Predicting Solar Performance Ratio Based on Encoder-Decoder Neural Network Model

Yen, Chih-Feng; Hsieh, He-Yen; Su, Kuan-Wu; Leu, Jenq‐Shiou

doi:10.1109/icumt48472.2019.8970993

Cited by 3 publications

(1 citation statement)

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“…Accurate generation predictions make power grids more reliable amid fluctuations in demand and capacity, avoid power outages, prevent plant managers from penalties, and save costs [3]. More specifically, deep learning (DL) models have been applied to forecast PV power generation with encouraging results [4,5]. Although the use of these forecasting models contributes to more active, flexible, and intelligent smart grids [6], they may be vulnerable to adversarial examples.…”

Section: Introductionmentioning

confidence: 99%

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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