Aiming at the problem of violent fluctuations caused by sudden abnormalities in power load forecasting due to extreme weather, equipment failure and other reasons, this paper introduces the Informer model to model sudden abnormal behaviors and uses Generative Adversarial Network (GAN) for intelligent error prevention. The GAN discriminator identifies and marks abnormal points, and the generator repairs abnormal data. Comparative experiments are conducted to verify the sudden anomaly recognition and repair capabilities of the intelligent error prevention mechanism in this paper. The ROC curve results show that the AUC value of the anomaly recognition designed in this paper is 0.95, significantly higher than 0.48 of the baseline model and 0.87 of the GAN model. In most cases, the load of the power system studied in this paper can respond in a shorter time, providing an efficient and intelligent solution for the power system to deal with emergencies. This enhances power load forecasting accuracy and system stability, offering new perspectives for intelligent error prevention and anomaly management in the power industry.
DongXDengSWangD. A short-term power load forecasting method based on k-means and SVM. J Ambient Intell Humaniz Comput2022; 13: 5253–5267.
2.
VeeramsettyVReddyKRSanthoshM, et al.Short-term electric power load forecasting using random forest and gated recurrent unit. Electr Eng2022; 104: 307–329.
3.
ZhangZKangC. Challenges and prospects for constructing the new-type power system towards a carbon neutrality future. Proc CSEE2022; 42: 2806–2818.
4.
KangCDuEGuoH, et al.Primary exploration of six essential factors in new power system. Power Syst Technol2023; 47: 1741–1750.
5.
JiaoJTangZZhangP, et al.Cyberattack-resilient load forecasting with adaptive robust regression. Int J Forecast2022; 38: 910–919.
6.
El-HadadRTanYFTanWN. Anomaly prediction in electricity consumption using a combination of machine learning techniques. Int J Technol2022; 13: 1317–1325.
7.
SonghuaH. A novel outlier calendrical heterogeneity reconstruction deep learning model for electricity demand forecasting. J Intell Fuzzy Syst2024; 46: 3363–3394.
8.
JebliIBelouadhaFZKabbajMI,et al.Prediction of solar energy guided by Pearson correlation using machine learning. Energy2021; 224: 120109.
9.
SarmasESpiliotisEStamatopoulosE, et al.Short-term photovoltaic power forecasting using meta-learning and numerical weather prediction independent long short-term memory models. Renew Energy2023; 216: 118997.
10.
PanagouliasDPSarmasEMarinakisV, et al.Integrating dynamic correlation shifts and weighted benchmarking in extreme value analysis. arXiv preprint arXiv:2411,13608. 2024.
11.
MestavKRWangXTongL. A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Trans Power Syst2022; 38: 4–13.
12.
MahmoudnezhadFMoradzadehAMohammadi-IvatlooB, et al.Electric load forecasting under false data injection attacks via denoising deep learning and generative adversarial networks. IET Gener Transm Distrib2024; 18: 3247–3263.
13.
FuJWangLKeJ, et al.GANAD: a GAN-based method for network anomaly detection. World Wide Web2023; 26: 2727–2748.
14.
HajirahimiZKhasheiM. Hybridization of hybrid structures for time series forecasting: a review. Artif Intell Rev2023; 56: 1201–1261.
15.
AhnHSunKKimKP. Comparison of missing data imputation methods in time series forecasting. Comput Mater Contin2022; 70: 767–779.
16.
HuangXChenNDengZ,et al.Multivariate time series anomaly detection via dynamic graph attention network and informer. Appl Intell2024; 54: 7636–7658.
17.
DuanCKeW. Advanced stock price prediction using LSTM and informer models. J Artif Intell Gen Sci (JAIGS) ISSN: 3006-40232024; 5: 141–166.
18.
HanKWangYChenH, et al.A survey on vision transformer. IEEE Trans Pattern Anal Mach Intell2022; 45: 87–110.
19.
