Stochastically generated demand profiles for buildings in two case study districts, alongside the model definition for the district. Dataset can be used wholly for scenario optimisation of models within the Calliope optimisation framework (https://callio.pe)

Detailed description:

District energy system model configuration and data for two geographic contexts: Bangalore, India and Cambridge, UK. Models describe buildings or groups of buildings as nodes that are connected together by electricity and thermal (Bangalore: cooling, Cambridge: heat) networks. In each node there are hourly electricity and thermal demands, and building-level technologies available to meet that demand. One node describes a district heating/cooling centre, which can produce the thermal energy to distribute along the network. Each model is contained in its own directory, with timeseries data in compressed CSV files and configuration and static data in YAML files. Also included in each directory is a README providing information on installing a python environment with the correct package versions, a Jupyter notebook to build and run the model, and the license under which the information is shared. The models are configured for optimisation using the Calliope energy system modelling framework (https://callio.pe). There are 500 timeseries data files per model, each generated using the data-driven stochastic sampling method described in the associated publication. The underlying measured data to undertake this sampling is from an office building in Bangalore and a number of public university buildings in Cambridge. Units in the models are: kWh (energy), kW (power), m^2 (area), and INR (costs, Bangalore) / GBP (costs, Cambridge).