Predicting solar power generation is necessary to successfully integrate solar energy into the grid and reduce the impact of changing weather on the system. Predicting the solar power output can reduce costs and improve financial predictions for solar energy systems. It allows for better timing of maintenance, reducing downtime and improving the efficiency of solar systems. Due to its adaptable nature, Random Forest (RF) is appropriate for a range of solar energy input variables such as temperature, humidity, solar irradiance, and others, whether numerical or categorical. Artificial neural networks are successful when working with large datasets and maintain good performance as data volume increases. This study presents Artificial Neural Networks in combination with the Random Forest Algorithm. Customizing this approach can address specific challenges in predicting solar energy, like incorporating different types of input data (e.g., time-series data, weather conditions) and adjusting to varying prediction goals (e.g., short-term forecasts, long-term predictions). Combining RF and ANN techniques can potentially achieve significant improvements in solar energy prediction accuracy and reliability.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Performance Enhancement of Solar Energy Prediction using Machine Learning Algorithms


    Contributors:


    Publication date :

    2024-11-06


    Size :

    601345 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Machine Health Prediction Enhancement Using Machine Learning

    Kalamdani, Rajeev / Jalluri, Chandra / Hermiller, Stephen et al. | British Library Conference Proceedings | 2017


    Machine Health Prediction Enhancement Using Machine Learning

    Jalluri, Chandra / Kalamdani, Rajeev / Clifton, Robert et al. | SAE Technical Papers | 2017


    Heart Disease Prediction Using Machine Learning Algorithms

    Mammen, Rea / Pawar, Arti | Springer Verlag | 2023



    Airline Fare Prediction Using Machine Learning Algorithms

    Subramanian, R. Raja / Murali, Marisetty Sai / Deepak, B et al. | IEEE | 2022