BIM enabled building energy modelling: development and verification of a GBXML to IDF conversion method

As part of the Design4Energy retrofit scenario a methodology is developed that uses Building Information Modelling (BIM) of existing domestic buildings to assess their energy performance using a Building Energy Modelling (BEM) technique. The focus is on the conversion process from gbXML BIM export file to an idf file for EnergyPlusTM. The conversion process is broken down into six steps of progressive addition of idf objects to enable verification. The measured operational data are used to assess the adequacy of the defaults being used

Data-driven simple thermal models: the importance of the parameter estimates

A simple 1st order data-driven lumped parameter model of a domestic building is developed to explore the effect of using different model parameter values in the model outputs. The adequacy of the Ordinary Least Square estimation technique is explored. Results show that an improved fit to the measured data can be achieved by varying the initial model parameter values of capacitance (up to 78%), resistance (-46%) and effective window area (-59%). This highlights the importance of having a reference set of parameters based on the known physical characteristics of the building. Finally, the model residuals are deemed appropriate to inform the decision making process for further model development

Developing suitable thermal models for domestic buildings with Smart Home equipment

Smart Home controls are part of a Smart Home system and allow remote and automated control of heating systems. The key research question is: with the rapid advancement of new wireless and networked control products, which thermal modelling techniques are able to best make use of the real-time performance data arising from in-home sensors and predict the impact of using advanced controls to reduce energy demand and maximise comfort? As part of identifying suitable modelling approaches for Smart Homes, a lumped parameter model which builds on the work done by Bacher and Madsen (2011) using a data-driven “Grey box” model has been developed. The potential for using the measured data and the impacts of advanced controls for this modelling technique are discussed

Data-driven simple thermal models: The radiator - gas consumption model

A simple data-driven Lumped Parameter thermal model is developed linking the radiator surface temperature to the whole house gas consumption using operational data collected from a real house. The indoor room air temperatures, the radiator surface temperatures and the whole house gas consumption are used as input data. The model parameters are estimated using the Ordinary Least Squares technique. Results show that an improved fit can be achieved by excluding data points for which gas consumption is being used for purposes other than space heating using a lower threshold (Tr,pred-Tr,meas) of 1oC during the model calibration. Finally, the model is applied to disaggregate the gas consumption data that are linked to space heating from the original whole house gas consumption at half -hourly intervals

Exploring the performance gap in UK homes: new evidence from smart home and smart meter data

The performance gap between measured and predicted energy consumption in buildings is long established. This paper explores the reasons for the performance gap using data collected in ten UK homes. Predictions made by steady state energy models were compared to measured building performance data. Model inputs relating to external conditions and occupant practices were changed to align with measured data. The results show that the performance gap in individual homes is still significant after accounting for occupant practices and suggests that more work is required to develop techniques to estimate the thermal properties of the building fabric using measured data

Decision support systems for domestic retrofit provision using smart home data streams

The scope of this paper is a study of the potential of decision support systems for retrofit provision in domestic buildings, using monitoring technologies and performance-based analysis. The key research question is: in the age of proliferation of cheap, mobile and networked sensing equipment, how can measured energy and performance data from multiple in-home sensors be utilised to accelerate building retrofit measures and energy demand reduction? Over the coming decade there will be a significant increase in the amount of measured data available from households, from national Smart Meter rollouts to personal Smart Home systems, which will provide unparalleled insights into how our homes are performing and how households are behaving. The new data streams from Smart Homes will challenge the prevailing research and policy initiatives for understanding and promoting energy-saving building retrofits. This work is part of a £1.5m UK Research Council funded project ‘REFIT: Personalised Retrofit Decision Support Tools for UK Homes using Smart Home Technology’ (www.refitsmarthomes.org). Three methods are combined to give multiple perspectives of the research challenge: 1) A literature review on Smart Homes with a focus on academic progress to date in this area; 2) Results from actual Smart Home monitored data streams, as measured in an on-going study of UK-based Smart Homes; and 3) a discussion of performance-based analysis leading to insights in decision support system provision for Smart Building owners. The approach outlined in this work will be of significant interest to national governments when promoting Smart Meter roll-outs, to energy companies in promoting new services using Smart Home data and to the academic community in providing a foundation for future studies to meet the domestic building retrofit challenge

