{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T16:06:36Z","timestamp":1778169996890,"version":"3.51.4"},"reference-count":74,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,6,8]],"date-time":"2022-06-08T00:00:00Z","timestamp":1654646400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Yayasan Universiti Teknologi PETRONAS Fundamental Research","award":["015-LCO0166"],"award-info":[{"award-number":["015-LCO0166"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>A smart grid is a modern electricity system enabling a bidirectional flow of communication that works on the notion of demand response. The stability prediction of the smart grid becomes necessary to make it more reliable and improve the efficiency and consistency of the electrical supply. Due to sensor or system failures, missing input data can often occur. It is worth noting that there has been no work conducted to predict the missing input variables in the past. Thus, this paper aims to develop an enhanced forecasting model to predict smart grid stability using neural networks to handle the missing data. Four case studies with missing input data are conducted. The missing data is predicted for each case, and then a model is prepared to predict the stability. The Levenberg\u2013Marquardt algorithm is used to train all the models and the transfer functions used are tansig and purelin in the hidden and output layers, respectively. The model\u2019s performance is evaluated on a four-node star network and is measured in terms of the MSE and R2 values. The four stability prediction models demonstrate good performances and depict the best training and prediction ability.<\/jats:p>","DOI":"10.3390\/s22124342","type":"journal-article","created":{"date-parts":[[2022,6,13]],"date-time":"2022-06-13T02:01:44Z","timestamp":1655085704000},"page":"4342","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Smart Grid Stability Prediction Model Using Neural Networks to Handle Missing Inputs"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3975-1253","authenticated-orcid":false,"given":"Madiah Binti","family":"Omar","sequence":"first","affiliation":[{"name":"Department of Chemical Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1939-0155","authenticated-orcid":false,"given":"Rosdiazli","family":"Ibrahim","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2226-4923","authenticated-orcid":false,"given":"Rhea","family":"Mantri","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Vellore Institute of Technology, Vellore 632014, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4753-0565","authenticated-orcid":false,"given":"Jhanavi","family":"Chaudhary","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Vellore Institute of Technology, Vellore 632014, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2033-2454","authenticated-orcid":false,"given":"Kaushik","family":"Ram Selvaraj","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Vellore Institute of Technology, Vellore 632014, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7333-7438","authenticated-orcid":false,"given":"Kishore","family":"Bingi","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Vellore Institute of Technology, Vellore 632014, India"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gharavi, H., and Ghafurian, R. (2011). Smart Grid: The Electric Energy System of the Future, IEEE.","DOI":"10.1109\/JPROC.2011.2124210"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"McLaughlin, K., Friedberg, I., Kang, B., Maynard, P., Sezer, S., and McWilliams, G. (2015). Secure communications in smart grid: Networking and protocols. Smart Grid Security, Elsevier.","DOI":"10.1016\/B978-0-12-802122-4.00005-5"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1016\/j.ijepes.2015.05.008","article-title":"Framework to manage multiple goals in community-based energy sharing network in smart grid","volume":"73","author":"Rathnayaka","year":"2015","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s42979-021-00463-5","article-title":"Predicting Smart Grid Stability with Optimized Deep Models","volume":"2","author":"Breviglieri","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1140\/epjst\/e2015-50136-y","article-title":"Taming instabilities in power grid networks by decentralized control","volume":"225","author":"Grabow","year":"2016","journal-title":"Eur. Phys. J. Spec. Top."