Smart and intelligent energy monitoring systems: A comprehensive literature survey and future research guidelines
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Han T, 2020, An Efficient Deep Learning Framework for Intelligent Energy Management in IoT Networks, IEEE Internet of Things Journal, 1, 10.1109/JIOT.2020.3013306
Daut MAM, 2017, An improved building load forecasting method using a combined Least Square support vector machine and modified artificial bee Colony, ELEKTRIKA‐Journal of Electrical Engineering, 16, 1
A. B. Georges Hebrail“Individual household electric power consumption Data Set"https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption 2012.
A.Trindade “ElectricityLoadDiagrams20112014 Data Set ” https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014 2015.
Zeng M, 2013, Short‐term load forecasting of smart grid systems by combination of general regression neural network and least squares‐support vector machine algorithm optimized by harmony search algorithm method, Appl Math, 7, 291
Ekici S, 2016, Electric load forecasting using regularized extreme learning machines, International Journal of Industrial Electronics and Electrical Engineering, 4, 119
Behera S., 2016, Predicting consumer loads for improved power scheduling in smart homes, Computational Intelligence in Data Mining, 2, 463
Papageorgiou E. I., 2015, Application of fuzzy cognitive maps to electricity consumption prediction, 2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) Held Jointly with 2015 5th World Conference on Soft Computing (WConSC), 1
Poczeta K., 2018, Application of fuzzy cognitive maps to multi‐step ahead prediction of electricity consumption, 2018 Conference on Electrotechnology: Processes, Models, Control and Computer Science (EPMCCS), 1
Dey N., 2017, Forecasting energy consumption from smart home sensor network by deep learning, International Conference on Smart Trends for Information Technology and Computer Communications, 255
Tabrizchi H, 2019, Estimates of residential building energy consumption using a multi‐verse optimizer‐based support vector machine with k‐fold cross‐validation, Evolving Systems, 1
Saikia P., 2018, Unsupervised pre‐training on improving the performance of neural network in regression, 2018 International Joint Conference on Neural Networks (IJCNN), 01
Tran‐Nguyen M.‐T., 2018, Decision tree using local support vector regression for large datasets, Asian Conference on Intelligent Information and Database Systems, 255
Rasel R. I., 2019, Predicting electric energy use of a low energy house: a machine learning approach, 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), 1
Zhang T., 2018, Electrical energy prediction with regression‐oriented models, The Euro‐China Conference on Intelligent Data Analysis and Applications, 146
Y Amri, 2016, Analysis clustering of electricity usage profile using K‐means algorithm, IOP Conference Series: Materials Science and Engineering, 105, 012020, 10.1088/1757-899X/105/1/012020
R Moslemi, 2017, A machine learning based demand charge management solution, 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT‐Europe), 1
JinX LiS ZhangY andYanX. “Multi‐step deep autoregressive forecasting with latent states.” 2019.http://roseyu.com/time‐series‐workshop/submissions/2019/timeseries‐ICML19_paper_19.pdf
Moslemi R., 2017, A data‐driven demand charge management solution for behind‐the‐meter storage applications, 2017 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 1
SenR YuH.‐F andDhillonI.“Think globally act locally: a deep neural network approach to high‐dimensional time series forecasting ” forecasting. In Advances in Neural Information Processing Systems (pp. 4837‐4846).arXiv preprint arXiv:1905.03806 2019.
Zamora‐MartinezF "SML2010 ” https://archive.ics.uci.edu/ml/datasets/SML2010 2014. Accessed on 9 November 2020.
Bevilacqua V, 2016, Adaptive bi‐objective genetic programming for data‐driven system modeling, International Conference on Intelligent Computing, 248
MaggioloMandSpanakisG.“Autoregressive convolutional recurrent neural network for Univariate and multivariate time series prediction ”2019.https://arxiv.org/abs/1903.02540.
QinY SongD ChenH ChengW JiangG andCottrellG.“A dual‐stage attention‐based recurrent neural network for time series prediction ”arXiv preprint arXiv:1704.02971 2017.
Wollsen MG, 2016, INNS Conference on Big Data, 71
Sun W., 2015, Predicting electrical power output by using granular computing based Neuro‐fuzzy modeling method, The 27th Chinese Control and Decision Conference (2015 CCDC), 2865
Roni M. H. K., 2017, An artificial neural network based predictive approach for analyzing environmental impact on combined cycle power plant generation, 2017 2nd International Conference on Electrical & Electronic Engineering (ICEEE), 1
Zhou Y, 2018, Voxelnet: end‐to‐end learning for point cloud based 3d object detection, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4490
Wang W, 2016, Multiagent‐based resource allocation for energy minimization in cloud computing systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47, 205
