LQF Manual

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Overview

LUCY provides a method to calculate the anthropogenic heat flux. It uses wide-area energy consumption and vehicle ownership values, and uses and higher-resolution residential population data estimate the heat flux from buildings, transport and human metabolism at 60 minute intervals at the spatial resolution of the population data.

  • Energy and vehicle use are assumed to be correlated to residential (night-time) populations
  • Temporal resolution is maximised by applying empirically measured diurnal, day-of-week and seasonal variations to the data.

Workflow to model QF

  1. Select parameters and data sources files
  2. Select output path: This contains model outputs, logs and any pre-processed data that is produced
  3. Perform pre-processing of the data or select existing pre-processed data: A single set of processed input data can be re-used in subsequent runs
  4. Optionally: Specify land cover fractions at high spatial resolution: Allows the spatial resolution of the modelled outputs to be enhanced
  5. Run the model: Executes the model for the chosen date range components.
  6. Visualise outputs: A simple tool is provided to generate maps and time series from the model outputs.

Main user interface

The main user interface allows the user to select the temporal extent of the model run, and the configuration files. The configuration files describe model assumptions and the library of available data files.

Information required to run the model

GQF main dialogue box
  • Model parameters file: Fortran-90 namelist file containing numerical parameters required in model calculations
  • QF components to include: Anthropogenic heat flux partitions into sensible, latent and (in the case of buildings) wastewater fractions. The modelled QF results can include any combination of these contributions, as selected here.
  • Data sources file: Fortran-90 namelist file that contains the locations of spatial and temporal input files used by the model
  • Output Path: Directory into which model outputs and associated data will be stored. Any existing files will be overwritten.
  • Start and end dates: The first and final dates for the model run. Data are produced at 30-minute intervals
  • Extra disaggregation: An optional set of inputs so that the data can be disaggregated to a higher spatial resolution:
    • Land cover fractions: Land cover fractions calculated using the UMEP land cover classifier in the pre-processing toolbox.
    • Corresponding polygon grid: The ESRI shapefile grid of polygons represented by the land cover fractions.
  • Load Results: Retrieves results from a previous run of GQF so that they can be visualised.

GQF requires spatial and temporal information about the population, energy consumption and transport in order to model QF at high temporal and spatial resolution. Before QF can be calculated for a desired output area, the energy use data must be disaggregated to match the chosen output areas. It is then temporally disaggregated to 30 minute resolution based on template diurnal cycles and scaling data that reflects the time of year. At the end of this process, the data are ready for use in QF calculations.

GQF results visualisation dialogue box
Visualising output

A simple visualisation tool accompanies the model, which produces maps and time series plots of the most recent run by default.

The results of previous runs can also be visualised without re-running the model: Select the relevant output directory and Data Sources file are chosen in the GQF UI before pressing the "Visualise" button.

Time series plots

  • One plot per output area is produced for all of the time steps present in the model output directory, showing the three QF components on separate axes. To plot a time series, select the output area of interest and click the "Show" button.

Maps

  • One map per QF component and time step is produced, coloured on a logarithmic scale according to the QF value in each output area. The map is updated in the main QGIS window each time a different QF component or time step is selected.


Model outputs

Model outputs are stored in the /ModelOutput/ subdirectory of the selected model output directory. A separate data file is produced for each time step of the model run. Each file contains a column per heat flux component and a row for each spatial feature.

  • Output files are timestamped with the patternGQFYYYYmmdd_HH-MM.csv, with times stated in UTC.
    • YYYY: 4-digit year
    • mm: 2-digit month
    • dd: 2-digit day of month
    • HH: 2-digit hour (00 to 23)
    • MM: 2-digit minute
  • The first model output is labelled 00:30 UTC and covers the period 00:00-00:30 UTC.
  • Each data file is in comma-separated value (CSV) format

Synthesised shapefiles

If pre-processing of the input data has taken place, the Disaggregated energy, transport and population shapefiles are stored in the /DownscaledData/ subdirectory of the model outputs, with filenames that reflect the time period they represent. This folder can be used as the source of processed input data in the future to save time, provided that the data sources file have not changed.

If previously processed input data are being used, the /DownscaledData/ subdirectory remains empty.

Logs

Several log files are saved in the /Logs/ subdirectory. The logs are intended to help interpretation of model outputs by providing a traceable history of why a particular spatial or temporal disaggregation value was looked up.

  1. The steps taken to disaggregate spatial data, including which attributes were involved
  2. The day of week and the time of day that was returned from each diurnal and annual profile data source when it was queried with a particular model time step.

Configuration files

The Parameters and Data Sources file are copied to the /ConfigFiles/ subdirectory of the model output directory for future reference.

Input data

Input data consists of spatial and temporal information, a lookup table for vehicle fuel efficiency and (optionally) land use cover data to further enhance the spatial resolution of the model output.

  • Spatial information:
    • Residential (evening) and work day (daytime) populations
    • District-scale domestic and industrial energy consumption
    • Road network topography and associated traffic flows
  • Temporal information (provided via CSV files):
    • Template diurnal cycles for energy consumption, traffic flow and human activity
    • Variations of these cycles for different days of week
    • Variations of the above at different times of year.

Spatial data

This section lists the spatial data (provided via ESRI shapefiles) that the model requires.

Population data

Population data [number of people per spatial unit] is used by the model in two ways:

  1. Calculating metabolic emissions in different areas
  2. Attributing domestic and industrial energy use at a finer spatial scale.

Two types of population are needed:

  • Residential/evening population: The population residing in each area
  • Workday/daytime population: The population at work or home during the daytime in each area

Since population data are key to the model method, it is important to use the finest available spatial scale.

The model must output results for a consistent set of spatial units, so the populations are assigned to the model output areas based on how much each spatial unit of population is intersected each output area. It is recommended that a population shapefile is chosen as the output areas.

Energy consumption data

The total annual energy consumption [kWh/year] must be provided five sub-sectors

  1. Industrial electricity
  2. Industrial gas
  3. Domestic electricity
  4. Domestic gas
  5. Domestic “Economy 7”: an electrical supply with a distinct diurnal pattern (may be set to zero in the data sources file if not available)

This data is used to calculate heat emissions from residential and industrial buildings, and is generally available in coarse spatial units. Residential and workday population data are therefore used to spatially disaggregate it into the model output areas.

Transportation data

A comprehensive road network shapefile is required.

  • Minimum: vector line for each segment of the road network, together with the type of road each segment represents.

Four road classes are assumed in the model:

Motorway Purpose-built highways
Primary road Major thoroughfares
Secondary road Thoroughfares with less traffic
Other Any other road segments: Assumed to have minor traffic flow

The naming convention used in the shapefile must be defined in the transport section of the | Data Sources file for the first three.

Diesel and petrol consumption are calculated for seven vehicle types indicated using any segment-specific traffic flow and speed data available. This is combined with fuel consumption data. The vehicle types are:

Name in model Description
Motorcycle Motorcycles
Taxi Taxis
Bus Buses and coaches
Artic Articulated trucks
Rigid Rigid body trucks
LGV Light Goods Vehicle
Car Ordinary cars

Fuel consumption for a given vehicle type on a particular road segment [g/day] is estimated by multiplying:

  1. Speed, fuel and vehicle-dependent consumption rates [g/km] from the COPERT-II database, which lists consumption for different vehicle types under different Euro-class regimes that apply to vehicles manufactured after a particular date.
  2. Length of the road segment [km]
  3. Vehicle type and fuel-dependent average daily total (AADT) number of vehicles passing over the road segment.

Each road segment in the shapefile would ideally be accompanied by a speed for the segment and an AADT for each vehicle type that is further broken down into diesel and petrol components for cars and LGVs. It is not always possible to obtain some or even any of these, so default representative values must also be specified in the model parameters file:

AADT A representative AADT associated with each road class
Road fleet fraction Contribution of different vehicle types to the total traffic on each road classification.
Fuel fraction Fraction of each vehicle type powered by diesel and petrol
Speed Typical speed of traffic on each road classification

The use of the default parameters depends upon the available information in the shapefile. This relations are summarised below: when parameters are used if certain information are (green) or are not (red) available.

Available in shapefile Looked up from parameters
Total AADT AADT by vehicle AADT by vehicle & fuel Speed AADT Fuel fraction Fleet fraction Speed
X X X X / / / /
/ X X X X / / /
/ / X X X / X /
/ / / X X X X /
X X X / / / / X
X X X / / / / X
/ X X / X / / X
/ / X / X / X X
/ / / / X X X X


The fuel consumption that a segment contributes to a model output area (OA) is calculated by determining the proportion of the segment that intersects the OA and multiplying the total segment consumption by this. Total fuel consumption inside an output area is calculated by summing over all the segments that intersect it. This yields a new shapefile in which each output area is associated with a daily petrol and diesel consumption.


Daily fuel consumption in an OA is converted to mean heat flux [W m-2] using the heat of combustion [J kg-1], number of seconds in a day and the area of the OA [m2]. This is disaggregated to half-hour time steps using empirical diurnal cycle data for each day of the week.

