Patrick A.B. James’s research while affiliated with University of Southampton and other places

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Publications (35)


Analysis of interpretability, temporal and spatial scalability and causality in modelling for selected literature.
Grey-box modelling approaches and applications (by building life-cycle phases).
Interpretable Data-Driven Methods for Building Energy Modelling—A Review of Critical Connections and Gaps
  • Article
  • Full-text available

February 2024

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97 Reads

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3 Citations

Energies

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Karla M. Gonzalez-Carreon

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Patrick A. B. James

Technological improvements are crucial for achieving decarbonisation targets and addressing the impacts of climate change in the built environment via mitigation and adaptation measures. Data-driven methods for building performance prediction are particularly important in this regard. Nevertheless, the deployment of these technologies faces challenges, particularly in the domains of artificial intelligence (AI) ethics, interpretability and explainability of machine learning (ML) algorithms. The challenges encountered in applications for the built environment are amplified, particularly when data-driven solutions need to be applied throughout all the stages of the building life cycle and to address problems from a socio-technical perspective, where human behaviour needs to be considered. This requires a consistent use of analytics to assess the performance of a building, ideally by employing a digital twin (DT) approach, which involves the creation of a digital counterpart of the building for continuous analysis and improvement. This paper presents an in-depth review of the critical connections between data-driven methods, AI ethics, interpretability and their implementation in the built environment, acknowledging the complex and interconnected nature of these topics. The review is organised into three distinct analytical levels: The first level explores key issues of the current research on the interpretability of machine learning methods. The second level considers the adoption of interpretable data-driven methods for building energy modelling and the problem of establishing a link with the third level, which examines physics-driven grey-box modelling techniques, in order to provide integrated modelling solutions. The review’s findings highlight how the interpretability concept is relevant in multiple contexts pertaining to energy and the built environment and how some of the current knowledge gaps can be addressed by further research in the broad area of data-driven methods.

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The influence of weather on heat demand profiles in UK social housing tower blocks

April 2022

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105 Reads

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12 Citations

Building and Environment

Prediction of heat demand is of distinct importance for policy planning in social housing, where residents are in higher danger of falling into fuel poverty. Understanding the behavioural response of fuel vulnerable households against weather allows generating accurate baseline energy models and estimations of energy savings. This paper evaluates weekly heat demand profiles of 462 social housing dwellings in five tower blocks in the South of the UK, monitored over two years. Linear and segmented regressions are fitted through the ‘segmented’ package in R Studio to explore the relationship between heat demand (including both Domestic Hot Water and space heating) and Outdoor Temperature, generating energy signature models for each flat. Three distinct heat demand profiles are found: (i) households that do not use space heating (11%), (ii) irregular consumers, where the transition towards the heating season is not identifiable (33%), and (iii) households with marked seasonal thresholds (56%). Consumption trends as well as the effect of extremely weather events such as the 2018 storm ‘The Beast from the East’ on the heat demand are evaluated. Low consuming households show little to no variation in their demand patterns during extreme weather events, whereas higher consumers seem to reach a plateau in their demand even at extremely low temperatures. The variability of heat demand in dwellings which have identical physical properties, and are exposed to the same weather conditions, is attributed to occupant behaviour. This study highlights the heterogeneity of heat demand in social housing and the need to move away from national averages.



Electrical Minigrids for Development: Lessons From the Field

August 2019

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1,062 Reads

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32 Citations

Proceedings of the IEEE

Energy services are crucial to human wellbeing and development, and without reliable energy, it is difficult to escape subsistence lifestyles and poverty. Here, we report on four identical capacity rural minigrid interventions undertaken in Kenya and Uganda with differing socioeconomic characteristics and demographics. The research outcomes presented briefly discuss the preparation stages of the interventions including community surveys that informed the technical design, deployment phases, and setup of the community cooperatives to manage the minigrid projects. The main focus here is on lessons learned, including system design and minigrid performance under various load profiles. The results show a clear and increasing uptake of power by the communities with intensities varying depending on the electricity tariff used. Across the four minigrids, daily electricity growth rates are seen to vary by a factor of 8. The Ugandan minigrids operated at close to utility grid tariff and reached the 28-kWh/day design limit within two years. By contrast, the Kenyan minigrids charged a higher cost recovery tariff, which capped the demand and systems operate below the design limit. These findings have implications not only to system design but also to system stability and longevity. The approach taken here, of community centered cooperatives running the delivered minigrids, is now embedded within the rural electrification authorities/agencies in both countries, with additional similar projects being planned in 2019/2020. The application, ramifications, and replication of such a minigrid concept as compared to other approaches are also discussed in this paper.


Onshore wind and the likelihood of planning acceptance: Learning from a Great Britain context

May 2019

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476 Reads

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50 Citations

Energy Policy

Geospatial modelling is extensively used to identify suitable sites for the installation of onshore wind turbines, with the starting point being the estimate of exploitable resource. However, there are concerns that such approaches do not accurately consider the social issues surrounding such projects, resulting in large numbers of projects subsequently being rejected at the planning permission stage. Using the location of 1721 historic wind turbine planning applications in Great Britain, this paper explores whether the planning success of proposed wind turbine projects can be better predicted using a range of geospatial, social and political parameters. The results indicate that the size of the project, local demographics and the proximity to existing wind turbines are key influences affecting planning approval. The paper demonstrates that quantitatively linking local social and political data enhances the prediction of the planning outcome of wind turbine proposals, and highlights that geospatial parameters are necessary but in isolation, not sufficient for assessing the suitability of potential sites. These results also suggest that national policy is restricting the development of onshore wind energy in regions which appear generally supportive of wind energy.


