{'fraction_non_alphanumeric': 0.022435897435897436, 'fraction_numerical': 0.0, 'mean_word_length': 5.092682926829268, '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}}
Wind speed prediction based on FWA-LSTM

With the increasingly severe global energy crisis and environmental pollution, the development of renewable energy has become the key to protecting the environment, saving energy and reducing emissions. As a kind of clean and low-cost renewable energy, wind power has been paid more and more attention by more and more countries. However, the time-varying and intermittent wind speed often leads to instability of wind power and hinders the integration of wind power into the grid. Therefore, effectively predicting the wind spee of wind farms can effectively reduce the cost of wind power grid connection and improve the security and stability of the power grid. In this paper, we propose a wind speed prediction model LSTM-FWA based on long short-term memory (LSTM) network and fireworks algorithm (FWA). The experimental results show that the prediction performance based on LSTM-FWA model is the most obvious. The LSTM-FWA was compared with other prediction models, and the performance of the model under various optimization strategies was comprehensively analyzed. Experimental results show that the proposed spatio-temporal optimized LSTM-FWA model has the best performance for wind speed prediction.