{'fraction_non_alphanumeric': 0.014598540145985401, 'fraction_numerical': 0.0, 'mean_word_length': 4.835106382978723, '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}}
Application of soft computing in simulation of solar power tracking

Since the first decade of this century, the focus of research institutes has shifted to developing efficient renewable energy sources. Solar radiation is one of the primary sources of energy on this planet. For this reason photovoltaic (PV) system remains a popular subject of study. Due to its non-linear I-V and P-V characteristics, the PV Generator output varies with both solar radiation and temperature. In order to compensate for the low conversion efficiency, various MPPT algorithms are developed that provide automated control over power interfaces to generate optimal power efficiency. It is used to adjust the panels in order to promote solar operating voltage close to MPP under changing day to day weather conditions. There are libraries that simulate the results of such MPPT algorithms in MATLAB and Python. This paper aims to summarize the use of Python as a tool for forecasting and simulation method for solar energy resource and possible work that can be done to increase the efficiency of the same using ML.