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Modelling energy retrofit adoption in domestic urban building stocks is vital for policymakers aiming to reduce emissions. The use of surrogate models to evaluate building performance combined with optimization procedures can optimize small building stocks but are insufficient at the urban scale. Recent methods train neural networks using samples of near-optimal solutions further decreasing the computational cost of optimization. However, these models do not make definitive predictions of decision makers with given environmental preferences. To rectify this, we extend the method by assigning a carbon valuation to households to derive their optimal retrofit solutions. By including the carbon valuation when training the predictive model, we can analyze the impact of households' changing attitudes to emissions. To demonstrate this method we construct an agent-based model of Nottingham, finding that simulated government campaigns to boost environmentalism improve both the number of retrofits performed and the mean emissions reduction of each installation. [ABSTRACT FROM AUTHOR]

In the UK, heating systems are the most prominent contributor to residential energy demand, with about 80% of the share. Their representation has thus been at the core of all UK-focussed Housing Stock Energy Models (HSEMs). However, these HSEMs estimate heating demand based on monthly or annual energy balances, with correspondingly approximate representations of heating systems and practices (incl. energy conversion, distribution and spatiotemporal control). This paper describes an extension to the dynamic HSEM: EnHub, to rigorously simulate space heating and hot-water components (i.e. heaters, boilers, pumps, radiators, end-point registers, thermostats, taps). Baseline simulations estimate the English housing stock's energy use as 35.9 mtoe. Alternative scenarios in which heating systems are substituted across the board to district heating or ground-source heat pumps predict a reduction in demand to 30 and 18 mtoe respectively; the latter potentially being zero-carbon if the power sector is successfully decarbonised. Abbreviations ASHP: Air Source Heat Pump; BREDEM: Building Research Establishment Domestic Energy Model; CHM: Cambridge Housing Model; DHW: domestic hot water; EHS: English Housing Survey; EnHub: housing stock Energy Hub; GSHP: Ground Source Heat Pump; GUI: Graphical User Interface; HDD: Heating Degree Day; HSEM: Housing Stock Energy Model; idf: EnergyPlus Input Data File; IEA: International Energy Agency; LHS: Latin Hypercube Sampling; NEED: National Energy Efficiency Data-Framework; SAP: Standard Assessment Procedure; SH: space heating; UK: United Kingdom [ABSTRACT FROM AUTHOR]

This paper introduces a new general platform for the simulation of occupants' presence and behaviours. Called No-MASS (Nottingham Multi-Agent Stochastic Simulation), this generates a synthetic population of agents, predicts their presence and, in the case of residences also their activities and inferred locations, as well as their use of windows, lights and blinds. Using the Functional Mockup Interface, No-MASS is coupled with EnergyPlus: EnergyPlus parses environmental parameters to No-MASS which in turn parses back the energetic consequences of agents' behaviours. After describing the architecture of No-MASS and the form of the integrated models, we demonstrate its utility through two use cases: a house and an office. We close by outlining how No-MASS has been extended to more comprehensively simulate the behaviours of agents occupying multiple buildings, including behaviours for which data is scarce, social interactions between agents, and a generalization of No-MASS to simulate electrical devices and their interactions. [ABSTRACT FROM AUTHOR]

Based on the analyses of data from two distinct measurement campaigns conducted in residential indoor environments in Japan and Switzerland, we identify the specificities of occupants’ behaviour with respect to their interactions with windows, including the choice of opening angles for axial openings. As a first step, each dataset is analysed to develop separate predictive models which account for the specificities of window usage in the residential context. The predictive accuracy of these models is then challenged by validation on external data: using models inferred from data obtained from one survey, actions on windows are simulated for the other survey and the predictions are compared with observations. Dynamic models developed using data from office buildings as well as previously published models are also compared using this verification procedure. In the case of the Swiss dataset, these analyses demonstrate the ability of carefully formulated behavioural models developed from office environment data to reliably predict window usage in a residential context and vice-versa. However, we observe that the same models perform less satisfactorily in the prediction of window usage in Japan. From these results it seems that such models require specific calibration in the case of buildings equipped with an air-conditioning unit as was the case for the hot and humid summer climate of Japan. [ABSTRACT FROM AUTHOR]

