{'fraction_non_alphanumeric': 0.01696969696969697, 'fraction_numerical': 0.0, 'mean_word_length': 5.3538461538461535, '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}}
Short-term load forecasting using artificial immune network

The precision of short-term load forecasting for electric power systems directly affects the economic benefit of power systems. However, since the relationship between load power and factors influencing load power is nonlinear, it is difficult to identify its nonlinearity by using conventional methods. A nonlinear short-term load forecasting model based on artificial immune network is presented in this paper. The design and learning of neural network system uses immune network regulation and immune programming, and artificial immune network is defined. This paper presents the method in power system short-term load forecasting, and it is compared with neural network method. The result indicates that this model is effective and has adaptive ability itself.