{'fraction_non_alphanumeric': 0.02193419740777667, 'fraction_numerical': 0.0, 'mean_word_length': 5.121951219512195, '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}}
Research on Short-term Load Forecasting of Power System Based on Wavelet Denoising and Artificial Neural Network

Power system short-term load forecasting plays an important role in the reliable, safe and economic operation of power system. Power system load forecasting data is an important basis for power grid planning, scheduling, marketing and other departments. In order to fully mine the effective information in the load data of power system and carry out accurate short-term load forecasting, this paper proposes a Long Short-Term Memory (LSTM) model based on wavelet denoising to build a short-term load forecasting model. Wavelet denoising method is used for data preprocessing, so as to ensure the accuracy of the prediction model, while LSTM is used to achieve high-quality short-term load forecasting of the power system. The method proposed in this paper has the advantages of strong training and learning ability, fast convergence speed, high prediction accuracy and strong adaptability.