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Verma, K., and Niazi, K. (2012, January 22\u201326). Generator coherency determination in a smart grid using artificial neural network. Proceedings of the 2012 IEEE Power and Energy Society General Meeting, San Diego, CA, USA.","DOI":"10.1109\/PESGM.2012.6345255"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Karthikumar, K., Karthik, K., Karunanithi, K., Chandrasekar, P., Sathyanathan, P., and Prakash, S.V.J. (2021). SSA-RBFNN strategy for optimum framework for energy management in Grid-Connected smart grid infrastructure modeling. Mater. Today Proc.","DOI":"10.1016\/j.matpr.2021.01.477"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Massaoudi, M., Abu-Rub, H., Refaat, S.S., Chihi, I., and Oueslati, F.S. (2021, January 19\u201320). Accurate Smart-Grid Stability Forecasting Based on Deep Learning: Point and Interval Estimation Method. Proceedings of the 2021 IEEE Kansas Power and Energy Conference (KPEC), Manhattan, KS, USA.","DOI":"10.1109\/KPEC51835.2021.9446196"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"7050","DOI":"10.1109\/TII.2021.3056867","article-title":"A Stacked GRU-RNN-based Approach for Predicting Renewable Energy and Electricity Load for Smart Grid Operation","volume":"17","author":"Xia","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1665","DOI":"10.1016\/j.renene.2020.09.141","article-title":"Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks","volume":"162","author":"Gao","year":"2020","journal-title":"Renew. Energy"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2443","DOI":"10.1109\/TII.2020.3000184","article-title":"A novel hybrid short-term load forecasting method of smart grid using mlr and lstm neural network","volume":"17","author":"Li","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"824","DOI":"10.1007\/s13198-019-00884-9","article-title":"Energy load forecasting model based on deep neural networks for smart grids","volume":"11","author":"Mohammad","year":"2020","journal-title":"Int. J. Syst. Assur. Eng. Manag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"6684","DOI":"10.1109\/TSG.2017.2718241","article-title":"Advanced and adaptive dispatch for smart grids by means of predictive models","volume":"9","author":"Capizzi","year":"2018","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"7961","DOI":"10.1002\/er.6449","article-title":"Computer-assisted demand-side energy management in residential smart grid employing novel pooling deep learning algorithm","volume":"45","author":"Jeyaraj","year":"2021","journal-title":"Int. J. Energy Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"877","DOI":"10.1007\/s00521-016-2408-3","article-title":"Development of chaotically improved meta-heuristics and modified BP neural network-based model for electrical energy demand prediction in smart grid","volume":"28","author":"Islam","year":"2017","journal-title":"Neural Comput. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Gupta, S., Kazi, F., Wagh, S., and Kambli, R. (2015). Neural network based early warning system for an emerging blackout in smart grid power networks. Intelligent Distributed Computing, Springer.","DOI":"10.1007\/978-3-319-11227-5_16"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Neupane, B., Perera, K.S., Aung, Z., and Woon, W.L. (2012, January 18\u201320). Artificial neural network-based electricity price forecasting for smart grid deployment. Proceedings of the 2012 International Conference on Computer Systems and Industrial Informatics, Sharjah, United Arab Emirates.","DOI":"10.1109\/ICCSII.2012.6454392"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Verma, K., and Niazi, K. (2011, January 16\u201318). Determination of vulnerable machines for online transient security assessment in smart grid using artificial neural network. Proceedings of the 2011 Annual IEEE India Conference, Yderabad, India.","DOI":"10.1109\/INDCON.2011.6139562"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1007\/s10706-004-8680-5","article-title":"A study of slope stability prediction using neural networks","volume":"23","author":"Sakellariou","year":"2005","journal-title":"Geotech. Geol. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1225","DOI":"10.1016\/S0895-4356(96)00002-9","article-title":"Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes","volume":"49","author":"Tu","year":"1996","journal-title":"J. Clin. Epidemiol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.jclinepi.2021.11.023","article-title":"Missing data is poorly handled and reported in prediction model studies using machine learning: A literature review","volume":"142","author":"Nijman","year":"2022","journal-title":"J. Clin. Epidemiol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"8218","DOI":"10.1109\/JIOT.2020.2983911","article-title":"Locational detection of the false data injection attack in a smart grid: A multilabel classification approach","volume":"7","author":"Wang","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Niu, X., Li, J., Sun, J., and Tomsovic, K. (2019, January 18\u201321). Dynamic detection of false data injection attack in smart grid using deep learning. Proceedings of the 2019 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA.","DOI":"10.1109\/ISGT.2019.8791598"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"12788","DOI":"10.1109\/ACCESS.2017.2723360","article-title":"WNN-LQE: Wavelet-neural-network-based link quality estimation for smart grid WSNs","volume":"5","author":"Sun","year":"2017","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ungureanu, S., \u0162opa, V., and Cziker, A. (2019, January 21\u201323). Integrating