Photovoltaic Generation Forecast: Model Training and Adversarial Attack Aspects

Santana, Everton José; Silva, Ricardo Petri; Zarpelão, Bruno Bogaz; Barbon, Sylvio

doi:10.1007/978-3-030-61380-8_43

Cited by 5 publications

(5 citation statements)

References 17 publications

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“…In other words, most researchers in adversarial ML are devoted to tackling classification focused on image processing tasks. As observed in a previous work [13], adversarial examples can also affect regression models, which are usually the core of PV generation forecasting. Among the related works, only Chen et al [32] addressed this possibility, but they did not propose a defense solution against these attacks.…”

Section: Defense Approachesmentioning

confidence: 77%

“…The malicious sample to be inputted to the target results from adding η and x. FGSM is computationally cheap since it only needs the gradient sign, which can be quickly obtained. Although it was designed to compute adversarial image perturbations, Santana et al [13] showed that FGSM is also effective at making adversarial examples to degrade the prediction performance of DL models in PV power generation forecasting.…”

Section: Adversarial Examples Generationmentioning

confidence: 99%

“…An adversarial sample might make the forecasting model predict a much higher or lower generation capacity than the correct one. As operators use these predictions to coordinate multiple power plants that operate together to meet the energy demand, a high prediction error will eventually lead to wrong decisions, which can cause large-scale failures [13].…”

Section: Introductionmentioning

confidence: 99%

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Detecting and Mitigating Adversarial Examples in Regression Tasks: A Photovoltaic Power Generation Forecasting Case Study

et al. 2021

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With data collected by Internet of Things sensors, deep learning (DL) models can forecast the generation capacity of photovoltaic (PV) power plants. This functionality is especially relevant for PV power operators and users as PV plants exhibit irregular behavior related to environmental conditions. However, DL models are vulnerable to adversarial examples, which may lead to increased predictive error and wrong operational decisions. This work proposes a new scheme to detect adversarial examples and mitigate their impact on DL forecasting models. This approach is based on one-class classifiers and features extracted from the data inputted to the forecasting models. Tests were performed using data collected from a real-world PV power plant along with adversarial samples generated by the Fast Gradient Sign Method under multiple attack patterns and magnitudes. One-class Support Vector Machine and Local Outlier Factor were evaluated as detectors of attacks to Long-Short Term Memory and Temporal Convolutional Network forecasting models. According to the results, the proposed scheme showed a high capability of detecting adversarial samples with an average F1-score close to 90%. Moreover, the detection and mitigation approach strongly reduced the prediction error increase caused by adversarial samples.

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Section: Defense Approachesmentioning

confidence: 77%

Section: Adversarial Examples Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detecting and Mitigating Adversarial Examples in Regression Tasks: A Photovoltaic Power Generation Forecasting Case Study

et al. 2021

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“…It also differs from classical approaches based on sliding windows and thresholds of statistical moments [3]. [1,4,16,32], [1,2,4,8,16,32], [1,3,6,12,24], [1,2,6,12,24], [1,2,4,8,16], [1,4,16], [1,2,4,8], [1,4,8] Blocks 1, 2 Dropout 0…”

Section: Osts Methodsmentioning

confidence: 99%

“…From time series analyses, it is possible to examine these patterns and create predictions of future samples, as discussed in Mahalakshmi et al [14]. Models based on machine learning, e.g., Long short-term memory (LSTM) and Temporal Convolutional Network (TCN), have shown promising results, [15,16], as an alternative to statistical models. Approaches that apply machine learning concepts can adapt their settings to improve predictive ability [17].…”

Section: Introductionmentioning

confidence: 99%

Time Series Segmentation Based on Stationarity Analysis to Improve New Samples Prediction

Silva

Zarpelão

Cano

et al. 2021

Sensors

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A wide range of applications based on sequential data, named time series, have become increasingly popular in recent years, mainly those based on the Internet of Things (IoT). Several different machine learning algorithms exploit the patterns extracted from sequential data to support multiple tasks. However, this data can suffer from unreliable readings that can lead to low accuracy models due to the low-quality training sets available. Detecting the change point between high representative segments is an important ally to find and thread biased subsequences. By constructing a framework based on the Augmented Dickey-Fuller (ADF) test for data stationarity, two proposals to automatically segment subsequences in a time series were developed. The former proposal, called Change Detector segmentation, relies on change detection methods of data stream mining. The latter, called ADF-based segmentation, is constructed on a new change detector derived from the ADF test only. Experiments over real-file IoT databases and benchmarks showed the improvement provided by our proposals for prediction tasks with traditional Autoregressive integrated moving average (ARIMA) and Deep Learning (Long short-term memory and Temporal Convolutional Networks) methods. Results obtained by the Long short-term memory predictive model reduced the relative prediction error from 1 to 0.67, compared to time series without segmentation.

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Analysis of deceptive data attacks with adversarial machine learning for solar photovoltaic power generation forecasting

et al. 2022

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Photovoltaic Generation Forecast: Model Training and Adversarial Attack Aspects

Cited by 5 publications

References 17 publications

Detecting and Mitigating Adversarial Examples in Regression Tasks: A Photovoltaic Power Generation Forecasting Case Study

Detecting and Mitigating Adversarial Examples in Regression Tasks: A Photovoltaic Power Generation Forecasting Case Study

Time Series Segmentation Based on Stationarity Analysis to Improve New Samples Prediction

Analysis of deceptive data attacks with adversarial machine learning for solar photovoltaic power generation forecasting

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