This paper presents a machine learning-based approach for predicting solar power generation with high accuracy using a 99% AUC (Area Under the Curve) metric. The approach includes data collection, pre-processing, feature
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PLANT_ID and SOURCE_KEY is same for all records so can be dropped from both the datasets. Also, no. of inverters is 22 in df_gen. No null values are present in both datasets.
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In addition, RFR and LSTM demonstrate their capability to capture the intricate patterns and complex relationships inherent in solar power generation data. The developed machine learning models can aid solar PV investors in streamlining their processes and improving their planning for the production of solar energy.
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Solar Power Generation Analysis and Predictive Maintenance using Kaggle Dataset - nimishsoni/Solar-Power-Generation-Forecasting-and-Predictive-Maintenance
The project explores solar tracking prediction in IoT, which optimizes solar panel positioning using real-time data, historical weather patterns, and machine learning algorithms. By integrating IoT
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Energy demand is growing and by 2050 solar energy will account for 11% total electricity production. It has emerged as one of the most potential sources of alternative energy Even though the usage of solar energy in residential places has increased, yet they are regarded as unpredictable and irregular power sources because the generated power output depends on
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