Include checks to ensure data validity

[IlkaCu]

To ensure the validity of our resulting data, some data checks should be integrated in the workflow. Central resulting tables such as eTraGo tables should be part of the checks.
One useful example:

eGon-data/src/egon/data/datasets/heat_supply/__init__.py

Lines 110 to 125 in 75f05b4

	# If the deviation is > 1%, throw an error
	assert (
	df_check.deviation.abs().max() < 1
	), f"""Unexpected deviation between target value and distributed
	heat supply: {df_check}
	"""
	# Add gas boilers as conventional backup capacities
	backup = backup_gas_boilers("eGon2035")
	backup.to_postgis(
	targets["district_heating_supply"]["table"],
	schema=targets["district_heating_supply"]["schema"],
	con=db.engine(),
	if_exists="append",
	)

[nesnoj]

Thanks for taking care!
How can we integrate additional checks?

@nailend Could you add some points/scrips/links to other issues here for validating the HH el. demand that have been used during the development?

[nailend]

These are a few ideas. The test would not be very precise but more like: is the aggregation of the disaggregated data within a tollerance

• compare aggregated load at nuts3 to demand-regio
• compare aggregated refined hh distribution to census hh distribution
• households > buildings
• check amount of cells with hh data after imputation