A new study explores the application of LSTM (Long Short-Term Memory) neural networks and the Nadam optimiser to predict solar photovoltaic (PV) power output. This method aims to address the challenges of integrating solar PV systems into the energy grid, which is crucial due to the variability and intermittency of solar power.
Highlights
- LSTM Neural Networks:
- LSTM networks are a type of recurrent neural network (RNN) designed to capture temporal dependencies in time series data.
- The architecture includes memory cells, input gates, output gates, and forget gates, enabling the network to retain relevant information and discard irrelevant data, thereby improving prediction accuracy.
- The study utilised MATLAB’s deep learning tools for creating and training LSTM networks, which allow customisation of hidden layers, LSTM units, and layer activation functions.
- Nadam Optimiser:
- The Nadam optimiser is a variant of the Adam optimiser that combines the benefits of Nesterov-accelerated gradient and Adam optimisers.
- It adjusts the weights and biases of the LSTM model during training based on the gradient of the loss function, leading to improved performance.
- The study compared Nadam with other optimisers like SGD, RMSprop, and Adagrad, finding Nadam to be superior in terms of prediction accuracy and training efficiency.
- Methodology and Results:
- The research involved preprocessing historical solar power output data and various meteorological factors, followed by dividing the dataset into training and testing sets.
- The LSTM model’s predictions were compared with traditional statistical models like ARIMA and SARIMA.
- Performance metrics such as root mean square error (RMSE) demonstrated that the LSTM-Nadam model outperformed these traditional models, making it more suitable for large-scale solar power forecasting.
- Implications for Solar Power Systems:
- The findings suggest that combining LSTM neural networks with the Nadam optimiser can significantly enhance the accuracy and reliability of solar power output predictions.
- This approach can optimise the operation, design, and integration of solar power systems, ultimately increasing their dependability and profitability.
The study emphasises the importance of advanced machine learning techniques in improving solar power forecasting. The LSTM-Nadam model provides a robust solution for long-term predictions, addressing the limitations of traditional methods and enhancing the management of renewable energy resources.
Source
Machine Learning Techniques for Solar Power Output Predicting, International Journal of Smart Grid, Vol 8, No 2 (2024) (June)
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