Photovoltaic energy, which uses solar panels to turn sunlight into electricity, is an important part of the shift to renewable energy. Deep learning-based prediction is critical for optimizing output, anticipating weather fluctuations, and improving solar system efficiency, allowing for more intelligent energy network management.
There are numerous techniques for predicting PV power generation. Traditional approaches such as linear regressions, decision trees, and Gaussian processes are among the most popular, providing quick results but low accuracy. More sophisticated machine learning approaches, such as artificial neural networks (ANNs), may detect complex relationships in data. Furthermore, deep learning techniques like convolutional networks (CNNs) and long short-term memory (LSTM) models are commonly employed due to their ability to analyze temporal and meteorological data. However, these models are sensitive to adversarial assaults, posing a security risk for accurate forecasts in smart grids.
In a groundbreaking development, a US-Norwegian research team has recently published a new paper that could revolutionize photovoltaic energy forecasting. Their work, which is based on ANN and incorporates an analysis of adversarial machine learning attacks, has the potential to improve the accuracy of solar energy predictions significantly. By adapting the classical FGSM attack to regression models (R-FGSM), this study demonstrates how subtle perturbations in the data can deceive ANN solar forecasting models with improved efficiency. This research advances energy forecasting and highlights the security challenges in smart grids, paving the way for more robust and secure forecasting models in the future.
Concretely, the method proposed by the research team is structured around four main steps. First, they collect and process solar data from public sources. This processing includes the elimination of missing values, the detection of anomalies, and the normalization of the data to ensure better forecast quality. Second, they introduce adversarial noise using the FGSM method. This noise, subtly added to the data, aims to mislead the forecasting model by slightly modifying the inputs. Third, they use an ANN model to predict solar energy production. This model is trained and tested on the processed data, with and without adversarial noise, to evaluate its performance under normal and disturbed conditions. Finally, the model performance is measured using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) to assess the impact of adversarial attacks on prediction accuracy.
An experimental study was presented in the paper, in which a solar PV power generation forecasting model is built from an open-source dataset from the 2014 Global Energy Forecasting Competition (GEFCOM). This dataset includes hourly solar photovoltaic (PV) power generation data and associated weather forecasts covering the period from April 2012 to July 2014. It contains 12 meteorological variables from the European Centre for Medium-Range Weather Forecasts (ECMWF), such as total cloud cover, 2-meter temperature, surface pressure, relative humidity, and 10-meter wind component. The architecture uses a grid search for optimization, and the ANN model has four layers. The activation functions used are ELU (Exponential Linear Unit) for the hidden layers and Softplus for the last layer. A dropout rate of 0.2 is applied to avoid overfitting. The optimizer used is Adam, known for its fast convergence and robustness in updating weights.
The study results show that the ANN model performs well in predicting photovoltaic power generation, achieving RMSE, MSE, and MAE metrics of 0.0874, 0.0076, and 0.0425 without attacks. However, under the FGSM attack with a perturbation factor of 0.5, the RMSE increases to about 0.793, highlighting the model’s vulnerability to adversarial perturbations. These results highlight the need to develop robustness strategies to ensure the reliability of solar energy forecasts under real-world conditions.
In conclusion, the research underscores the significant advancements in photovoltaic energy forecasting using artificial neural networks while exposing their vulnerability to adversarial attacks. This study highlights the critical need for robust forecasting models in smart grids by demonstrating the impact of adversarial perturbations on prediction accuracy. The findings enhance our understanding of the interplay between machine learning and energy prediction and pave the way for future developments that prioritize security alongside efficiency in renewable energy systems.
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