Time indexing of spatial data

A series of shapefiles, each associated with a different start date, can be loaded into the model to capture the time evolution of energy use, transport or population. The following example describes how such a series is treated by the model implementation:

Two shapefiles are provided for population. The first is correct as of 2015-01-01, and the second is correct as of 2016-01-01. The model is set to calculate QF from 2014 to 2017 continuously:

  1. Model time steps representing dates before 2015-01-01 use the earliest available shapefile (2015-01-01).
  2. Model time steps on/after 2015-01-01 but before 2016-01-01 use the 2015-01-01 shapefile
  3. Model time steps on/after 2016-01-01 use the 2016-01-01 shapefile. No transition is assumed between the shapefiles.

Since energy consumption data is disaggregated to finer spatial units based on population, the energy consumption on/before 2015-12-31 is disaggregated using the 2015-01-01 population data, while energy consumption associated with 2016-01-01 or later is disaggregated using the 2016-01-01 population data.

Temporal files required by GQF

Overview

  • Four temporal profile files (summarised below) contain information about half-hourly, daily and seasonal variations in traffic, metabolic activity and energy use. These allow the annualised data provided by the shapefiles to be temporally disaggreated into time series.

Each file contains a time series of values at 30 minute intervals, binned to the right hand side. This means the first entry of every file represents the period 00:00-00:30 and is labelled 00:30.

QF component File description(s) Size of file
Metabolism Diurnal cycles of metabolic activity for each day of week and each season 48 half-hours * 7 days * N seasons
Transport Traffic flows for each vehicle type during each day of the week 336 half-hours (=48 * 7) * 7 vehicle types
Building energy Seasonal variations: Daily total gas and electricity consumption variation (one file for electricity and gas) 365 (or 366) days * 2 fuel types
Diurnal variations: Template cycles for weekdays, Saturdays and Sundays for each season (separate file for each fuel) 48 half-hours * 3 day types * N seasons

Each file contains headers that store metadata used by the model to interpret the data:

  1. The time zone represented by the file (“UTC” or of the style “Europe/London”). If “UTC” is specified, then values must be explicitly provided for each daylight savings regime to capture shifts in human behaviour. Note that the model outputs are always UTC, with the necessary conversion taking place in the software.
  2. The start and end dates of the period represented by the data. This allows seasonality to be captured.

Ideally these files contain data taken from the period being modelled, but this is not always practical. In this case, temporal profile data from the most recent available year is looked up for the same day of week (taking into account public holidays), season and daylight savings regime if applicable. Different variants are used for traffic, energy and metabolism, and each of these is described below.

Details of temporal files

Traffic flow profiles

A template week of traffic variations at 30 min intervals (336 entries, 48 * 7) beginning on Monday must be specified for each vehicle type, so that day of week effects are captured.

An example is shown below. The first header line must be exactly as shown because it specifies the vehicle types used in the model. Each file may contain only one set of values. Subsequent periods or years must be stored in separate files.