Citations (28)


... The study of Pachouri et al. [54] highlight that industry 4.0 applications related to energy consumption, energy efficiency, carbon emissions, and energy management help to achieve sustainability in the BE. Manfren, Gonzalez-carreon and James [55] conducted a comprehensive analysis of interpretable data-driven techniques for constructing energy models. They found that DTs enhanced with AI and ML technologies are increasingly being recognised as valuable instruments in the energy transition. ...

Reference:

Review of reducing energy consumption and carbon emissions through digital twin in built environment
Interpretable Data-Driven Methods for Building Energy Modelling—A Review of Critical Connections and Gaps

Energies

... On the other hand, during cold months, a weather dependent heat load was added to this base load, due to SH. The breakpoint between the two segments, also referred to as the changepoint temperature or base temperature [77], represents the maximum external temperature that requires heating. Cooling was not considered in this four-parameter model. ...

The influence of weather on heat demand profiles in UK social housing tower blocks

Building and Environment

... Exploratory graphical analysis sought to link these variables to customer categories, such as public premise, home, business, or mixed-use. Bahaj and James [56] compared load profiles and daily electricity demand across four rural microgrids with the same capacity. Consistent with Louw et al. [31], they found that consumption dynamics are cost-sensitive, driven by the relationship between income and tariff. ...

Electrical Minigrids for Development: Lessons From the Field

Proceedings of the IEEE

... To understand the location dynamics underlying onshore wind and solar PV locations comprehensively, six variable categories are considered: meteorological, orographic, environmental, infrastructure, socioeconomic, and policy [12,13,16]. The theoretical relations found in the literature informed the selection of explanatory variables in this study. ...

Onshore wind and the likelihood of planning acceptance: Learning from a Great Britain context

Energy Policy

... In recent years, the predominant approach in most studies concerning window opening behavior entailed predictions through factor analysis [10,28,29,31,57,[66][67][68]; however, the quantitative descriptions often lacked precision and clarity. • Occupants' information including occupant types and subjective factors have rarely been taken into consideration in the reported studies. ...

The effect of behavioural interventions on energy conservation in naturally ventilated offices

Energy Economics

... However, the association between age and emissions differs across consumption domains. For instance, older people are more likely to have high home energy emissions than younger or middle-aged people, presumably due to reduced mobility and a greater likelihood of being at home during the day (Büchs & Schnepf, 2013;Büchs et al., 2018;Christis et al., 2019;Lévay et al., 2021). Emissions from car travel tend to be higher for middle-aged and older people compared to those of younger people, but car travel emissions decline again beyond a certain age when older people become less mobile (Büchs et al., 2018;Mattioli et al., 2023). ...

Sick and stuck at home – how poor health increases electricity consumption and reduces opportunities for environmentally-friendly travel in the United Kingdom

Energy Research & Social Science

... The residents' adoption of low-carbon travel methods can effectively reduce urban traffic carbon emissions and their willingness to engage in low-carbon travel indirectly affects the city's total carbon emissions. Relevant research has found that the residents' personal characteristics [14,15] and their subjective perceptions of the neighbourhood environment [16], travel time [17] and low-carbon knowledge [18] all influence their choice of travel mode. The number of parking lots within a city, the level of traffic congestion and the overall standard of public transportation are urban characteristics that also influence the residents' choices of travel modes [14]. ...

Promoting low carbon behaviours through personalised information? Long-term evaluation of a carbon calculator interview

Energy Policy

... Furthermore, the increased use of private vehicles has often pressurised the transport planners while allocating the available road space among different transport modes (Gwilliam, 2003). Motorised individual movements in relatively large boxes have residual issues, retaining the need for large amounts of road space for substantial, fast-moving objects that provide barriers to people's activity being one (James et al., 2017). This is underpinned by the truism that all infrastructures are, at their very essence, created and operated to deliver shared resources in towns and cities, and more widely regionally and globally. ...

The Little Book of Rezoning

... TMY3 is the most recent version of the TMY data sets [9]. In the literature, multiple studies show the application of algorithms to adapt weather data in Hong Kong [11,31], Perugia (Italy) [23], Thessaloniki (Greece) [32], Melbourne (Australia) [12], and Hangzhou (China) [33]. This option may be more economical, but it relies on the parameterization of urban features or on detailed computational analysis (i.e. ...

Transforming typical hourly simulation weather data files to represent urban locations by using a 3D urban unit representation with micro-climate simulations

Future Cities and Environment

... The number of occupants directly affects energy consumption levels, while wake-up and bedtime shape energy demand patterns during activities, such as cooking and lighting. Unoccupied periods also impact energy consumption as appliances and lights are mostly turned off [17,43,59]. ...

Developing English domestic occupancy profiles