LSTM-based Load Forecasting Robustness Against Noise Injection Attack in Microgrid
In this paper, we investigate the robustness of an LSTM neural network against noise injection attacks for electric load forecasting in an ideal microgrid. The performance of the LSTM model is investigated under a black-box Gaussian noise attack with different SNRs. It is assumed that attackers have just access to the input data of the LSTM model. The results show that the noise attack affects the performance of the LSTM model. The load prediction means absolute error (MAE) is 0.047 MW for a healthy prediction, while this value increases up to 0.097 MW for a Gaussian noise insertion with SNR= 6 dB. To robustify the LSTM model against noise attack, a low-pass filter with optimal cut-off frequency is applied at the model's input to remove the noise attack. The filter performs better in case of noise with lower SNR and is less promising for small noises.
Code (0)
등록된 구현이 없습니다.
Tasks
Load ForecastingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Electrical Load Forecasting Model Using Hybrid LSTM Neural Networks with Online Correction
Accurate electrical load forecasting is of great importance for the efficient operation and control of modern power systems. In this work, a hybrid long short-term memory (LSTM)-based model with online correction is deve…
Load ForecastingTime SeriesMulti-horizon short-term load forecasting using hybrid of LSTM and modified split convolution
Precise short-term load forecasting (STLF) plays a crucial role in the smooth operation of power systems, future capacity planning, unit commitment, and demand response. However, due to its non-stationary and its dep…
Data AblationLoad ForecastingMissing ElementsMultivariate Time Series Forecasting+3From ARIMA to Attention: Power Load Forecasting Using Temporal Deep Learning
Accurate short-term power load forecasting is important to effectively manage, optimize, and ensure the robustness of modern power systems. This paper performs an empirical evaluation of a traditional statistical model a…
Q-DPTS: Quantum Differentially Private Time Series Forecasting via Variational Quantum Circuits
Time series forecasting is vital in domains where data sensitivity is paramount, such as finance and energy systems. While Differential Privacy (DP) provides theoretical guarantees to protect individual data contribution…
Time Series ForecastingA Hybrid PCA-PR-Seq2Seq-Adam-LSTM Framework for Time-Series Power Outage Prediction
Accurately forecasting power outages is a complex task influenced by diverse factors such as weather conditions [1], vegetation, wildlife, and load fluctuations. These factors introduce substantial variability and noise …