A novel gated dual convolutional neural network model with autoregressive method and attention mechanism for probabilistic load forecasting

Alvarez V, Mazuelas S, Lozano JA (2021) Probabilistic load forecasting based on adaptive online learning. IEEE Trans Power Syst 36(4):3668–3680 Xu L, Wang SW, Tang R (2019) Probabilistic load forecasting for buildings considering weather forecasting uncertainty and uncertain peak load. Appl Energy 237:180–195 Sheng ZY, Wang HW, Chen G, Zhou B, Sun J (2021) Convolutional residual network to short-term load forecasting. Appl Intell 51(4):2485–2499 Barman M, Choudhury NBD (2019) Season specific approach for short-term load forecasting based on hybrid FA-SVM and similarity concept. Energy 174:886–896 Liu H, Fang S, Zhang ZL, Li DTC, Lin K, Wang JZ (2022) MFDNet: collaborative poses perception and matrix fisher distribution for head pose estimation. IEEE Trans Multimed 24:2449–2460 Liu TT, Wang JX, Yang B, Wang X (2021) NGDNet: nonuniform Gaussian-label distribution learning for infrared head pose estimation and on-task behavior understanding in the classroom. Neurocomputing 436:210–220 Imani M (2021) Electrical load-temperature CNN for residential load forecasting. Energy 227:120480 Jalali SMJ, Ahmadian S, Khosravi A, Shafie-khah M, Nahavandi S, Catalão JP (2021) A novel evolutionary-based deep convolutional neural network model for intelligent load forecasting. IEEE Trans Ind Inform 17(12):8243–8253 Huang Q, Li JH, Zhu MS (2020) An improved convolutional neural network with load range discretization for probabilistic load forecasting. Energy 203:117902 Zhang WJ, Quan H, Srinivasan D (2018) Parallel and reliable probabilistic load forecasting via quantile regression forest and quantile determination. Energy 160:810–819 Wen HL, Gu J, Ma JH, Yuan L, Jin ZJ (2021) Probabilistic load forecasting via neural basis expansion model based prediction intervals. IEEE Trans Smart Grid 12(4):3648–3660 Brusaferri A, Matteucci M, Spinelli S, Vitali A (2022) Probabilistic electric load forecasting through Bayesian mixture density networks. Appl Energy 309:118341 Wan C, Cao Z, Lee WJ, Song Y, Ju P (2021) An adaptive ensemble data driven approach for nonparametric probabilistic forecasting of electricity load. IEEE Trans Smart Grid 12(6):5396–5408 Jeong D, Park C, Ko YM (2021) Short-term electric load forecasting for buildings using logistic mixture vector autoregressive model with curve registration. Appl Energy 282:116249 Wang Y, Gan DH, Sun MY, Zhang N, Lu ZX, Kang CQ (2019) Probabilistic individual load forecasting using pinball loss guided LSTM. Appl Energy 235:10–20 Arora P, Khosravi A, Panigrahi BK, Suganthan PN (2022) Remodelling state-space prediction with deep neural networks for probabilistic load forecasting. IEEE Trans Emerg Topics Comput Intell 6(3):628–637 Lai GK, Chang WC, Yang YM, Liu HX (2018) Modeling long-and short-term temporal patterns with deep neural networks. In the 41st international ACM SIGIR conference on Research & Development in information retrieval, July 8-12, MI, USA, pp 95–104 Zhang WJ, Quan H, Srinivasan D (2018) An improved quantile regression neural network for probabilistic load forecasting. IEEE Trans Smart Grid 10(4):4425–4434 Yang YD, Hong WJ, Li SF (2019) Deep ensemble learning based probabilistic load forecasting in smart grids. Energy 189:116324 Meinshausen N, Ridgeway G (2006) Quantile regression forests. J Mach Learn Res 7(6):983–999 He YY, Qin Y, Wang S, Wang X, Wang C (2019) Electricity consumption probability density forecasting method based on LASSO-quantile regression neural network. Appl Energy 233:565–575 Zang HX, Xu RQ, Cheng LL, Ding T, Liu L, Wei ZN, Sun GQ (2021) Residential load forecasting based on LSTM fusing self-attention mechanism with pooling. Energy 229:120682 Gan DH, Wang Y, Yang S, Kang CQ (2018) Embedding based quantile regression neural network for probabilistic load forecasting. J Mod Power Syst Clean Energy 6(2):244–254 Xie