TransportType motorcycles taxis cars Buses LGVs rigids artics
StartDate 2016-01-01
EndDate 2016-12-31
Timezone Europe/London
00:30 0.237 1.125 0.398 0.594 0.198 0.435 0.436
01:00 0.178 1.003 0.312 0.433 0.172 0.393 0.4
01:30 0.12 0.881 0.226 0.272 0.146 0.352 0.365
02:00 0.093 0.647 0.192 0.234 0.138 0.378 0.378
02:30 0.066 0.412 0.159 0.197 0.13 0.404 0.39
03:00 0.065 0.349 0.147 0.189 0.148 0.355 0.366
03:30 0.063 0.286 0.135 0.18 0.167 0.306 0.342
04:00 0.086 0.276 0.149 0.204 0.215 0.413 0.427
04:30 0.109 0.267 0.163 0.229 0.262 0.52 0.511
05:00 0.199 0.343 0.226 0.367 0.341 0.7 0.664
05:30 0.288 0.419 0.288 0.505 0.42 0.88 0.817
06:00 0.699 0.565 0.54 0.721 0.934 1.195 1.161
06:30 1.11 0.71 0.791 0.937 1.448 1.511 1.504
07:00 1.62 0.786 1.086 1.184 1.771 1.5 1.646
07:30 2.129 0.861 1.381 1.431 2.094 1.49 1.788
08:00 2.375 0.873 1.461 1.435 1.875 1.498 1.739
08:30 2.62 0.885 1.54 1.438 1.656 1.507 1.689
09:00 2.166 0.897 1.424 1.487 1.672 1.693 1.791
09:30 1.712 0.909 1.308 1.537 1.689 1.88 1.892
10:00 1.452 0.983 1.23 1.499 1.724 1.96 1.956
10:30 1.192 1.057 1.152 1.462 1.76 2.041 2.02
11:00 1.165 1.095 1.144 1.404 1.765 2.077 2.025
11:30 1.138 1.133 1.136 1.347 1.77 2.112 2.031
12:00 1.167 1.125 1.168 1.335 1.76 2.118 2.034
12:30 1.196 1.117 1.2 1.324 1.75 2.124 2.037
13:00 1.239 1.143 1.209 1.339 1.748 2.072 1.988
13:30 1.282 1.169 1.219 1.354 1.746 2.021 1.94
14:00 1.292 1.281 1.231 1.392 1.775 1.97 1.862
14:30 1.302 1.393 1.244 1.43 1.804 1.919 1.784
15:00 1.375 1.321 1.31 1.454 1.838 1.853 1.678
15:30 1.447 1.248 1.376 1.477 1.872 1.788 1.572
16:00 1.671 1.337 1.448 1.504 1.887 1.665 1.468
16:30 1.894 1.425 1.521 1.531 1.902 1.542 1.363
17:00 2.237 1.447 1.606 1.47 1.714 1.419 1.241
17:30 2.579 1.469 1.691 1.41 1.525 1.296 1.119
18:00 2.518 1.414 1.647 1.377 1.314 1.214 1.038
18:30 2.458 1.36 1.604 1.343 1.103 1.132 0.956
19:00 2.086 1.394 1.54 1.33 0.973 0.799 0.733
19:30 1.715 1.429 1.476 1.318 0.843 0.466 0.511
20:00 1.417 1.445 1.314 1.195 0.724 0.462 0.498
20:30 1.119 1.461 1.153 1.071 0.604 0.459 0.485
21:00 0.963 1.396 1.054 0.971 0.52 0.384 0.427
21:30 0.807 1.331 0.954 0.871 0.437 0.31 0.37
22:00 0.705 1.301 0.893 0.807 0.384 0.338 0.381
22:30 0.602 1.271 0.832 0.744 0.331 0.365 0.393
23:00 0.525 1.287 0.748 0.745 0.3 0.409 0.424
23:30 0.447 1.304 0.665 0.747 0.269 0.453 0.455
00:00 0.346 1.235 0.539 0.681 0.237 0.452 0.453
00:30 0.246 1.167 0.412 0.616 0.206 0.451 0.451
01:00 0.185 1.04 0.323 0.449 0.178 0.408 0.415
01:30 0.125 0.914 0.234 0.282 0.151 0.365 0.378
02:00 0.097 0.671 0.2 0.243 0.143 0.392 0.391
02:30 0.069 0.428 0.165 0.205 0.134 0.419 0.404
03:00 0.067 0.362 0.153 0.195 0.154 0.368 0.379
03:30 0.066 0.297 0.14 0.186 0.173 0.317 0.354
04:00 0.089 0.287 0.155 0.212 0.222 0.428 0.442
04:30 0.113 0.277 0.17 0.238 0.272 0.539 0.53
05:00 0.206 0.355 0.234 0.381 0.354 0.725 0.688
05:30 0.299 0.434 0.299 0.524 0.436 0.911 0.847
06:00 0.726 0.586 0.56 0.748 0.968 1.239 1.203
06:30 1.153 0.737 0.821 0.972 1.5 1.566 1.559
07:00 1.676 0.813 1.124 1.225 1.832 1.552 1.703
07:30 2.199 0.89 1.427 1.478 2.163 1.539 1.847
08:00 2.47 0.908 1.519 1.491 1.947 1.557 1.807
08:30 2.74 0.925 1.611 1.504 1.732 1.576 1.767
09:00 2.264 0.937 1.488 1.554 1.748 1.769 1.871
09:30 1.787 0.949 1.366 1.605 1.763 1.963 1.976
10:00 1.515 1.025 1.283 1.564 1.799 2.045 2.04
10:30 1.243 1.101 1.201 1.523 1.834 2.127 2.105
11:00 1.211 1.138 1.189 1.459 1.834 2.158 2.105
11:30 1.18 1.174 1.177 1.396 1.835 2.189 2.105
12:00 1.207 1.163 1.207 1.381 1.82 2.19 2.103
12:30 1.233 1.152 1.237 1.366 1.805 2.191 2.101
13:00 1.275 1.176 1.245 1.378 1.799 2.133 2.047
13:30 1.317 1.201 1.252 1.391 1.794 2.076 1.993
14:00 1.329 1.317 1.266 1.432 1.825 2.025 1.914
14:30 1.34 1.434 1.28 1.472 1.856 1.974 1.836
15:00 1.416 1.36 1.349 1.497 1.892 1.908 1.728
15:30 1.491 1.286 1.418 1.522 1.929 1.843 1.62
16:00 1.721 1.377 1.492 1.549 1.944 1.715 1.512
16:30 1.95 1.468 1.566 1.576 1.959 1.588 1.404
17:00 2.318 1.499 1.663 1.522 1.774 1.469 1.285
17:30 2.686 1.53 1.761 1.469 1.589 1.35 1.166
18:00 2.635 1.48 1.723 1.44 1.374 1.27 1.086
18:30 2.583 1.43 1.686 1.412 1.16 1.189 1.005
19:00 2.182 1.456 1.608 1.389 1.017 0.836 0.767
19:30 1.78 1.482 1.531 1.366 0.874 0.483 0.529
20:00 1.471 1.498 1.363 1.239 0.75 0.479 0.516
20:30 1.162 1.515 1.196 1.111 0.626 0.475 0.503
21:00 1 1.448 1.093 1.007 0.539 0.398 0.443
21:30 0.838 1.381 0.989 0.903 0.452 0.322 0.383
22:00 0.732 1.349 0.926 0.837 0.398 0.35 0.395
22:30 0.625 1.318 0.863 0.772 0.343 0.378 0.407
23:00 0.545 1.335 0.776 0.773 0.311 0.424 0.439
23:30 0.464 1.352 0.69 0.774 0.279 0.47 0.471
00:00 0.355 1.261 0.552 0.696 0.243 0.461 0.462
00:30 0.247 1.171 0.414 0.618 0.207 0.452 0.453
01:00 0.186 1.044 0.324 0.45 0.179 0.409 0.416
01:30 0.125 0.917 0.235 0.283 0.152 0.366 0.38
02:00 0.097 0.673 0.2 0.244 0.143 0.393 0.393
02:30 0.069 0.429 0.166 0.205 0.135 0.421 0.406
03:00 0.067 0.363 0.153 0.196 0.154 0.369 0.381
03:30 0.066 0.298 0.141 0.187 0.174 0.318 0.356
04:00 0.09 0.288 0.155 0.213 0.223 0.43 0.444
04:30 0.114 0.278 0.17 0.238 0.273 0.541 0.532
05:00 0.207 0.357 0.235 0.382 0.355 0.728 0.691
05:30 0.3 0.436 0.3 0.526 0.437 0.915 0.851
06:00 0.729 0.588 0.562 0.751 0.972 1.243 1.208
06:30 1.157 0.739 0.823 0.976 1.507 1.572 1.566
07:00 1.695 0.822 1.136 1.238 1.851 1.567 1.721
07:30 2.233 0.904 1.449 1.501 2.196 1.562 1.876
08:00 2.496 0.918 1.535 1.507 1.97 1.574 1.827
08:30 2.759 0.932 1.621 1.514 1.743 1.586 1.779
09:00 2.273 0.94 1.493 1.559 1.753 1.774 1.877
09:30 1.786 0.949 1.365 1.604 1.762 1.962 1.975
10:00 1.518 1.028 1.286 1.568 1.803 2.05 2.046
10:30 1.249 1.107 1.207 1.531 1.844 2.139 2.116
11:00 1.219 1.145 1.197 1.469 1.847 2.172 2.119
11:30 1.189 1.183 1.186 1.407 1.849 2.206 2.121
12:00 1.212 1.168 1.213 1.387 1.828 2.2 2.112
12:30 1.235 1.153 1.239 1.367 1.807 2.193 2.103
13:00 1.278 1.179 1.247 1.381 1.802 2.137 2.051
13:30 1.321 1.204 1.255 1.395 1.798 2.081 1.998
14:00 1.333 1.321 1.27 1.436 1.83 2.031 1.92
14:30 1.344 1.439 1.284 1.477 1.862 1.981 1.842
15:00 1.421 1.364 1.353 1.502 1.899 1.915 1.734
15:30 1.497 1.29 1.423 1.527 1.936 1.849 1.626
16:00 1.733 1.386 1.502 1.559 1.956 1.726 1.521
16:30 1.968 1.481 1.58 1.591 1.977 1.603 1.417
17:00 2.317 1.5 1.664 1.524 1.777 1.471 1.287
17:30 2.666 1.519 1.748 1.458 1.577 1.34 1.157
18:00 2.623 1.473 1.716 1.434 1.368 1.264 1.081
18:30 2.58 1.428 1.684 1.41 1.158 1.188 1.004
19:00 2.183 1.457 1.61 1.391 1.018 0.837 0.768
19:30 1.786 1.487 1.536 1.372 0.877 0.485 0.531
20:00 1.476 1.504 1.368 1.243 0.753 0.481 0.518
20:30 1.166 1.52 1.201 1.115 0.629 0.477 0.505
21:00 1.004 1.453 1.097 1.011 0.542 0.4 0.445
21:30 0.841 1.385 0.993 0.906 0.454 0.323 0.385
22:00 0.734 1.354 0.929 0.84 0.399 0.351 0.397
22:30 0.627 1.322 0.866 0.775 0.344 0.38 0.409
23:00 0.546 1.34 0.779 0.776 0.312 0.426 0.441
23:30 0.466 1.357 0.692 0.777 0.28 0.472 0.473
00:00 0.357 1.269 0.555 0.7 0.244 0.464 0.465
00:30 0.249 1.181 0.418 0.623 0.208 0.456 0.457
01:00 0.188 1.053 0.327 0.454 0.181 0.413 0.42
01:30 0.126 0.925 0.237 0.285 0.153 0.369 0.383
02:00 0.098 0.679 0.202 0.246 0.145 0.397 0.396
02:30 0.069 0.433 0.167 0.207 0.136 0.424 0.409
03:00 0.068 0.367 0.155 0.198 0.156 0.372 0.384
03:30 0.066 0.3 0.142 0.189 0.175 0.321 0.359
04:00 0.091 0.29 0.157 0.215 0.225 0.433 0.448
04:30 0.115 0.28 0.172 0.24 0.275 0.546 0.536
05:00 0.209 0.36 0.237 0.385 0.358 0.734 0.697
05:30 0.303 0.44 0.303 0.53 0.441 0.923 0.858
06:00 0.735 0.593 0.567 0.757 0.98 1.254 1.218
06:30 1.167 0.746 0.831 0.984 1.519 1.585 1.578
07:00 1.707 0.828 1.144 1.247 1.864 1.578 1.733
07:30 2.247 0.909 1.458 1.51 2.209 1.572 1.887
08:00 2.506 0.921 1.541 1.514 1.978 1.581 1.835
08:30 2.764 0.933 1.625 1.517 1.747 1.59 1.782
09:00 2.282 0.945 1.5 1.567 1.762 1.783 1.886
09:30 1.8 0.956 1.375 1.617 1.776 1.977 1.99
10:00 1.524 1.031 1.291 1.573 1.809 2.057 2.053
10:30 1.249 1.107 1.206 1.53 1.843 2.137 2.115
11:00 1.217 1.143 1.194 1.466 1.842 2.168 2.114
11:30 1.185 1.179 1.182 1.401 1.842 2.198 2.113
