Renewable Energy Forecasting: A Comparative Study of Machine Learning and Deep Learning Architectures
Keywords:
prediction and forecasting machine learning approach, deep learning, renewable energy, solar and wind power energy, architectures, etc.Abstract
Renewable energy forecasting is crucial for efficient integration of renewable energy sources into the power grid. This study compares the performance of machine learning (ML) and deep learning (DL) architectures in forecasting renewable energy production. We evaluate the accuracy of various ML algorithms (ARIMA, SVM, Random Forest) and DL models (LSTM, CNN, GRU) using historical weather and energy production data. Results show that DL architectures outperform ML algorithms in forecasting accuracy, with LSTM achieving the best performance. This study presents a comparative analysis of various machine learning (ML) and deep learning (DL) architectures for forecasting renewable energy sources, specifically focusing on solar and wind power.
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