Integrated slacks-based measure of efficiency and super-efficiency in Data Envelopment Analysis

Dr Trung Hieu Tran and the co-authors have recently published a paper in the International Journal of Management Science (OMEGA, impact factor: 4.311). The paper describes an integrated slacks-based model for measuring the efficiency of decision-making units in Data Envelopment Analysis. The model has been applied for evaluating the sustainability of construction firms in Nottingham, UK in terms of financial aspects. It supports the firms in recognising the strength and weakness to improve and obtain the sustainable development.

In this paper, we develop an integrated model for slacks-based measure (SBM) simultaneously of both the efficiency and the super-efficiency for decision-making units (DMUs) in data envelopment analysis (DEA). Unlike the traditional solution approaches in which we need to identify the efficient DMUs by the SBM model of Tone (2001) [20] before applying the super SBM model of Tone (2002) [21] for the DMUs to achieve their super-efficiency scores, our integration can obtain the efficiency scores of the inefficient DMUs and the super-efficiency scores of the efficient DMUs by solving simultaneously these two models by an one-stage approach. Therefore, it may save computational time for large-scale practical applications. Due to the non-linearity in the objective function of this integrated model, we develop a linearisation technique to deal with the non-linear model. The numerical experiments, carried out on several examples in the literature and a case study, have demonstrated the accuracy and the computational time effectiveness of our proposed model as compared with the traditional solution approaches.

Article link: https://www.sciencedirect.com/science/article/pii/S0305048317308472?via%3Dihub

Call for papers - Entropy and Scale-Dependence in Urban Modelling

Prof Darren Robinson and Dr Yong Mao are editing a Special Issue of the journal Entropy. The call title is “Entropy and Scale-Dependence in Urban Modelling” and is accepting submissions until 16th November 2018.

Cities are complex systems that require resources to function. They are maintained in more or less stable states by exchanging entropy across their boundaries: Relatively low entropy resources are imported, processed and higher entropy wastes are exported. Entropy, as originally developed by Boltzmann, measures all microscopic-scale configurations of the universe, and in combination with the second law of thermodynamics, provides a robust metric for assessing universal irreversibility, and therefore future sustainability.

This Special Issue focuses on the application of entropy in modelling and evaluating urbanisation at multiple scales. The aim is to clarify the boundaries of applicability of thermodynamic and information entropies, to demonstrate their utility and to identify promising avenues for future exploration.

Special Issue details: http://www.mdpi.com/journal/entropy/special_issues/Urban_Modelling

Automated classification metrics for energy modelling of residential buildings in the UK with open algorithms

We have a new journal article on using Ordnance Survey MasterMap and AddressBase Plus data for energy modelling studies published in Environment and Planning B: Urban Analytics and City Science. The paper describes techniques to automatically extract information on the geometric and topological characteristics of residential buildings, which can then be used as parameters for estimating the energy use at the city scale.

We describe creating a spatial database of residential properties, qualitative classification of each building’s form according to its spatial relations, and the addition of quantitative metrics relating to the building envelope. The techniques described in the article were implemented as PostGIS database queries and have been made available at pgBuiltForm under a Creative Commons Attribution 4.0 licence.

Abstract:

Estimating residential building energy use across large spatial extents is vital for identifying and testing effective strategies to reduce carbon emissions and improve urban sustainability. This task is underpinned by the availability of accurate models of building stock from which appropriate parameters may be extracted. For example, the form of a building, such as whether it is detached, semi-detached, terraced etc. and its shape may be used as part of a typology for defining its likely energy use. When these details are combined with information on building construction materials or glazing ratio, it can be used to infer the heat transfer characteristics of different properties. However, these data are not readily available for energy modelling or urban simulation. Although this is not a problem when the geographic scope corresponds to a small area and can be hand-collected, such manual approaches cannot be easily applied at the city or national scale. In this article, we demonstrate an approach that can automatically extract this information at the city scale using off-the-shelf products supplied by a National Mapping Agency. We present two novel techniques to create this knowledge directly from input geometry. The first technique is used to identify built form based upon the physical relationships between buildings. The second technique is used to determine a more refined internal/external wall measurement and ratio. The second technique has greater metric accuracy and can also be used to address problems identified in extracting the built form. A case study is presented for the City of Nottingham in the United Kingdom using two data products provided by the Ordnance Survey of Great Britain: MasterMap and AddressBase. This is followed by a discussion of a new categorisation approach for housing form for urban energy assessment.

The full paper is available at: http://journals.sagepub.com/eprint/h489ZksJJ9RKMEbFYNRj/full

The code is available on Github: pgBuiltForm

INSMART - Insights on integrated modelling of EU cities energy system transition

Gavin Long co-authored a paper to be published in the April 2018 edition of Energy Strategy Reviews. The paper summarises the results and findings of the InSmart project, offering insights into how European cities might effectively make the transition to a low carbon future.

Abstract:

Urban areas have a pivotal role to play in climate change mitigation, as they are responsible for a high share of energy consumption and provide many opportunities for more efficient supply & use of energy. This makes the case for energy system modelling at city level, as done within the INSMART EU project, which identified the optimum mix of measures for a sustainable energy future for four European cities in a holistic manner. The approach combined quantitative modelling with Multi-Criteria Decision Analysis. Sector specific data and models (buildings and transport) were articulated into one integrated energy system model based on the TIMES model generator. It was found that urban level energy modelling brings with it a new set of challenges, since for a well-known territory, transparency and effective communication with local decision-makers are even more important than at national or transnational level. Special efforts should be paid to making model results geographically explicit, and urban modelling results should expect scrutiny by local agents. It was found that there is a gap between the scope for action of local energy planners and the most energy intensive urban sectors, which highlighted new priorities instead of those traditionally taken under municipal management.

Early access to full paper is available at: https://authors.elsevier.com/c/1WfvJ,oI6xbcUV

Mining households' energy data to disclose fuel poverty - Lessons for Southern Europe

Gavin Long co-authored a paper to be published in the March 2018 edition of the Journal of Cleaner Production. The paper combines actual energy use data from smart meters with energy demand predictions generated from building energy simulations and household surveys carried out under the InSmart project.

Abstract:

Fuel poverty is a recognized and increasing problem in several European countries. A growing body of literature covers this topic, but dedicated analysis for Portugal is scarce despite the high perception of this condition. This paper contributes to fill this knowledge gap focusing on a European southern city while bringing new datasets and analysis to the assessment of this topic; consumer groups identification and to policy discussion. Daily electricity smart meters’ registries were combined with socio-economic data, collected from door-to-door surveys, to understand the extent and the determinants of energy consumption for two contrasting consumer groups (herein called fuel poverty and fuel obesity groups). The analysis is based on the amount and annual profile of electricity consumption and was complemented with building energy simulations for relevant building typologies in those groups, to identify heating and cooling thermal performance gaps. Results disclose socio-economic variables, as income, and consumers’ behaviour as key determinants of electricity consumption. It was identified a severe lack of thermal comfort levels inside households of both groups, either in cooling (98% for fuel poverty and 87% for fuel obesity) and heating seasons (98% for fuel poverty and 94% for fuel obesity). Major conclusion refers that electricity consumption cannot be used alone to segment consumer groups. This assessment may serve to support energy policy measures and instruments targeted to different consumers’ groups.

Early access to full paper is available at: https://authors.elsevier.com/c/1WWD03QCo9Q~jl