12:00 1.214 1.17 1.215 1.389 1.831 2.204 2.116
12:30 1.244 1.162 1.248 1.377 1.82 2.209 2.119
13:00 1.296 1.195 1.264 1.4 1.827 2.166 2.079
13:30 1.347 1.228 1.281 1.423 1.834 2.123 2.038
14:00 1.359 1.348 1.295 1.465 1.867 2.072 1.958
14:30 1.371 1.467 1.31 1.506 1.9 2.021 1.879
15:00 1.443 1.387 1.375 1.526 1.93 1.946 1.762
15:30 1.515 1.306 1.44 1.546 1.96 1.872 1.646
16:00 1.746 1.397 1.514 1.572 1.973 1.741 1.535
16:30 1.977 1.488 1.588 1.598 1.986 1.61 1.423
17:00 2.339 1.513 1.679 1.537 1.791 1.483 1.298
17:30 2.701 1.538 1.77 1.477 1.597 1.357 1.172
18:00 2.657 1.492 1.738 1.452 1.385 1.28 1.094
18:30 2.613 1.446 1.705 1.428 1.173 1.203 1.017
19:00 2.207 1.473 1.627 1.405 1.029 0.846 0.776
19:30 1.801 1.5 1.549 1.383 0.885 0.489 0.536
20:00 1.489 1.517 1.38 1.254 0.759 0.485 0.522
20:30 1.176 1.534 1.211 1.125 0.634 0.481 0.509
21:00 1.012 1.465 1.106 1.019 0.546 0.403 0.448
21:30 0.848 1.397 1.001 0.914 0.458 0.326 0.388
22:00 0.741 1.366 0.937 0.848 0.403 0.354 0.4
22:30 0.633 1.334 0.873 0.781 0.347 0.383 0.412
23:00 0.551 1.351 0.786 0.782 0.315 0.429 0.445
23:30 0.47 1.369 0.698 0.784 0.283 0.476 0.477
00:00 0.358 1.271 0.557 0.702 0.245 0.465 0.466
00:30 0.247 1.174 0.415 0.619 0.207 0.453 0.454
01:00 0.186 1.047 0.325 0.451 0.179 0.41 0.417
01:30 0.126 0.92 0.235 0.283 0.152 0.367 0.38
02:00 0.097 0.675 0.201 0.245 0.144 0.394 0.393
02:30 0.069 0.43 0.166 0.206 0.135 0.422 0.407
03:00 0.068 0.364 0.154 0.197 0.155 0.37 0.382
03:30 0.066 0.299 0.141 0.187 0.174 0.319 0.356
04:00 0.09 0.288 0.156 0.213 0.224 0.431 0.445
04:30 0.114 0.278 0.171 0.239 0.273 0.542 0.533
05:00 0.207 0.358 0.236 0.383 0.356 0.73 0.692
05:30 0.301 0.437 0.301 0.527 0.438 0.917 0.852
06:00 0.73 0.589 0.563 0.752 0.974 1.246 1.21
06:30 1.159 0.741 0.825 0.978 1.509 1.575 1.568
07:00 1.677 0.815 1.125 1.226 1.834 1.555 1.706
07:30 2.195 0.888 1.424 1.475 2.158 1.535 1.843
08:00 2.456 0.903 1.511 1.483 1.938 1.549 1.798
08:30 2.718 0.918 1.598 1.492 1.718 1.563 1.753
09:00 2.25 0.932 1.479 1.546 1.738 1.76 1.861
09:30 1.781 0.946 1.361 1.6 1.757 1.956 1.969
10:00 1.51 1.022 1.279 1.559 1.793 2.039 2.034
10:30 1.239 1.098 1.197 1.519 1.828 2.121 2.099
11:00 1.216 1.143 1.194 1.465 1.842 2.167 2.114
11:30 1.193 1.188 1.19 1.412 1.856 2.214 2.129
12:00 1.22 1.176 1.221 1.396 1.84 2.214 2.126
12:30 1.247 1.164 1.251 1.38 1.824 2.214 2.124
13:00 1.29 1.19 1.259 1.394 1.82 2.158 2.071
13:30 1.334 1.216 1.268 1.408 1.816 2.102 2.017
14:00 1.346 1.334 1.282 1.45 1.848 2.052 1.939
14:30 1.358 1.453 1.297 1.492 1.881 2.001 1.861
15:00 1.431 1.375 1.364 1.514 1.914 1.93 1.748
15:30 1.505 1.297 1.43 1.535 1.946 1.859 1.634
16:00 1.741 1.392 1.509 1.567 1.966 1.734 1.529
16:30 1.977 1.488 1.587 1.598 1.986 1.61 1.423
17:00 2.335 1.511 1.677 1.535 1.789 1.482 1.296
17:30 2.693 1.534 1.766 1.473 1.593 1.353 1.169
18:00 2.659 1.493 1.739 1.453 1.386 1.281 1.095
18:30 2.625 1.452 1.713 1.434 1.178 1.208 1.021
19:00 2.207 1.472 1.626 1.404 1.029 0.847 0.777
19:30 1.789 1.491 1.54 1.374 0.879 0.486 0.532
20:00 1.479 1.508 1.372 1.246 0.754 0.482 0.519
20:30 1.168 1.524 1.203 1.117 0.63 0.478 0.505
21:00 1.005 1.457 1.099 1.013 0.542 0.401 0.445
21:30 0.843 1.389 0.995 0.908 0.455 0.324 0.385
22:00 0.736 1.358 0.931 0.842 0.4 0.352 0.397
22:30 0.628 1.326 0.868 0.776 0.345 0.38 0.409
23:00 0.547 1.343 0.781 0.777 0.313 0.427 0.442
23:30 0.466 1.361 0.694 0.779 0.281 0.473 0.474
00:00 0.337 1.188 0.526 0.66 0.23 0.439 0.439
00:30 0.207 1.015 0.358 0.54 0.18 0.405 0.403
01:00 0.156 0.905 0.281 0.394 0.156 0.367 0.37
01:30 0.105 0.795 0.203 0.247 0.132 0.328 0.337
02:00 0.082 0.583 0.173 0.213 0.125 0.352 0.349
02:30 0.058 0.372 0.144 0.179 0.118 0.377 0.361
03:00 0.057 0.315 0.133 0.171 0.134 0.331 0.339
03:30 0.055 0.258 0.122 0.163 0.151 0.285 0.317
04:00 0.075 0.249 0.134 0.186 0.194 0.385 0.395
04:30 0.095 0.241 0.147 0.208 0.238 0.485 0.473
05:00 0.174 0.309 0.204 0.334 0.309 0.653 0.615
05:30 0.252 0.378 0.26 0.46 0.381 0.821 0.757
06:00 0.612 0.509 0.486 0.656 0.847 1.115 1.075
06:30 0.972 0.641 0.712 0.853 1.313 1.409 1.393
07:00 1.155 0.599 0.783 0.905 1.309 1.144 1.223
07:30 1.338 0.556 0.854 0.957 1.305 0.88 1.053
08:00 1.567 0.584 0.944 0.979 1.217 0.967 1.115
08:30 1.796 0.612 1.035 1 1.129 1.054 1.177
09:00 1.606 0.683 1.07 1.145 1.279 1.342 1.411
09:30 1.416 0.753 1.105 1.289 1.429 1.63 1.644
10:00 1.286 0.884 1.13 1.359 1.592 1.83 1.84
10:30 1.155 1.015 1.155 1.428 1.756 2.03 2.035
11:00 1.167 1.09 1.185 1.411 1.818 2.15 2.118
11:30 1.178 1.165 1.215 1.394 1.88 2.27 2.201
12:00 1.213 1.161 1.247 1.385 1.87 2.288 2.2
12:30 1.247 1.157 1.279 1.376 1.859 2.306 2.199
13:00 1.299 1.19 1.289 1.395 1.858 2.235 2.138
13:30 1.351 1.224 1.299 1.413 1.857 2.164 2.076
14:00 1.328 1.307 1.28 1.423 1.84 2.048 1.938
14:30 1.305 1.39 1.261 1.433 1.823 1.932 1.8
15:00 1.335 1.28 1.27 1.407 1.784 1.791 1.629
15:30 1.365 1.17 1.278 1.382 1.745 1.651 1.458
16:00 1.517 1.212 1.289 1.367 1.688 1.476 1.298
16:30 1.669 1.254 1.3 1.351 1.631 1.301 1.138
17:00 1.951 1.268 1.358 1.293 1.457 1.202 1.045
17:30 2.232 1.281 1.416 1.234 1.283 1.103 0.953
18:00 2.262 1.284 1.472 1.26 1.17 1.114 0.955
18:30 2.293 1.286 1.529 1.285 1.057 1.126 0.958
19:00 1.897 1.288 1.429 1.242 0.911 0.78 0.715
19:30 1.501 1.289 1.329 1.199 0.765 0.435 0.473
20:00 1.24 1.303 1.184 1.087 0.657 0.431 0.461
20:30 0.98 1.318 1.038 0.975 0.548 0.427 0.449
21:00 0.843 1.259 0.949 0.883 0.472 0.358 0.396
21:30 0.707 1.201 0.859 0.792 0.396 0.289 0.342
22:00 0.617 1.174 0.804 0.734 0.348 0.315 0.353
22:30 0.527 1.147 0.749 0.677 0.3 0.34 0.363
23:00 0.459 1.162 0.674 0.678 0.272 0.381 0.392
23:30 0.391 1.176 0.599 0.679 0.244 0.423 0.421
00:00 0.203 0.899 0.53 0.55 0.179 0.221 0.245
00:30 0.015 0.622 0.46 0.421 0.114 0.019 0.07
01:00 0.012 0.523 0.367 0.315 0.094 0.017 0.061
01:30 0.009 0.425 0.275 0.209 0.075 0.014 0.052
02:00 0.007 0.357 0.231 0.168 0.065 0.014 0.052
02:30 0.005 0.288 0.188 0.128 0.055 0.014 0.052
03:00 0.006 0.262 0.181 0.136 0.054 0.017 0.062
03:30 0.007 0.237 0.174 0.145 0.053 0.02 0.073
04:00 0.007 0.231 0.187 0.2 0.067 0.023 0.083
04:30 0.007 0.226 0.2 0.255 0.081 0.026 0.093
05:00 0.008 0.257 0.254 0.399 0.138 0.044 0.156
05:30 0.01 0.287 0.308 0.542 0.194 0.062 0.219
06:00 0.014 0.304 0.404 0.691 0.304 0.082 0.288
06:30 0.018 0.32 0.501 0.839 0.413 0.102 0.357
07:00 0.024 0.365 0.6 0.932 0.533 0.118 0.413
07:30 0.029 0.409 0.7 1.025 0.653 0.134 0.468
08:00 0.032 0.481 0.761 1.033 0.66 0.124 0.433
08:30 0.035 0.553 0.823 1.041 0.667 0.114 0.398
09:00 0.038 0.646 0.976 1.057 0.666 0.099 0.347
09:30 0.041 0.738 1.129 1.073 0.665 0.085 0.297
10:00 0.045 0.795 1.281 1.021 0.682 0.082 0.288
10:30 0.049 0.852 1.433 0.969 0.698 0.08 0.28
11:00 0.047 0.889 1.511 0.915 0.712 0.074 0.259
11:30 0.046 0.926 1.589 0.861 0.726 0.068 0.238
12:00 0.049 0.912 1.639 0.844 0.714 0.062 0.219
12:30 0.052 0.897 1.689 0.827 0.703 0.057 0.2
13:00 0.052 0.908 1.705 0.828 0.695 0.054 0.19
13:30 0.052 0.919 1.721 0.83 0.688 0.051 0.18
14:00 0.055 0.925 1.725 0.847 0.676 0.051 0.178
14:30 0.058 0.931 1.729 0.863 0.665 0.05 0.177
15:00 0.053 0.954 1.717 0.878 0.65 0.048 0.17
15:30 0.049 0.978 1.704 0.892 0.634 0.046 0.163
16:00 0.052 0.982 1.694 0.915 0.619 0.045 0.159
16:30 0.056 0.986 1.684 0.938 0.605 0.044 0.155
17:00 0.054 1.005 1.693 0.931 0.597 0.044 0.154
17:30 0.052 1.025 1.702 0.924 0.59 0.043 0.153
18:00 0.054 1.027 1.717 0.935 0.586 0.045 0.16
18:30 0.056 1.03 1.733 0.946 0.582 0.047 0.167
19:00 0.05 0.982 1.558 0.884 0.523 0.047 0.167
19:30 0.045 0.934 1.383 0.821 0.465 0.047 0.166
20:00 0.04 0.871 1.239 0.78 0.413 0.055 0.194
20:30 0.035 0.807 1.095 0.739 0.362 0.063 0.221
21:00 0.032 0.754 1 0.719 0.328 0.065 0.227
21:30 0.03 0.701 0.905 0.699 0.294 0.066 0.233
22:00 0.029 0.699 0.874 0.723 0.281 0.063 0.222
22:30 0.029 0.697 0.842 0.747 0.269 0.06 0.21
23:00 0.027 0.679 0.761 0.77 0.255 0.065 0.227
23:30 0.025 0.661 0.68 0.793 0.241 0.069 0.244
00:00 0.131 0.893 0.539 0.693 0.22 0.252 0.34