J, Hong T (2018) Temperature scenario generation for probabilistic load forecasting. IEEE Trans Smart Grid 9(3):1680–1687 Lou YW, Huang Y, Xing XL, Cao YZ, Wang HP (2022) MTS-LSTDM: multi-time-scale long short-term double memory for power load forecasting. J Syst Archit 125:102443 Liu H, Liu T, Chen Y, Zhang Z, Li YF (2022) EHPE: skeleton cues-based gaussian coordinate encoding for efficient human pose estimation. IEEE Trans Multimed:1–12. https://doi.org/10.1109/TMM.2022.3197364 Liu H, Nie HW, Zhang ZL, Li YF (2021) Anisotropic angle distribution learning for head pose estimation and attention understanding in human-computer interaction. Neurocomputing 433:310–322 Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780 Deng JF, Cheng LL, Wang ZW (2021) Attention-based BiLSTM fused CNN with gating mechanism model for Chinese long text classification. Comput Speech Lang 68:101182 Peng YQ, Xiao TF, Yuan HT (2022) Cooperative gating network based on a single BERT encoder for aspect term sentiment analysis. Appl Intell 52(5):5867–5879 Wu ZH, Pan SR, Long GD, Jiang J, Zhang CQ (2019) Graph wavenet for deep spatial-temporal graph modeling. In the 28th International Joint Conference on Artificial Intelligence, august 10-16, Macao, China, pp 1907–1913 Ienco D, Interdonato R (2023) Deep semi-supervised clustering for multi-variate time-series. Neurocomputing 516:36–47 Afrasiabi M, Mohammadi M, Rastegar M, Stankovic L, Afrasiabi S, Khazaei M (2020) Deep-based conditional probability density function forecasting of residential loads. IEEE Trans Smart Grid 11(4):3646–3657 Liu RH, Chen T, Sun GP, Muyeen SM, Lin SF, Mi Y (2022) Short-term probabilistic building load forecasting based on feature integrated artificial intelligent approach. Electr Power Syst Res 206:107802 Liu H, Zheng C, Li DTCL, Shen XX, Lin K, Wang JZ et al (2021) EDMF: efficient deep matrix factorization with review feature learning for industrial recommender system. IEEE Trans Ind Inform 18(7):4361–4371 Li ZF, Liu H, Zhang ZL, Liu TT, Xiong NN (2022) Learning knowledge graph embedding with heterogeneous relation attention networks. IEEE Trans Neural Netw Learn Syst 33(8):3961–3973 Liu JS, Kang Y, Li H, Wang HN, Yang XK (2022) STGHTN: spatial-temporal gated hybrid transformer network for traffic flow forecasting. Appl Intell. https://doi.org/10.1007/s10489-022-04122-x Liu G, Guo JB (2019) Bidirectional LSTM with attention mechanism and convolutional layer for text classification. Neurocomputing 337:325–338 Niu ZY, Zhong GQ, Yu H (2021) A review on the attention mechanism of deep learning. Neurocomputing 452:48–62 Wang SX, Wang X, Wang SM, Wang D (2019) Bi-directional long short-term memory method based on attention mechanism and rolling update for short-term load forecasting. Int J Electr Power Energy Syst 109:470–479 Huang Y, Huang Z, Yu JH, Dai XH, Li YY (2022) Short-term load forecasting based on IPSO-DBiLSTM network with variational mode decomposition and attention mechanism. Appl Intell. https://doi.org/10.1007/s10489-022-04174-z Lin J, Ma J, Zhu JG, Cui Y (2022) Short-term load forecasting based on LSTM networks considering attention mechanism. Int J Electr Power Energy Syst 137:107818 Zhang H, Yan J, Liu YQ, Gao YQ, Han S, Li L (2021) Multi-source and temporal attention network for probabilistic wind power prediction. IEEE Trans Sustain Energy 12(4):2205–2218 Huang ST, Wang DL, Wu XH, Tang A (2019) Dsanet: dual self-attention network for multivariate time series forecasting. In the 28th ACM international conference on information and knowledge management, November 3–7, Beijing, China, pp 2129–2132 Zhang WY, Chen Q, Yan JY, Zhang S, Xu JY (2021) A novel asynchronous deep reinforcement learning model with adaptive early forecasting method and reward incentive mechanism for short-term load forecasting. Energy 236:121492 Rafi SH, Deeba SR, Hossain E (2021) A short-term load forecasting method using integrated CNN and LSTM network. IEEE Access 9:32436–32448