KhanARaufZSohailA, et al.A survey of the vision transformers and their CNN-transformer based variants. Artif Intell Rev2023; 56: 2917–2970.
20.
YangHWangLXuY,et al.Covidvit: a novel neural network with self-attention mechanism to detect COVID-19 through X-ray images. Int J Mach Learn Cybern2023; 14: 973–987.
21.
KumarSSolankiA. An abstractive text summarization technique using transformer model with self-attention mechanism. Neural Comput Appl2023; 35: 18603–18622.
22.
WuJWangXGaoX, et al.On the effectiveness of sampled softmax loss for item recommendation. ACM Trans Inf Syst2024; 42: 1–26.
23.
DasPKSahooBMeherS. An efficient detection and classification of acute leukemia using transfer learning and orthogonal softmax layer-based model. IEEE/ACM Trans Comput Biol Bioinf2022; 20: 1817–1828.
24.
KozakJPZhangRPorterM, et al.Stability, reliability, and robustness of GaN power devices: a review. IEEE Trans Power Electron2023; 38: 8442–8471.
25.
BuffoloMFaveroDMarcuzziA, et al.Review and outlook on GaN and SiC power devices: industrial state-of-the-art, applications, and perspectives. IEEE Trans Electron Devices2024; 71: 1344–1355.
26.
LiuWYangTShiY, et al.Combined interrogation algorithm of phase function and minimum mean square error for fiber-optic Fabry-Perot micro-pressure sensors based on white light interference. J Lightwave Technol2023; 41: 3241–3248.
27.
SinitsynINSinitsynVIKorepanovÈRF,et al.Neural network synthesis of an optimal linear stochastic system according to the criterion of minimum mean square error. Sist Sredstva Inform [Syst Means Inform]2024; 34: 87–108.
28.
XingYSongQChengG. Benefit of interpolation in nearest neighbor algorithms. SIAM J Math Data Sci2022; 4: 935–956.
29.
FattoriniLMarcheselliMPisaniC,et al.Design-based properties of the nearest neighbor spatial interpolator and its bootstrap mean squared error estimator. Biometrics2022; 78: 1454–1463.
30.
GlagolevskijYV. Problems with Root-Mean-Square values of the magnetic field of Ap Stars. I. Astrophysics2022; 65: 203–217.
31.
FawwazrinAMArbansyahARezaeipanahA. ID Card printing time prediction with mean absolute error in linear regression. JSE J Sci Eng2025; 3: 80–84.
32.
MumaBKarokiA. Modeling GDP using autoregressive integrated moving average (ARIMA) model: a systematic review. Open Access Lib J2022; 9: 1–8.
33.
ArunKumarKEKalagaDVKumarCMS, et al.Comparative analysis of Gated Recurrent Units (GRU), long short-term memory (LSTM) cells, autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA) for forecasting COVID-19 trends. Alexandria Eng J2022; 61: 7585–7603.
34.
LiuYLiDWanS, et al.A long short-term memory-based model for greenhouse climate prediction. Int J Intell Syst2022; 37: 135–151.
35.
KratzertFGauchMKlotzD,et al.HESS Opinions: never train a long short-term memory (LSTM) network on a single basin. Hydrol Earth Syst Sci2024; 28: 4187–4201.
36.
DashRKNguyenTNCengizK,et al.Fine-tuned support vector regression model for stock predictions. Neural Comput Appl2023; 35: 23295–23309.
37.
MaJXiaDGuoH, et al.Metaheuristic-based support vector regression for landslide displacement prediction: a comparative study. Landslides2022; 19: 2489–2511.
38.
NiQJiJCFengK. Data-driven prognostic scheme for bearings based on a novel health indicator and gated recurrent unit network. IEEE Trans Ind Inf2022; 19: 1301–1311.
39.
ZhangWLiHTangL, et al.Displacement prediction of Jiuxianping landslide using gated recurrent unit (GRU) networks. Acta Geotech2022; 17: 1367–1382.