Using extensive field survey data acquired over the past 8 years at the Solar Energy and Building Physics Laboratory at EPFL in Switzerland, comprehensive models of occupants' presence, opening and closing of windows and the raising and lowering of blinds have been developed. These new models have been integrated within a new urban energy modelling tool, called CitySim. In this article, we describe briefly the structure of CitySim together with the means for representing occupants' presence and behaviour, both deterministic and stochastic. For a hypothetical scenario, we then go on to present results from simulations of the impact that occupants' behaviour may have on the indoor environment in buildings as well as on buildings' energy demands. From this we conclude that occupants' behaviour has a significant impact (of the order of a factor of two) on buildings' energy demands and that individuals' diversity has a yet greater impact. [ABSTRACT FROM PUBLISHER]

There is an increasing interest in the use of computer algorithms to identify combinations of parameters that optimize the energy performance of buildings. For such problems, the objective function can be multi-modal and needs to be approximated numerically using building energy simulation programs. As these programs contain iterative solution algorithms, they introduce discontinuities in the numerical approximation to the objective function. Metaheuristics often work well for such problems, but their convergence to a global optimum cannot be established formally. Moreover, different algorithms tend to be suited to particular classes of optimization problems. To shed light on this issue, we compared the performance of two metaheuristics, the hybrid CMA-ES/HDE and the hybrid PSO/HJ, in minimising standard benchmark functions and real-world building energy optimization problems of varying complexity. From this, we find that the CMA-ES/HDE performs well on more complex objective functions, but that the PSO/HJ more consistently identifies the global minimum for simpler objective functions. Both identified similar values in the objective functions arising from energy simulations, but with different combinations of model parameters. This may suggest that the objective function is multi-modal. The algorithms also correctly identified some non-intuitive parameter combinations that were caused by a simplified control sequence of the building energy system that does not represent actual practice, further reinforcing their utility. [ABSTRACT FROM AUTHOR]

Based on 6 years of continuous measurements, we have analysed in detail the occupancy, thermal and visual parameters influencing actions on shading devices in order to derive an accurate model for the prediction of their usage in office buildings. This article begins by presenting some of the key findings from these analyses. Informed by other developments in the literature, we go on to propose an approach for a comprehensive stochastic model for simulating blind usage. This model is based on a Markov process taking rigorously selected predictors (initial blind status, indoor and outdoor illuminance) as input variables to predict lowering and raising actions performed by occupants. A separate sub-model then predicts the chosen shaded fraction. An assessment of the predictive accuracy of simulations is then presented for several modelling variants using our measured data, from which the best performing model variant is selected. [ABSTRACT FROM AUTHOR]

Based on the results from a field survey campaign, this article describes three new developments which have been integrated to provide a comprehensive basis for the evaluation of overheating risk in offices. Firstly, a set of logistic regression equations have been derived to predict the probability of office occupants' adaptation of personal and environmental characteristics. Secondly, empirical adaptive increments (offsets in comfort temperature) have been derived for each of these modes of adaptation. Thirdly, these adaptive increments are used to derive adapted degree-days of overheating stimuli for input to a new model to predict overheating risk. Based on the analogy between the charging and discharging of humans' tolerance to overheating stimuli and that of charge in an electrical capacitor, this analytical model uses empirical coefficients to tune its (dis)charging time constants to a given population and situation. This article introduces these developments and how they may be coupled to building simulation programs to predict the risk that proposed design solutions will overheat. Scope for further development, as well as possible alternatives to the presented approach, are also discussed. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the authors discuss various reports published within the issue on agent-based model for building occupants, time use survey data for calibration of a stochastic model of occupants' activities, and activation of HVAC systems.

An introduction is presented in which the editor discusses various reports within the issue on topics including the study on the occupants' impact on building's energy performance, its use of shading and windows devices, and the use of environmental sensor data in predicting transient occupancy.