{'fraction_non_alphanumeric': 0.009198423127463863, 'fraction_numerical': 0.0, 'mean_word_length': 5.684210526315789, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Generation of Dynamic Energetic District Models from Statistical Relationships

This contribution describes a new method for creating populations of generic geometric and energetic building models for energetic district simulation, based on very common information such as the population density, the mean building age, the mean energy efficiency of the buildings and the local climate conditions. These automatically generated district models allow to calculate typical heating and cooling demand cycles for simulation experiments including their spatial distribution. The new approach is demonstrated by two different district types: a street village with a very low population density and four building blocks as a part of a high dense city centre in Berlin.