[1] Batra N, Singh A, Singh P et al. Data driven energy efficiency in buildings[EB/OL]. https://arxiv.org/abs/1404.7227

[2] Ibrahim M, El-Zaart A, Adams C. Smart sustainable cities roadmap: readiness for transformation towards urban sustainability[J]. Sustainable Cities and Society, 37, 530-540(2018).

[3] Zheng Z, Chen H N, Luo X W. A supervised event-based non-intrusive load monitoring for non-linear appliances[J]. Sustainability, 10, 1001(2018).

[4] Lam H Y, Fung G S K, Lee W K. A novel method to construct taxonomy electrical appliances based on load signaturesof[J]. IEEE Transactions on Consumer Electronics, 53, 653-660(2007).

[5] de Baets L, Develder C, Dhaene T et al. Automated classification of appliances using elliptical Fourier descriptors[C], 153-158(2017).

[6] Hassan T, Javed F, Arshad N. An empirical investigation of V-I trajectory based load signatures for non-intrusive load monitoring[J]. IEEE Transactions on Smart Grid, 5, 870-878(2014).

[7] Cheng X, Li L Z, Wu H et al. A survey of the research on non-intrusive load monitoring and disaggregation[J]. Power System Technology, 40, 3108-3117(2016).

[8] Kelly J, Knottenbelt W. Neural NILM: deep neural networks applied to energy disaggregation[C], 55-64(2015).

[9] Wang S X, Guo L Y, Chen H W et al. Non-intrusive load identification algorithm based on feature fusion and deep learning[J]. Automation of Electric Power Systems, 44, 103-110(2020).

[10] Zhang Y T, Deng C Y, Liu Y K et al. Non-intrusive load identification algorithm based on convolution neural network[J]. Power System Technology, 44, 2038-2044(2020).

[11] Breuel T M. The effects of hyperparameters on SGD training of neural networks[EB/OL]. https://arxiv.org/abs/1508.02788

[12] Du L, He D W, Harley R G et al. Electric load classification by binary voltage-current trajectory mapping[J]. IEEE Transactions on Smart Grid, 7, 358-365(2016).

[13] Alpaydin E. Neural networks and deep learning[M]. Machine learning: the new AI, 85-109(2016).

[14] Gao J K, Kara E C, Giri S et al. A feasibility study of automated plug-load identification from high-frequency measurements[C], 220-224(2015).

[15] Zhang C Z, Li Y, Kang B L et al. Blurred license plate character recognition algorithm based on deep learning[J]. Laser & Optoelectronics Progress, 58, 1610012(2021).

[16] Song X Y, Jin L T, Zhao Y et al. Plant image recognition with complex background based on effective region screening[J]. Laser & Optoelectronics Progress, 57, 041016(2020).

[17] Liu F, Li M J, Hu J W et al. Expression recognition based on low pixel face images[J]. Laser & Optoelectronics Progress, 57, 101008(2020).

[18] Russakovsky O, Deng J, Su H et al. ImageNet large scale visual recognition challenge[J]. International Journal of Computer Vision, 115, 211-252(2015).

[19] Dumoulin V, Visin F. A Guide to convolution arithmetic for deep learning[EB/OL]. https://arxiv.org/abs/1603.07285

[20] Li C M, Yang S X, Yang Y et al. Hyperspectral remote sensing image classification based on maximum overlap pooling convolutional neural network[J]. Sensors, 18, 3587(2018).

[21] Kennedy J, Eberhart R. Particle swarm optimization[C], 1942-1948(2011).

[22] Gao J K, Giri S, Kara E C et al. PLAID: a public dataset of high-resoultion electrical appliance measurements for load identification research: demo abstract[C], 198-199(2014).

[23] Kahl M, Haq A U, Kriechbaumer T. Whited-a worldwide household and industry transient energy data set[EB/OL]. http://nilmworkshop.org/2016/proceedings/Poster_ID18.pdf