Abstract
Nowadays, various advanced techniques, like big data and deep learning, have been used in energy management systems for improving energy efficiency. Various research has been done on solar power generation prediction using multiple machine learning approaches. However, it is not easy to make exact predictions due to the alternative nature of solar energy. Therefore, this work aims to accurately implement and compare the neural network models to predict solar power generation. The models in this study are implemented using Python, TensorFlow, and Keras. The performance of the models is evaluated using 500 KWp grid-connected plant data. This work forecasted a solar power generation for 72 h using a time series dataset. Approximately, three years of the dataset are collected from January 2019 to October 2021 with hourly resolution from Airport Depot, Delhi, India. This paper compared the performance of the recurrent neural network (RNN) and long short-term memory (LSTM) in the form of accuracy. The three-year energy generation dataset is split into train and test data. The train data uses three years of energy generation data from January 2019 to September 2021, while the test data are used for October 2021. As a result, the determinant of coefficient (R \(^{2}\)) is a statistical error matrix representing the predicted values’ accuracy. The results show that the LSTM model predicts solar power generation with high accuracy (R \(^{2}\) = 0.92).
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Neeraj, Gupta, P., Tomar, A. (2022). A Comparison of Hourly Solar Energy Generation Forecasting Using RNN and LSTM Network. In: Tomar, A., Malik, H., Kumar, P., Iqbal, A. (eds) Proceedings of 3rd International Conference on Machine Learning, Advances in Computing, Renewable Energy and Communication. Lecture Notes in Electrical Engineering, vol 915. Springer, Singapore. https://doi.org/10.1007/978-981-19-2828-4_20
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DOI: https://doi.org/10.1007/978-981-19-2828-4_20
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