the industrial consumer into smart grid by load curve forecasting using machine learning. Proceedings of the 2019 8th International Conference on Modern Power Systems (MPS), Cluj-Napoca, Romania.","DOI":"10.1109\/MPS.2019.8759707"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Alamaniotis, M. (October, January 29). Synergism of deep neural network and elm for smart very-short-term load forecasting. Proceedings of the 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), Bucharest, Romania.","DOI":"10.1109\/ISGTEurope.2019.8905686"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zahid, M., Ahmed, F., Javaid, N., Abbasi, R.A., Zainab Kazmi, H.S., Javaid, A., Bilal, M., Akbar, M., and Ilahi, M. (2019). Electricity price and load forecasting using enhanced convolutional neural network and enhanced support vector regression in smart grids. Electronics, 8.","DOI":"10.3390\/electronics8020122"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"\u00c7avdar, \u0130.H., and Faryad, V. (2019). New design of a supervised energy disaggregation model based on the deep neural network for a smart grid. Energies, 12.","DOI":"10.3390\/en12071217"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Selim, M., Zhou, R., Feng, W., and Quinsey, P. (2021). Estimating Energy Forecasting Uncertainty for Reliable AI Autonomous Smart Grid Design. Energies, 14.","DOI":"10.3390\/en14010247"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"85454","DOI":"10.1109\/ACCESS.2020.2991067","article-title":"A multidirectional LSTM model for predicting the stability of a smart grid","volume":"8","author":"Alazab","year":"2020","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Hasan, M., Toma, R.N., Nahid, A.A., Islam, M., and Kim, J.M. (2019). Electricity theft detection in smart grid systems: A CNN-LSTM based approach. Energies, 12.","DOI":"10.3390\/en12173310"},{"key":"ref_32","first-page":"21589379","article-title":"E2DNet: An Ensembling Deep Neural Network for Solving Nonconvex Economic Dispatch in Smart Grid","volume":"18","author":"Xu","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"55785","DOI":"10.1109\/ACCESS.2020.2981817","article-title":"A deep learning method for short-term residential load forecasting in smart grid","volume":"8","author":"Hong","year":"2020","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1049\/stg2.12042","article-title":"An aggregator-based resource allocation in the smart grid using an artificial neural network and sliding time window optimization","volume":"4","author":"Zheng","year":"2021","journal-title":"IET Smart Grid"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"387","DOI":"10.34768\/amcs-2021-0026","article-title":"Forecasting models for chaotic fractional-order oscillators using neural networks","volume":"31","author":"Bingi","year":"2021","journal-title":"Int. J. Appl. Math. Comput. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Bingi, K., and Prusty, B.R. (2021, January 1\u20133). Chaotic Time Series Prediction Model for Fractional-Order Duffing\u2019s Oscillator. Proceedings of the 2021 8th International Conference on Smart Computing and Communications (ICSCC), Kochi, India.","DOI":"10.1109\/ICSCC51209.2021.9528128"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Bingi, K., and Prusty, B.R. (2021, January 27\u201329). Neural Network-Based Models for Prediction of Smart Grid Stability. Proceedings of the 2021 Innovations in Power and Advanced Computing Technologies (i-PACT), Kuala Lumpur, Malaysia.","DOI":"10.1109\/i-PACT52855.2021.9696517"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1016\/j.eng.2019.04.012","article-title":"Applying neural-network-based machine learning to additive manufacturing: Current applications, challenges, and future perspectives","volume":"5","author":"Qi","year":"2019","journal-title":"Engineering"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"108474","DOI":"10.1016\/j.measurement.2020.108474","article-title":"Applicability of time-domain feature extraction methods and artificial intelligence in two-phase flow meters based on gamma-ray absorption technique","volume":"168","author":"Sattari","year":"2021","journal-title":"Measurement"},{"key":"ref_40","first-page":"2419","article-title":"Hybrid renewable energy based smart grid system for reactive power management and voltage profile enhancement using artificial neural network","volume":"43","author":"Chandrasekaran","year":"2021","journal-title":"Energy Sources Part A Recover. Util. Environ. Eff."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"606","DOI":"10.35833\/MPCE.2020.000569","article-title":"Unsupervised Learning for Non-Intrusive Load Monitoring in Smart Grid Based on Spiking Deep Neural Network","volume":"10","author":"Zhou","year":"2021","journal-title":"J. Mod. Power Syst. Clean Energy"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2752","DOI":"10.1109\/TII.2020.3007167","article-title":"Distributed deep reinforcement learning for intelligent load scheduling in residential smart grids","volume":"17","author":"Chung","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_43","unstructured":"Cahyono, M.R.A. (2021, January 27\u201328). Design Power Controller for Smart Grid System Based on Internet of Things Devices and Artificial Neural Network. Proceedings of the 2020 IEEE International Conference on Internet of Things and Intelligence System (IoTaIS), Bali, Indonesia."