Building energy profiles

Seasonal variations

This file records daily variations in total gas and electricity consumption over a wide area, so that seasonal variations are reconstructed by the model. The values in the files are converted to scaling factors when the file is read by the model software, so the unit of measurement is not important.

The file consists of three columns. The first is the day of year; the second and third must be headed “Elec” and “Gas” for electricity and gas consumption, respectively. Based on the start and end date chosen, the file must contain 365 or 366 entries. A truncated example of the file covering the first 7 days of the year is shown below to demonstrate the format:

Fuel Elec Gas
StartDate 2008-01-01
EndDate 2008-12-31
Timezone Europe/London
1 0.942515348 1.097280899
2 1.133871156 1.309574671
3 1.237227268 1.461329099
4 1.214487757 1.346215615
5 1.063433309 1.251089375
6 1.046604939 1.258738219
7 1.195052511 1.347154599
Diurnal variations

Each file contains triplets of 24-hour cycles at 30 minute resolution showing the relative variation of energy use during (i) a weekday, (ii) a Saturday and (iii) a Sunday.

Note that five separate input files must be provided for domestic electricity, domestic gas, industrial electricity, industrial gas and Economy 7 diurnal cycles. The link between file and energy type is made in the Data sources file.

Aside from the standard headers, this file contains headers for:

  • Season: A name for the period represented by each triplet of columns. Must be consistent within each triplet.
  • Day of week represented by the cycle: “Wd”: Weekday, “Sat”: Saturday or “Sun”: Sunday
  • Tariff: A brief description of tariff (for user information only)

The values for each day are normalised inside the model software so that they average to 1.

An example is shown below for a diurnal variations file that contains entries for Autumn (Aut), High Summer (HSr), Summer (Smr), Spring (Spr) and Winter (Wtr). Any number of seasons/periods of year can be added to a single file.

Season Aut Aut Aut HSr HSr HSr Smr Smr Smr Spr Spr Spr Wtr Wtr Wtr
Day Wd Sat Sun Wd Sat Sun Wd Sat Sun Wd Sat Sun Wd Sat Sun
Tariff DomUnr DomUnr DomUnr DomUnr DomUnr DomUnr DomUnr DomUnr DomUnr DomUnr DomUnr DomUnr DomUnr DomUnr DomUnr
StartDate 2014-09-01 2014-09-01 2014-09-01 2014-07-20 2014-07-20 2014-07-20 2014-05-11 2014-05-11 2014-05-11 2014-03-30 2014-03-30 2014-03-30 2013-10-27 2013-10-27 2013-10-27
EndDate 2014-10-25 2014-10-25 2014-10-25 2014-08-31 2014-08-31 2014-08-31 2014-07-19 2014-07-19 2014-07-19 2014-05-10 2014-05-10 2014-05-10 2014-03-29 2014-03-29 2014-03-29
Timezone Europe/London
00:30 0.31 0.33 0.339 0.315 0.325 0.324 0.314 0.333 0.344 0.338 0.351 0.366 0.352 0.387 0.391
01:00 0.273 0.294 0.306 0.287 0.291 0.296 0.276 0.301 0.306 0.304 0.312 0.312 0.313 0.344 0.348
01:30 0.252 0.268 0.277 0.26 0.269 0.276 0.256 0.271 0.28 0.279 0.304 0.286 0.294 0.322 0.32
02:00 0.236 0.248 0.259 0.242 0.249 0.255 0.247 0.249 0.259 0.258 0.262 0.271 0.278 0.3 0.299
02:30 0.23 0.24 0.249 0.234 0.238 0.243 0.229 0.236 0.241 0.25 0.251 0.26 0.266 0.284 0.283
03:00 0.228 0.238 0.24 0.228 0.231 0.234 0.221 0.23 0.233 0.244 0.25 0.255 0.258 0.269 0.27
03:30 0.224 0.233 0.235 0.223 0.23 0.229 0.22 0.23 0.228 0.239 0.239 0.244 0.258 0.269 0.268
04:00 0.219 0.228 0.227 0.222 0.224 0.221 0.215 0.222 0.222 0.236 0.235 0.249 0.255 0.263 0.264
04:30 0.221 0.226 0.224 0.22 0.22 0.221 0.217 0.222 0.219 0.238 0.249 0.247 0.256 0.261 0.261
05:00 0.226 0.226 0.227 0.222 0.225 0.218 0.221 0.219 0.213 0.245 0.243 0.251 0.266 0.266 0.263
05:30 0.234 0.233 0.23 0.23 0.226 0.219 0.225 0.224 0.224 0.251 0.252 0.254 0.276 0.273 0.265
06:00 0.254 0.245 0.238 0.238 0.228 0.229 0.238 0.221 0.222 0.263 0.251 0.258 0.302 0.288 0.28
06:30 0.302 0.264 0.251 0.266 0.245 0.239 0.27 0.25 0.236 0.308 0.264 0.276 0.36 0.316 0.295
07:00 0.355 0.297 0.275 0.304 0.267 0.248 0.328 0.274 0.249 0.358 0.312 0.309 0.418 0.345 0.312
07:30 0.425 0.34 0.307 0.34 0.299 0.28 0.37 0.314 0.28 0.412 0.368 0.358 0.499 0.394 0.357
08:00 0.477 0.395 0.349 0.387 0.333 0.321 0.406 0.357 0.318 0.462 0.415 0.39 0.56 0.454 0.42
08:30 0.479 0.46 0.43 0.415 0.396 0.371 0.427 0.409 0.377 0.482 0.472 0.441 0.581 0.527 0.485
09:00 0.487 0.509 0.48 0.448 0.444 0.423 0.451 0.459 0.433 0.51 0.496 0.503 0.586 0.591 0.559
09:30 0.484 0.523 0.521 0.453 0.466 0.452 0.45 0.478 0.47 0.512 0.532 0.514 0.582 0.616 0.602
10:00 0.473 0.542 0.532 0.456 0.487 0.465 0.449 0.501 0.488 0.505 0.526 0.562 0.574 0.645 0.648
10:30 0.463 0.546 0.542 0.45 0.477 0.491 0.444 0.5 0.496 0.492 0.512 0.572 0.556 0.637 0.675
11:00 0.45 0.51 0.567 0.442 0.451 0.496 0.43 0.479 0.506 0.488 0.543 0.566 0.549 0.635 0.685
11:30 0.452 0.51 0.581 0.443 0.458 0.49 0.436 0.485 0.506 0.483 0.573 0.566 0.539 0.622 0.691
12:00 0.448 0.502 0.576 0.44 0.455 0.488 0.438 0.481 0.521 0.482 0.537 0.551 0.54 0.61 0.718
12:30 0.46 0.509 0.59 0.443 0.469 0.508 0.443 0.494 0.513 0.485 0.467 0.571 0.555 0.62 0.728
13:00 0.458 0.507 0.591 0.439 0.463 0.517 0.441 0.485 0.515 0.481 0.482 0.546 0.557 0.626 0.731
13:30 0.451 0.501 0.584 0.433 0.462 0.506 0.423 0.463 0.514 0.478 0.459 0.544 0.549 0.619 0.714
14:00 0.436 0.487 0.552 0.421 0.452 0.476 0.419 0.457 0.482 0.465 0.464 0.511 0.536 0.603 0.693
14:30 0.425 0.473 0.545 0.407 0.445 0.465 0.412 0.455 0.477 0.452 0.444 0.494 0.532 0.591 0.673
15:00 0.431 0.478 0.539 0.402 0.434 0.469 0.41 0.464 0.474 0.458 0.453 0.526 0.546 0.605 0.677
15:30 0.438 0.47 0.557 0.405 0.433 0.473 0.415 0.455 0.479 0.456 0.452 0.514 0.563 0.615 0.699
16:00 0.468 0.478 0.563 0.417 0.431 0.483 0.434 0.466 0.487 0.475 0.437 0.515 0.606 0.638 0.733
16:30 0.507 0.494 0.595 0.444 0.434 0.499 0.459 0.464 0.502 0.499 0.472 0.544 0.672 0.674 0.784
17:00 0.554 0.533 0.629 0.482 0.459 0.515 0.5 0.477 0.528 0.543 0.51 0.577 0.762 0.755 0.849
17:30 0.617 0.594 0.684 0.538 0.495 0.556 0.553 0.516 0.547 0.6 0.536 0.636 0.856 0.849 0.927
18:00 0.65 0.649 0.698 0.547 0.529 0.58 0.564 0.545 0.56 0.637 0.593 0.666 0.9 0.897 0.95
18:30 0.683 0.684 0.737 0.56 0.54 0.574 0.564 0.542 0.56 0.649 0.619 0.667 0.921 0.921 0.945
19:00 0.723 0.691 0.763 0.569 0.546 0.564 0.568 0.563 0.564 0.652 0.617 0.661 0.907 0.91 0.921
19:30 0.716 0.697 0.765 0.562 0.549 0.562 0.557 0.549 0.557 0.643 0.571 0.665 0.874 0.866 0.886
20:00 0.709 0.665 0.757 0.545 0.526 0.545 0.546 0.524 0.554 0.649 0.623 0.635 0.832 0.822 0.854
20:30 0.692 0.645 0.732 0.553 0.55 0.554 0.538 0.51 0.555 0.664 0.611 0.669 0.801 0.779 0.82
21:00 0.661 0.622 0.685 0.555 0.541 0.556 0.533 0.506 0.546 0.659 0.587 0.636 0.764 0.739 0.781
21:30 0.634 0.592 0.657 0.561 0.53 0.556 0.541 0.506 0.551 0.655 0.569 0.65 0.73 0.705 0.739
22:00 0.593 0.572 0.606 0.548 0.522 0.54 0.54 0.514 0.545 0.622 0.569 0.602 0.689 0.672 0.691
22:30 0.555 0.54 0.562 0.523 0.497 0.508 0.524 0.499 0.533 0.591 0.552 0.589 0.644 0.63 0.643
23:00 0.496 0.488 0.497 0.474 0.469 0.467 0.481 0.481 0.485 0.532 0.503 0.513 0.566 0.576 0.57
23:30 0.423 0.443 0.423 0.415 0.424 0.404 0.438 0.43 0.425 0.461 0.469 0.396 0.487 0.518 0.485
00:00 0.36 0.393 0.358 0.359 0.374 0.353 0.377 0.396 0.366 0.39 0.367 0.335 0.414 0.452 0.415

