Build results scaffolding

[willu47]

Put together a Python script or Jupyter notebook which creates a set of plots which enables comparison of results from across the model runs.

List of plots required

Scenarios

• [x] population by LAD, timestep
• [x] GVA by LAD, timestep
• [x] jobs by LAD, timestep
• [x] dwellings by LAD, timestep

Energy Supply

• [x] Total emissions (from electricity + heat sector supply points) by LAD, timestep
• [x] Electricity supply capacity by generation type, timestep
• [ ] Heat supply capacity by generation type, timestep
• [ ] Gas supply capacity by generation type, timestep
• [ ] Hydrogen supply capacity (if available)

Energy Demand

• [ ] Total heating demand by LAD, timestep
• [ ] Compare temperature (min, max) with daily average heating demand, timestep
• [x] Total electricity demand by LAD, sector, timestep

Transport - Road

• [x] Hourly Total Electricity Consumption of EVs by timestep
• [ ] Total Annual Fuel consumption by vehicle type, timestep

Transport - Rail

• [ ] Station use

Digital Comms

• [x] Coverage by technology, LAD, timestep

Water

• [ ] Water consumption by Water Resource Zone, timestep
• [ ] Restriction amounts

[willu47]

Read scenario data (workaround for smif#370):

import pandas as pd
from smif.data_layer import Results

results = Results({'interface': 'local_csv', 'dir': '.'})

store = results._store

model_run_name = 'energy_sd_optimised'

model_run = store.read_model_run(model_run_name)

scenario = 'gva'
variant = dict(model_run['scenarios'])[scenario]

output = 'gva_per_head'
dimensions = [x['dims'] for x in store.read_scenario(scenario, skip_coords=True)['provides'] if x['name']==output][0]

df = None
for step in model_run['timesteps']:
    if df is not None:
        data = store.read_scenario_variant_data(scenario, variant, output, step).as_df()
        df[step] = data
    else:
        df = store.read_scenario_variant_data(scenario, variant, output, step).as_df()
        df = df.rename(columns={output: step})
df = df.reset_index().melt(id_vars=dimensions)
df = df.rename(columns={'variable': 'timestep', 'value': output})

[willu47]

Example of plotting spatial data using dimensions access via the store:

import geopandas as gpd

lads = store.read_dimension('lad_uk_2016')['elements']
lad_features = [x['feature'] for x in lads]

lad_df = gpd.GeoDataFrame.from_features(lad_features)

b = lad_df.merge(df, left_on='lad16cd', right_on='lad_uk_2016')

b.plot(column='population')

[fcooper8472]

Question: for plots like: Total heating demand by LAD, timestep, what do you actually want out?

heating demand on Y against timestep on X, and an individual graph per LAD? Or lots of lines, one for each LAD, on a single set of axes?

[willu47]

Hi @fcooper8472 - Suggest heating demand on Y against timestep on X, with an individual graph per LAD, or mean, min, max graphs over all LADs in three charts.