{'fraction_non_alphanumeric': 0.012482662968099861, 'fraction_numerical': 0.004160887656033287, 'mean_word_length': 5.11864406779661, '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}}
Effects of outliers on calculation of load profile factors

The paper presents the results of a new statistical based data mining approach that explores the load curves in order to detect abnormal data, the so-called outliers. The load curves characterized by their main indicators are provided from Smart Meters mounted on the power substation. The proposed method was tested using a real database with 290 substations from rural area. By outlier's elimination, the results demonstrate the ability of the proposed approach to be efficiently used by distribution operators in accurate patterns discovery of the load curves characteristics with the help of which the operation and planning of power systems, could be made.