Metabolic activity

Metabolism profiles contain multiple seasons per file and describe the variation in metabolic activity of the whole population on the average weekday, Saturday and Sunday at 30-minute intervals. Each weekday, Saturday and Sunday has 2 columns: Energy emitted per person, and Fraction of residents who are at work at each point in the day. Both workers and residents are assumed to emit the same amount of heat per person at each time of day.

Headers specific to this file:

  • Season: A name for the season being described. Must be consistent within all six columns describing a season
  • Day: “Weekday”, “Saturday” or “Sunday”, exactly as shown below
  • Type: “Energy” and “Fraction” as described above.
Season GMT GMT GMT GMT GMT GMT BST BST BST BST BST BST GMT2 GMT2 GMT2 GMT2 GMT2 GMT2
Day Weekday Weekday Saturday Saturday Sunday Sunday Weekday Weekday Saturday Saturday Sunday Sunday Weekday Weekday Saturday Saturday Sunday Sunday
Type Energy Fraction Energy Fraction Energy Fraction Energy Fraction Energy Fraction Energy Fraction Energy Fraction Energy Fraction Energy Fraction
StartDate 2008-01-01 2008-01-01 2008-01-01 2008-01-01 2008-01-01 2008-01-01 2008-03-30 2008-03-30 2008-03-30 2008-03-30 2008-03-30 2008-03-30 2008-10-26 2008-10-26 2008-10-26 2008-10-26 2008-10-26 2008-10-26
EndDate 2008-03-29 2008-03-29 2008-03-29 2008-03-29 2008-03-29 2008-03-29 2008-10-25 2008-10-25 2008-10-25 2008-10-25 2008-10-25 2008-10-25 2008-12-31 2008-12-31 2008-12-31 2008-12-31 2008-12-31 2008-12-31
Timezone Europe/London
00:30 64.3 0 0 0 0 0 64.3 0 0 0 0 0 64.3 0 0 0 0 0
01:00 64.3 0 0 0 0 0 64.3 0 0 0 0 0 64.3 0 0 0 0 0
01:30 64.3 0 0 0 0 0 64.3 0 0 0 0 0 64.3 0 0 0 0 0
02:00 64.3 0 0 0 0 0 64.3 0 0 0 0 0 64.3 0 0 0 0 0
02:30 64.3 0 0 0 0 0 64.3 0 0 0 0 0 64.3 0 0 0 0 0
03:00 64.3 0 0 0 0 0 64.3 0 0 0 0 0 64.3 0 0 0 0 0
03:30 64.3 0 0 0 0 0 64.3 0 0 0 0 0 64.3 0 0 0 0 0
04:00 64.3 0 0 0 0 0 64.3 0 0 0 0 0 64.3 0 0 0 0 0
04:30 64.3 0 0 0 0 0 64.3 0 0 0 0 0 64.3 0 0 0 0 0
05:00 64.3 0 0 0 0 0 64.3 0 0 0 0 0 64.3 0 0 0 0 0
05:30 64.3 0 0 0 0 0 64.3 0 0 0 0 0 64.3 0 0 0 0 0
06:00 64.3 0 0 0 0 0 64.3 0 0 0 0 0 64.3 0 0 0 0 0
06:30 64.3 0 0 0 0 0 64.3 0 0 0 0 0 64.3 0 0 0 0 0
07:00 68 0 0 0 0 0 68 0 0 0 0 0 68 0 0 0 0 0
07:30 80 0.02 0 0 0 0 80 0.02 0 0 0 0 80 0.02 0 0 0 0
08:00 110 0.08 0 0 0 0 110 0.08 0 0 0 0 110 0.08 0 0 0 0
08:30 150 0.2 0 0 0 0 150 0.2 0 0 0 0 150 0.2 0 0 0 0
09:00 166 0.4 0 0 0 0 166 0.4 0 0 0 0 166 0.4 0 0 0 0
09:30 170.5 0.6 0 0 0 0 170.5 0.6 0 0 0 0 170.5 0.6 0 0 0 0
10:00 170.5 0.9 0 0 0 0 170.5 0.9 0 0 0 0 170.5 0.9 0 0 0 0
10:30 170.5 0.98 0 0 0 0 170.5 0.98 0 0 0 0 170.5 0.98 0 0 0 0
11:00 170.5 1 0 0 0 0 170.5 1 0 0 0 0 170.5 1 0 0 0 0
11:30 170.5 1 0 0 0 0 170.5 1 0 0 0 0 170.5 1 0 0 0 0
12:00 170.5 1 0 0 0 0 170.5 1 0 0 0 0 170.5 1 0 0 0 0
12:30 170.5 1 0 0 0 0 170.5 1 0 0 0 0 170.5 1 0 0 0 0
13:00 170.5 1 0 0 0 0 170.5 1 0 0 0 0 170.5 1 0 0 0 0
13:30 170.5 1 0 0 0 0 170.5 1 0 0 0 0 170.5 1 0 0 0 0
14:00 170.5 1 0 0 0 0 170.5 1 0 0 0 0 170.5 1 0 0 0 0
14:30 170.5 1 0 0 0 0 170.5 1 0 0 0 0 170.5 1 0 0 0 0
15:00 170.5 1 0 0 0 0 170.5 1 0 0 0 0 170.5 1 0 0 0 0
15:30 170.5 1 0 0 0 0 170.5 1 0 0 0 0 170.5 1 0 0 0 0
16:00 170.5 1 0 0 0 0 170.5 1 0 0 0 0 170.5 1 0 0 0 0
16:30 170.5 1 0 0 0 0 170.5 1 0 0 0 0 170.5 1 0 0 0 0
17:00 170.5 0.98 0 0 0 0 170.5 0.98 0 0 0 0 170.5 0.98 0 0 0 0
17:30 170.5 0.9 0 0 0 0 170.5 0.9 0 0 0 0 170.5 0.9 0 0 0 0
18:00 170.5 0.6 0 0 0 0 170.5 0.6 0 0 0 0 170.5 0.6 0 0 0 0
18:30 170.5 0.4 0 0 0 0 170.5 0.4 0 0 0 0 170.5 0.4 0 0 0 0
19:00 170.5 0.2 0 0 0 0 170.5 0.2 0 0 0 0 170.5 0.2 0 0 0 0
19:30 170.5 0.08 0 0 0 0 170.5 0.08 0 0 0 0 170.5 0.08 0 0 0 0
20:00 170.5 0.02 0 0 0 0 170.5 0.02 0 0 0 0 170.5 0.02 0 0 0 0
20:30 170.5 0 0 0 0 0 170.5 0 0 0 0 0 170.5 0 0 0 0 0
21:00 170.5 0 0 0 0 0 170.5 0 0 0 0 0 170.5 0 0 0 0 0
21:30 170.5 0 0 0 0 0 170.5 0 0 0 0 0 170.5 0 0 0 0 0
22:00 166 0 0 0 0 0 166 0 0 0 0 0 166 0 0 0 0 0
22:30 150 0 0 0 0 0 150 0 0 0 0 0 150 0 0 0 0 0
23:00 110 0 0 0 0 0 110 0 0 0 0 0 110 0 0 0 0 0
23:30 80 0 0 0 0 0 80 0 0 0 0 0 80 0 0 0 0 0
00:00 68 0 0 0 0 0 68 0 0 0 0 0 68 0 0 0 0 0

Fuel consumption file

This file provides the fuel consumption of each Euro-class on urban roads and motorways, broken down by vehicle type and euro-class. Each euro-class corresponds to vehicles manufacturers on/after a certain date. This information is used with assumed vehicle age to capture the time evolution of fuel efficiency.