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"e4062","DOI":"10.1002\/ett.4062","article-title":"Intelligent intrusion detection system in smart grid using computational intelligence and machine learning","volume":"32","author":"Khan","year":"2021","journal-title":"Trans. Emerg. Telecommun. Technol."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Ullah, A., Javaid, N., Samuel, O., Imran, M., and Shoaib, M. (2020, January 15\u201319). CNN and GRU based deep neural network for electricity theft detection to secure smart grid. Proceedings of the 2020 International Wireless Communications and Mobile Computing (IWCMC), Limassol, Cyprus.","DOI":"10.1109\/IWCMC48107.2020.9148314"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"114636","DOI":"10.1016\/j.apenergy.2020.114636","article-title":"Neural-network-based Lagrange multiplier selection for distributed demand response in smart grid","volume":"264","author":"Ruan","year":"2020","journal-title":"Appl. Energy"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.matcom.2020.05.010","article-title":"An artificial neural network-based forecasting model of energy-related time series for electrical grid management","volume":"184","author":"Luna","year":"2021","journal-title":"Math. Comput. Simul."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Khalid, Z., Abbas, G., Awais, M., Alquthami, T., and Rasheed, M.B. (2020). A novel load scheduling mechanism using artificial neural network based customer profiles in smart grid. Energies, 13.","DOI":"10.3390\/en13051062"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Fan, L., Li, J., Pan, Y., Wang, S., Yan, C., and Yao, D. (2019, January 6\u20139). Research and application of smart grid early warning decision platform based on big data analysis. Proceedings of the 2019 4th International Conference on Intelligent Green Building and Smart Grid (IGBSG), Yichang, China.","DOI":"10.1109\/IGBSG.2019.8886291"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Li, G., Wang, H., Zhang, S., Xin, J., and Liu, H. (2019). Recurrent neural networks based photovoltaic power forecasting approach. Energies, 12.","DOI":"10.3390\/en12132538"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"139909","DOI":"10.1109\/ACCESS.2019.2943886","article-title":"Forecasting hourly solar irradiance using hybrid wavelet transformation and Elman model in smart grid","volume":"7","author":"Huang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"9427","DOI":"10.1007\/s00521-019-04453-w","article-title":"A whale optimization algorithm-trained artificial neural network for smart grid cyber intrusion detection","volume":"32","author":"Haghnegahdar","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Ahmed, F., Zahid, M., Javaid, N., Khan, A.B.M., Khan, Z.A., and Murtaza, Z. (2019). A deep learning approach towards price forecasting using enhanced convolutional neural network in smart grid. International Conference on Emerging Internetworking, Data & Web Technologies, Springer.","DOI":"10.1007\/978-3-030-12839-5_25"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Duong-Ngoc, H., Nguyen-Thanh, H., and Nguyen-Minh, T. (2019, January 22\u201323). Short term load forcast using deep learning. Proceedings of the 2019 Innovations in Power and Advanced Computing Technologies (i-PACT), Vellore, India.","DOI":"10.1109\/i-PACT44901.2019.8960036"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Kulkarni, S.N., and Shingare, P. (2018, January 11\u201313). Artificial Neural Network Based Short Term Power Demand Forecast for Smart Grid. Proceedings of the 2018 IEEE Conference on Technologies for Sustainability (SusTech), Long Beach, CA, USA.","DOI":"10.1109\/SusTech.2018.8671340"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1016\/j.ijepes.2018.01.036","article-title":"Detection of illegal consumers using pattern classification approach combined with Levenberg-Marquardt method in smart grid","volume":"99","author":"Ghasemi","year":"2018","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_57","first-page":"102","article-title":"Smart grid load forecasting using online support vector regression","volume":"65","author":"Ezzeddine","year":"2017","journal-title":"Comput. Electr. Eng."