modeling approaches, a gap persists between building energy modeling and traditional planning practices. This paper develops a methodology based on bottom-up approach for GIS-based residential building energy modeling at a district scale. The methodology is applied to districts in Dublin and modeling results indicate where and what type of buildings have the greatest potential for energy savings throughout the city.Science Foundation IrelandESIPP UC