The layout of this file is distinct from the other temporal files shown here, but the column headings, vehicle names and fuel types must be exactly as shown here. Since this is a CSV file, the reference text must also contain no commas (,).

Reference

<reference text e.g. URL>

StartDate Fuel vehicle Standard urban rural_single rural_dual motorway
1996-01-01 Petrol car Euro II 57.6 46.8 72.3 69
1996-01-01 Diesel car Euro II 42.4 30.1 36.2 35.1
1996-01-01 Petrol lgv Euro II 76.6 60.4 90.7 86.6
1996-01-01 Diesel lgv Euro II 88.3 75.8 101.6 98.2
1996-01-01 Petrol taxi Euro II 57.6 46.8 72.3 69
1996-01-01 Diesel taxi Euro II 42.4 30.1 36.2 35.1
1996-01-01 Petrol motorcycle Euro II 30.1 33.1 38.7 38.2
1996-01-01 Diesel motorcycle Euro II 0 0 0 0
1996-01-01 Petrol rigid Euro II 0 0 0 0
1996-01-01 Diesel rigid Euro II 168 155 175 181
1996-01-01 Petrol artic Euro II 0 0 0 0
1996-01-01 Diesel artic Euro II 364 299 311 319
1996-01-01 Petrol bus Euro II 0 0 0 0
1996-01-01 Diesel bus Euro II 415 203 202 206

Further spatial disaggregation

This is optional. It assigns transport, building and metabolism heat fluxes to only those regions of that map with compatible land covers. Since land cover fraction data are often available at high spatial resolution, this increases the resolution of the model outputs beyond the output areas that were specified initially.

Each model output area is divided into a number of "refined output areas" (ROAs). The land cover fraction lists the proportion of each ROA occupied by:

  • Water
  • Paved surfaces
  • Buildings
  • Soil
  • Low vegetation
  • High vegetation
  • Grass

The GQF user interface requires two input files for this process.

  • Land cover fractions: Land cover fractions calculated using the UMEP land cover classifier in the pre-processing toolbox.
  • Corresponding polygon grid: The ESRI shapefile grid of polygons represented by the land cover fractions. This is a required input for the UMEP land cover classifier.

Note that this feature may be very slow and memory limitations may cause it to fail or produce very large output files.

The overall building, transport and metabolic QF components in an MOA are attributed to each ROA based on a set of weightings that associate land cover classes with QF components.

A fixed set of weightings determines the behaviour of this routine and ensure the following principles are satisfied:

  1. Transport heat flux only occurs on paved areas (roads)
  2. Building heat flux only occurs where there are buildings
  3. Metabolic energy reflects the distribution of people between indoor and outdoor environments
Land cover class Weightings (columns must sum to 1)
QF,B QF,M QF,T
Building 1 0.8 0
Paved 0 0.05 1
Water 0 0.0 0
Soil 0 0.05 0
Grass 0 0.05 0
High vegetation 0 0.0 0
Low vegetation 0 0.05 0


Current limitations:

  • Building height not accounted for: same fraction of QF would be assigned to a very tall building and short building if they occupied the same footprint, despite the former being likely to emit more heat per square metre of the surface it occupies
  • Land cover data: assumed to be consistent with the original input data. If non-zero building energy is calculated in an MOA that has a building land cover of zero, then this energy is lost.

Configuration data

The GQF software has two input files:

  • Data sources file: Manages the various input data files and their associated metadata
  • Parameters file: Contains numerical values and assumptions used in model calculations.

Parameters file

The GQF parameters file contains public holidays and numeric values used in calculations. The table below describes the entries in each parameters file

Parameter name Description
params: Model run parameters
  • city
Area model is being run for. Expressed in Continent/City format (e.g. Europe/London)
  • use_uk_holidays
Set to 1 to use UK public holidays (calculated automatically) or 0 otherwise
  • use_custom_holidays
Set to 1 to use a list of public holidays (specified separately) or 0 otherwise
  • custom_holidays
A list of custom public holidays in YYYY-mm-dd format.
  • heaterEffic_elec
Electrical heating efficiency (values from 0 to 1)
  • heaterEffic_gas
Gas heating efficiency (values from 0 to 1)
  • metabolicLatentHeatFract
Fraction of metabolic partitioned into latent heat (values from 0 to 1)
  • metabolicSensibleHeatFract
Fraction of metabolic heat partitioned into sensible heat (values from 0 to 1)
  • vehicleAge
Assumed vehicle age (years) relative to the current model time step
waterHeatingFractions: Fraction of building energy spent on heating water

Values from 0 to 1

  • domestic_elec
Domestic electricity
  • domestic_gas
Domestic gas
  • industrial_elec
Industrial electricity
  • industrial_gas
Industrial gas
  • industrial_other
Other industrial energy sources
heatOfCombustion: Heat of combustion for different fuels

Two values per entry: net and gross (respectively) [MJ/kg]

  • natural_gas
Natural gas
  • Petrol_Fuel
Petrol
  • Diesel_Fuel
Diesel
  • Crude_Oil
Crude Oil
petrolDieselFractions: Vehicle fuel fractions

Two values per entry: petrol and diesel (respectively). Must be between 0 and 1

  • motorcycle
Motorcycles
  • taxi
Taxis
  • car
Cars
  • bus
Buses (and long-distance coaches)
  • lgv
LGVs
  • rigid
Rigid HGVs
  • artic
Articulated HGVs
vehicleFractions: Breakdown of vehicle types by road classification

Each entry contains 7 values respectively for car, LGV, motorcycle, taxi, bus, rigid, artic Values in each entry must sum to 1. Used when transport shapefile does not include vehicle-specific AADT

  • motorway
Motorways
  • primary_road
Primary roads
  • secondary_road
Secondary roads
  • other
Other roads
roadSpeeds: Default speeds [km/h] traffic speeds for each road classification

Used if transport shapefile does not provide speeds for each road segment

  • motorway
Motorway speed
  • primary_road
Primary road speed
  • secondary_road
Secondary road speed
  • other
Other road speed
roadAADTs: Default AADTs (annual average daily total) for each road classification

Used if transport shapefile does not provide AADTs for each road segment

  • motorway
Motorway AADT
  • primary_road
Primary AADT
  • secondary_road
Secondary AADT
  • other
Other AADT

Example parameters file

A model configuration for the UK, with two more public holidays than are ordinarily present. Cars make up the majority of the transport fleet, and the majority of cars are found on motorways and primary roads. All other vehicles are found exclusively on primary roads.

&params
   use_uk_holidays = 1 
   use_custom_holidays = 0
   custom_holidays = '2000-10-30', '2000-11-14 
   heaterEffic_elec = 0.98
   heaterEffic_gas = 0.85
   metabolicLatentHeatFract = 0.3
   metabolicSensibleHeatFract = 0.7
/
&waterHeatingFractions
   domestic_elec = 0.139
   domestic_gas = 0.27
   industrial_elec = 0.036
   industrial_gas = 0.146
   industrial_other = 0.084
/
&heatOfCombustion
   natural_gas = 35.5, 39.4
   Petrol_Fuel = 44.7, 47.1
   Diesel_Fuel = 43.3, 45.5
   Crude_Oil = 43.4, 45.7
/
&petrolDieselFractions
   motorcycle = 1,0
   taxi = 0,1
   car = 0.84, 0.16
   bus = 0,1
   lgv = 0.1, 0.9
   rigid = 0,1
   artic = 0, 1
/

&vehicleFractions
  ! Overall fractions of the fleet 
  fractions =      0.4,  0.1, 0.1, 0.1, 0.1, 0.1, 0.1

  ! Proportions of each vehicle found on different types of road
  motorway =       0.4,  0,   0,    0,   0,   0,0    
  primary_road =   0.4,  1,   1,    1,   1,   1,1
  secondary_road = 0.15, 0,   0,    0,   0,   0,0  
  other =          0.05, 0,   0,    0,   0,   0,0       
/
 
&roadSpeeds
   motorway = 80       
   primary_road = 60   
   secondary_road = 40
   other = 20          
/ 
&roadAADTs
   motorway = 8000        
   primary_road = 4000    
   secondary_road = 2000   
   other = 10        
/

Data sources file

The data sources file manages the library of shapefiles and temporal profile files used by the model. It is divided into a number of sections (described below).