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2587","DOI":"10.1109\/TII.2016.2638322","article-title":"An accurate and fast converging short-term load forecasting model for industrial applications in a smart grid","volume":"13","author":"Ahmad","year":"2017","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Li, L., Ota, K., and Dong, M. (2017, January 21\u201323). Everything is image: CNN-based short-term electrical load forecasting for smart grid. Proceedings of the 2017 14th International Symposium on Pervasive Systems, Algorithms and Networks & 2017 11th International Conference on Frontier of Computer Science and Technology & 2017 Third International Symposium of Creative Computing (ISPAN-FCST-ISCC), Exeter, UK.","DOI":"10.1109\/ISPAN-FCST-ISCC.2017.78"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1016\/j.energy.2016.10.050","article-title":"Maximizing performance of fuel cell using artificial neural network approach for smart grid applications","volume":"116","author":"Bicer","year":"2016","journal-title":"Energy"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.rser.2014.08.035","article-title":"Demand side management using artificial neural networks in a smart grid environment","volume":"41","author":"Macedo","year":"2015","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Muralidharan, S., Roy, A., and Saxena, N. (2014, January 22\u201324). Stochastic hourly load forecasting for smart grids in korea using narx model. Proceedings of the 2014 International Conference on Information and Communication Technology Convergence (ICTC), Busan, Korea.","DOI":"10.1109\/ICTC.2014.6983109"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Ioakimidis, C., Eliasstam, H., and Rycerski, P. (2012, January 29\u201331). Solar power forecasting of a residential location as part of a smart grid structure. Proceedings of the 2012 IEEE Energytech, Cleveland, OH, USA.","DOI":"10.1109\/EnergyTech.2012.6304674"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TSG.2012.2187220","article-title":"An intelligent wide area synchrophasor based system for predicting and mitigating transient instabilities","volume":"3","author":"Hashiesh","year":"2012","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_65","unstructured":"Fei, W., Zengqiang, M., Shi, S., and Chengcheng, Z. (2011, January 13\u201316). A practical model for single-step power prediction of grid-connected PV plant using artificial neural network. Proceedings of the 2011 IEEE PES Innovative Smart Grid Technologies, Perth, WA, USA."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.egypro.2011.10.003","article-title":"Power distribution system planning for smart grid applications using ANN","volume":"12","author":"Qudaih","year":"2011","journal-title":"Energy Procedia"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Zhang, H.T., Xu, F.Y., and Zhou, L. (2010, January 11\u201314). Artificial neural network for load forecasting in smart grid. Proceedings of the 2010 International Conference on Machine Learning and Cybernetics, Qingdao, China.","DOI":"10.1109\/ICMLC.2010.5580713"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Arzamasov, V., B\u00f6hm, K., and Jochem, P. (2018, January 29\u201331). Towards concise models of grid stability. Proceedings of the 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), Aalborg, Denmark.","DOI":"10.1109\/SmartGridComm.2018.8587498"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.tjem.2018.08.001","article-title":"User\u2019s guide to correlation coefficients","volume":"18","author":"Akoglu","year":"2018","journal-title":"Turk. J. Emerg. Med."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Sheela, K.G., and Deepa, S.N. (2013). Review on methods to fix number of hidden neurons in neural networks. Math. Probl. Eng., 2013.","DOI":"10.1155\/2013\/425740"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Bingi, K., Prusty, B.R., Panda, K.P., and Panda, G. (2022). Time Series Forecasting Model for Chaotic Fractional-Order R\u00f6ssler System. Sustainable Energy and Technological Advancements, Springer.","DOI":"10.1007\/978-981-16-9033-4_60"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"14771","DOI":"10.1007\/s00521-021-06116-1","article-title":"An intelligent model to predict the life condition of crude oil pipelines using artificial neural networks","volume":"33","author":"Shaik","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Bingi, K., Prusty, B.R., Kumra, A., and Chawla, A. (2021, January 5\u20137). Torque and temperature prediction for permanent magnet synchronous motor using neural networks. Proceedings of the 2020 3rd International Conference on Energy, Power and Environment: Towards Clean Energy Technologies, Shillong, India.","DOI":"10.1109\/ICEPE50861.2021.9404536"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Ramadevi, B., and Bingi, K. (2022). Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review. Symmetry, 14.","DOI":"10.3390\/sym14050955"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/12\/4342\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:25:56Z","timestamp":1760138756000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/12\/4342"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,8]]},"references-count":74,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["s22124342"],"URL":"https:\/\/doi.org\/10.3390\/s22124342","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,8]]}}}