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PREDICTING SOLAR POWER GENERATION USING SEMI-SUPERVISED LEARNING
PREDICTING SOLAR POWER GENERATION USING SEMI-SUPERVISED LEARNING
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机译:通过半监督学习预测太阳能发电
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摘要
A method for predicting solar power generation receives historical power profile data and historical weather micro-forecast data at a given location for a set of days. Based on power output features for the days, clusters are generated. A classification model that assigns a day to a generated cluster according to weather features is created. For each cluster, a regression model that takes as input weather features and outputs predicted solar power is built. A system includes a sensor for collecting meteorological data at a solar farm, a meter for measuring photovoltaic power output of the solar farm, and a computer processor for executing instructions to predict solar power generation at the solar farm according to the method disclosed, based on data from the sensor and the meter, for a predefined time period. Further instructions predict solar power generation at the solar farm based on a micro-forecast for the solar farm.
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