Output areas

The shapefile that defines the model output areas to be used: all input data are disaggregated into these spatial units, and the model results are shown using them. There are three entries:

Parameter Description
Shapefile Location of the shapefile on the local machine
epsgCode EPSG code (numeric) of the shapefile coordinate reference system
featureIds Column that contains a unique identifier for each output area (optional: order of the output areas in the file is used if empty). This is used for cross-referencing and is shown in the model outputs.

An example:

&outputAreas
   shapefile = 'C:\GreaterQF\PopDens_2014.shp'
   epsgCode = 27700
   featureIds = 'LSOA11CD' 
/

Spatial data: Population and energy use shapefiles

The population and energy use shapefiles are specified using a standardised pattern, each of which consists of four entries:

Parameter Description
shapefiles Location of the shapefile(s) on the local machine
startDates Start of the time period(s) represented by the shapefile(s) (YYYY-mm-dd format)
epsgCodes EPSG code (numeric) of the shapefile(s) coordinate reference system
attribToUse Attribute(s) of the input shapefile(s) that contains the data of interest

Entries for the residentialPop and workplacePop sections of the data sources file (residential and workplace population data) example:

&residentialPop
   shapefiles = 'C:\GreaterQF\popOA2014.shp'
   startDates = '2014-01-01'
   epsgCodes = 27700
   attribToUse = 'Pop'
/
&workplacePop
   shapefiles ='C:/GreaterQF/2011OAworkdaypop.shp'
   startDates = '2014-01-01'
   epsgCodes = 27700
   attribToUse = 'WorkPop'
/

Same patterns are used to specify energy consumption data. The full list of input shapefile section headings are:

Parameter Description
residentialPop Residential population
workplacePop Workday (daytime) population
annualIndGas Annual industrial gas consumption
annualIndElec Annual industrial electricity consumption
annualDomGas Annual domestic gas consumption
annualDomElec Annual domestic electricity consumption
annualEco7 Annual domestic economy 7 electricity consumption

Specifying multiple shapefiles per section

The examples above show the use of a single shapefile for each energy and population data, but multiple shapefiles can also be used in order to capture variations over time. This is achieved by using a list of values. An example is shown below for residential population, in which populations for 2014 and 2015 are added and different CRS and attributes are used for each file:

&residentialPop
   shapefiles = 'C:\GreaterQF\popOA2014.shp', 'C:\GreaterQF\popOA2015.shp', 
   startDates = '2014-01-01', '2015-01-01'
   epsgCodes = 27700, 32631
   attribToUse = 'Pop', 'Pop2015'
/

Note that a “startDate”, “epsgCode” and “attribToUse” must be specified for every shapefile.

Temporal data: Metabolism, energy use and transportation temporal profiles

Temporal profile files are each added using the same pattern, with a list of “profileFiles” added for each category. A complete list is shown below as an example:

&dailyEnergyUse
   ! Daily variations in total power use
   profileFiles = 'C:\\GreaterQF\\testDailyEnergy.csv'
/
&diurnalDomElec
   ! Diurnal variations in total domestic electricity use (metadata provided in file; files can contain multiple seasons)
   profileFiles = C:\\GreaterQF\\BuildingLoadings_DomUnre.csv'
/
&diurnalDomGas
   ! Diurnal variations in total domestic gas use (metadata provided in file; files can contain multiple seasons)
   profileFiles = C:\\GreaterQF\\BuildingLoadings_DomUnre.csv'
/
&diurnalIndElec
   ! Diurnal variations in total industrial electricity use (metadata provided in file; files can contain multiple seasons)
   profileFiles = C:\\GreaterQF\\BuildingLoadings_Industrial.csv'
/
&diurnalIndGas
   ! Diurnal variations in total industrial gas use (metadata provided in file; files can contain multiple seasons)
   profileFiles = 'C:\\GreaterQF\\BuildingLoadings_Industrial.csv'
/
&diurnalEco7
   ! Diurnal variations in total economy 7 electricity use (metadata provided in file; files can contain multiple seasons)
   profileFiles = 'C:\\GreaterQF\\BuildingLoadings_EC7.csv'
/

! Temporal transport data
&diurnalTraffic
   ! Diurnal cycles of transport flow for different vehicle types
   profileFiles = 'C:\\GreaterQF\\testTransport.csv'
/

! Temporal metabolism data
&diurnalMetabolism
   profileFiles = 'C:\\GreaterQF\\testMetabolism.csv'
/
&fuelConsumption
   ! File containing fuel consumption performance data for each vehicle type as standards change over the years
   profileFiles = 'C:\\GreaterQF\\fuelConsumption.csv'
/

The section headings in the data sources file must exactly match those shown. The complete list of required section headings is:

Section header Model input
dailyEnergyUse Daily variations in energy consumption
diurnalDomElec Seasonal diurnal cycles: Domestic electricity
diurnalDomGas Seasonal diurnal cycles: Domestic gas
diurnalIndElec Seasonal diurnal cycles: Industrial electricity
diurnalIndGas Seasonal diurnal cycles: Industrial gas
diurnalEco7 Annual domestic electricity consumption
annualEco7 Seasonal diurnal cycles: Economy 7 electricity
diurnalTraffic Traffic weekly cycles
diurnalMetabolism Seasonal diurnal cycles: Metabolism
fuelConsumption Fuel consumption file

Using multiple temporal profile files

As with shapefiles, multiple temporal profile files can be loaded into the model to capture different periods of time. All of the data is combined into a single file inside the model, provided that none of the periods described within the files clash.

Transport data

The transport data shapefile section is longer than the others because the model must deal with different levels of data describing traffic flow and speed for each road segment. Traffic flow data are read in as the Annual Average Daily Traffic (AADT; equivalent to vehicle kilometres divided by road segment length), defined as the mean number of vehicles passing over the road segment daily.

The data availability scenarios covered by the software are as listed below. These correspond directly to the scenarios shown in (TABLE IN PREVIOUS SECTION).

Scenario Available data
Minimum data Classification for every road segment that allows a default AADT and speed to be applied. Default values are specified in the parameters file.
diurnalDomElec Seasonal diurnal cycles: Domestic electricity
Scenario 1a Minimum data + Total AADT available for each road segment
Scenario 1b Minimum data + Total AADT + Mean speed available for each road segment
Scenario 2a Minimum data + Vehicle-specific AADT available for each road segment
Scenario 2b Minimum data + Vehicle-specific AADT + Mean speed available for each road segment
Scenario 3 Minimum data + Vehicle-specific AADT, with car and LGV AADTs further broken down into diesel and petrol vehicles, and bus AADT broken down into local buses and long-distance coaches.
Best case Scenario 3 + Mean speed for each road segment

Gap-filling for incomplete data

If AADT or speed is generally available in the shapefile but found to be missing for a particular road segment, the software will attempt to gap-fill using mean value from the nearest ten road segments with the same classification. Default or calculated values for speed and/or vehicle-specific AADT in each road segment are required in all but the best-case scenario. These are based upon values stored in the GQF parameters file.


Transport parameters

The table below shows the parameters used in the transport section of the data sources file.

The shapefile(s) to load are specified using the same format as in the energy and population sections. Additionally, there are flags to signal whether certain types of data are available in the shapefile(s), and mappings to shapefile attributes so that the software refers to the correct input data.

Name Description When used
Standard shapefile descriptors
shapefiles One or more shapefiles containing road segments, classifications and (optionally) traffic counts and speeds for each road segment Always
startDates Start date(s) for shapefile(s) Always
epsgCodes Numeric EPSG code(s) for shapefile(s) Always
Data availability flags (1=True, 0=False) – applies to all transport shapefiles
speed_available Speed data is available for each road segment Always
total_AADT_available Total AADT is available for each road segment Always
vehicle_AADT_available AADT for each vehicle type is available for each road segment Always
Information on road segment classifications

Note: Any other classifications in the shapefile are treated as “other”: small local roads.

class_field Attribute name: road segment classification field Always
motorway_class The name used for motorways Always
primary_class The name used for primary roads (UK “A” roads) Always
secondary_class The name used for secondary roads (UK “B” roads) Always
Shapefile attribute names
speed_field Speed of each road segment speed_available = 1
speed_multiplier A multiplicative conversion factor to convert the speed data to km h-1 speed_available = 1
AADT_total Total AADT for all vehicle types total_AADT_available=1 and vehicle_AADT_available=0
Attribute names for vehicle-specific AADT vehicle_AADT_available = 1
AADT_diesel_car AADT of diesel cars AADT_petrol_car and AADT_diesel_car filled in
AADT_petrol_car AADT of petrol cars AADT_petrol_car and AADT_diesel_car filled in
AADT_total_car AADT for all cars (used when ) AADT_petrol_car =’’and/or AADT_petrol_car =’’
AADT_diesel_LGV AADT of diesel LGVs AADT_petrol_LGV and AADT_diesel_LGV filled in
AADT_petrol_LGV AADT of petrol LGVs AADT_petrol_LGV and AADT_diesel_LGV filled in
AADT_total_LGV AADT for all cars AADT_petrol_car =’’ and/or AADT_petrol_car =’’
AADT_motorcycle AADT of all motorcycles
AADT_taxi AADT of all motorcycles
AADT_bus AADT ofbuses
AADT_coach AADT of long-distance coaches If specified
AADT_rigid AADT of all rigid HGVs
AADT_artic AADT of all articulated HGVs