Utilizing smart home data to support the reduction of energy demand from space heating – insights from a UK field study

It is anticipated that the wider deployment of Smart Home systems will give building occupants improved control and automation capabilities over building conditions, services and equipment. These smart technologies will also provide numerous streams of data which could help to identify opportunities to reduce energy demand in homes. This paper explores this topic by focusing on data gathered from Smart Home systems, installed in a sample of five UK homes, which provide occupants with advanced zonal space heating control. Initial results suggest that Smart Home data can generate useful information to assist energy demand reduction; including the identification of excessive heat loss from specific rooms, periods of unoccupied heating, and heating system characteristics that lead to suboptimal heating patterns. Practical issues encountered during the field study highlight important social and contextual factors that can influence the quality of data recorded. These factors could potentially impede the wider adoption of Smart Home technologies with zonal heating functions. This work is continuing and the next steps are to calculate the energy savings which would result after data from Smart Home systems was used to identify inefficient homes, systems or practices

Technical challenges and approaches to transfer building information models to building energy

The complex data exchange between architectural design and building energy simulation constitutes the main challenge in the use of energy performance analyses in the early design stage. The enhancement of BIM model data with additional specific energy-related information and the subsequent mapping to the input of an energy analysis or simulation tool is yet an open issue. This paper examines three approaches for the data transfer from 3D CAD applications to building performance simulations using BIM as central data repository and points out their current and envisaged use in practice. The first approach addresses design scenarios. It focuses on the supporting tools needed to achieve interoperability given a 74 wide-spread commercial BIM model (Autodesk Revit) and a dedicated pre-processing tool (DesignBuilder) for EnergyPlus. The second approach is similar but addresses retrofitting scenarios. In both workflows gbXML is used as the transformation format. In the third approach a standard BIM model, IFC is used as basis for the transfer process for any relevant lifecycle phase

Supporting retrofit decisions using smart meter data: a multi-disciplinary approach

The UK Government’s flagship energy efficiency program, the Green Deal, provides retrofit advice for household occupants based on a technical house survey and an engineering modelling tool. Smart meter data provides an opportunity to give bespoke advice to occupants based on the actual performance of their home and their own heating practices as well as visualisations of hourly and daily energy use. This work presents initial results from one component of a complex multidisciplinary research project which aims to use smart meter and smart home data to design and develop retrofit decision support concepts. Home visits involving creative design based research activities were carried out in five homes. Household occupants were presented with two types of energy use report; 1) a Green Deal advice report which includes suggested retrofit measures and annual energy consumption figures based on a steady state modelling approach and; 2) a personalised energy use report, based on smart meter data collected in their homes over a 12 month period. The home visits were carried out with the occupants to discuss a range of possible retrofit measures and gather feedback regarding the communication method for advice about energy efficiency improvements. Initial findings from the home visits indicate that the provision of energy feedback using smart meter data did not directly influence the occupants to make energy efficient retrofits any more than the Green Deal advice reports. However, the visualisation of actual hourly and daily energy use enabled householders to make links with their lived experience and stimulated discussions about their energy use which may impact on their preconceived ideas about energy use and energy efficiency measures

The applicability of Lumped Parameter modelling in houses using in-situ measurements

The Lumped Parameter technique is a simplified thermal modelling method, which can be informed by measured operational data whilst allowing for physical interpretation of components and processes. The application of Lumped Parameter methods to the residential sector has been limited, primarily due to the lack of availability of operational data and the complex nature of real-world factors, such as variable occupant behaviours. This work investigates the potential of using the Lumped Parameter technique for modelling the performance of existing houses using high resolution in-situ measurements. Lumped Parameter models are created based on building surveys of eleven existing domestic buildings in the UK. The models are shown to have an average Root Mean Square Error (RMSE) of 1.35oC when compared to measured values of internal air temperature. A parametric analysis of model parameters showed wall thermal resistance, window area, boiler efficiency and infiltration rate as the most significant factors affecting the indoor air temperature calculated values. Model calibration improved the average RMSE to 1.03oC (a 23.7% decrease from initial fit). In conclusion, the Lumped Parameter models were able to realistically represent the majority of the houses, highlighting the technique’s potential in informing strategic retrofit decision making across the housing stock.</p