[
Ahmad, T., Chen, H., 2018. Potential of three variant machine-learning models for forecasting district level medium-term and long-term energy demand in smart grid environment. Energy, 160. DOI: 10.1016/j.energy.2018.07.08410.1016/j.energy.2018.07.084
]Search in Google Scholar
[
Ali, S., Wu, K., Weston, K., Marinakis, D., 2016. A Machine Learning Approach to Meter Placement for Power Quality Estimation in Smart Grid. IEEE Transactions on Smart Grid, 7(3). DOI: 10.1109/TSG.2015.244283710.1109/TSG.2015.2442837
]Search in Google Scholar
[
Awan, N., Khan, S., Rahmani, M.K.I., Tahir, M., Alam, N.M.D., Alturki, R., Ullah, I., 2021. Machine Learning-Enabled Power Scheduling in IoT-Based Smart Cities. Computers, Materials and Continua, 67(2). DOI: 10.32604/cmc.2021.01438610.32604/cmc.2021.014386
]Search in Google Scholar
[
Azad, S., Sabrina, F., Wasimi, S., 2019. Transformation of smart grid using machine learning. 2019 29th Australasian Universities Power Engineering Conference, AUPEC 2019. DOI: 10.1109/AUPEC48547.2019.21180910.1109/AUPEC48547.2019.211809
]Search in Google Scholar
[
Danalakshmi, D., Prathiba, S., Ettappan, M., Krishna, D.M., 2021. Reparation of voltage disturbance using PR controller-based DVR in Modern power systems. Production Engineering Archives, 27(1). DOI: 10.30657/pea.2021.27.310.30657/pea.2021.27.3
]Search in Google Scholar
[
De Santis, E., Rizzi, A., Sadeghian, A., 2018. A cluster-based dissimilarity learning approach for localized fault classification in Smart Grids. Swarm and Evolutionary Computation, 39. DOI: 10.1016/j.swevo.2017.10.00710.1016/j.swevo.2017.10.007
]Search in Google Scholar
[
Deja, A., Kaup, M., Strulak-Wójcikiewicz, R., 2019. The concept of transport organization model in container logistics chains using inland waterway transport, Smart Innovation, Systems and Technologies, 2019, 155, 521-531.10.1007/978-981-13-9271-9_43
]Search in Google Scholar
[
Dharmadhikari, S.C., Gampala, V., Rao, C.M., Khasim, S., Jain, S., Bhaskaran, R., 2021. A smart grid incorporated with ML and IoT for a secure management system. Microprocessors and Microsystems, 83. DOI: 10.1016/j.micpro.2021.10395410.1016/j.micpro.2021.103954
]Search in Google Scholar
[
Haseeb, M., Kot, S., Iqbal Hussain, H., Kamarudin, F., 2021. The natural resources curse-economic growth hypotheses: Quantile–on–Quantile evidence from top Asian economies. Journal of Cleaner Production, 279. DOI: 10.1016/j.jclepro.2020.12359610.1016/j.jclepro.2020.123596
]Search in Google Scholar
[
Jamil, F., Iqbal, N., Imran, Ahmad, S., Kim, D., 2021. Peer-to-Peer Energy Trading Mechanism Based on Blockchain and Machine Learning for Sustainable Electrical Power Supply in Smart Grid. IEEE Access, 9. DOI: 10.1109/ACCESS.2021.306045710.1109/ACCESS.2021.3060457
]Search in Google Scholar
[
Li, D., Jayaweera, S.K., 2015. Machine-Learning Aided Optimal Customer Decisions for an Interactive Smart Grid. IEEE Systems Journal, 9(4). DOI: 10.1109/JSYST.2014.233463710.1109/JSYST.2014.2334637
]Search in Google Scholar
[
Mikita, M., Kolcun, M., Špes, M., Vojtek, M., Ivančák, M., 2017. Impact of electrical power load time management at sizing and cost of hybrid renewable power system. Polish Journal of Management Studies, 15(1). DOI: 10.17512/pjms.2017.15.1.1510.17512/pjms.2017.15.1.15
]Search in Google Scholar
[
Mohamed, M.A., Eltamaly, A.M., Farh, H.M., Alolah, A.I., 2015. Energy management and renewable energy integration in smart grid system. International Conference on Smart Energy Grid Engineering, SEGE 2015. DOI: 10.1109/SEGE.2015.732462110.1109/SEGE.2015.7324621
]Search in Google Scholar
[
Mukherjee, A., Mukherjee, P., Dey, N., De, D., Panigrahi, B.K., 2020. Lightweight sustainable intelligent load forecasting platform for smart grid applications. Sustainable Computing: Informatics and Systems, 25. DOI: 10.1016/j.suscom.2019.10035610.1016/j.suscom.2019.100356
]Search in Google Scholar
[
Muralitharan, K., Sakthivel, R., Vishnuvarthan, R., 2018. Neural network based optimization approach for energy demand prediction in smart grid. Neurocomputing, 273. DOI: 10.1016/j.neucom.2017.08.01710.1016/j.neucom.2017.08.017
]Search in Google Scholar
[
Nawaz, R., Akhtar, R., Shahid, M.A., Qureshi, I.M., Mahmood, M.H., 2021. Machine learning based false data injection in smart grid. International Journal of Electrical Power and Energy Systems, 130. DOI: 10.1016/j.ijepes.2021.10681910.1016/j.ijepes.2021.106819
]Search in Google Scholar
[
Omitaomu, O.A., Niu, H., 2021. Artificial Intelligence Techniques in Smart Grid: A Survey. Smart Cities, 4(2). DOI: 10.3390/smartcities402002910.3390/smartcities4020029
]Search in Google Scholar
[
Pallonetto, F., De Rosa, M., Milano, F., Finn, D.P., 2019. Demand response algorithms for smart-grid ready residential buildings using machine learning models. Applied Energy, 239. DOI: 10.1016/j.apenergy.2019.02.02010.1016/j.apenergy.2019.02.020
]Search in Google Scholar
[
Parvez, I., Aghili, M., Sarwat, A. I., Rahman, S., Alam, F., 2019. Online power quality disturbance detection by support vector machine in smart meter. Journal of Modern Power Systems and Clean Energy, 7(5). DOI: 10.1007/s40565-018-0488-z10.1007/s40565-018-0488-z
]Search in Google Scholar
[
Perera, K.S., Aung, Z., Woon, W.L., 2014. Machine Learning Techniques for Supporting Renewable Energy Generation and Integration: A Survey. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8817. DOI: 10.1007/978-3-319-13290-7_710.1007/978-3-319-13290-7_7
]Search in Google Scholar
[
Renugadevi, N., Saravanan, S., Naga Sudha, C.M., 2021. IoT based smart energy grid for sustainable cites. Materials Today: Proceedings. DOI: 10.1016/j.matpr.2021.02.27010.1016/j.matpr.2021.02.270
]Search in Google Scholar
[
Sabishchenko, O., Rębilas, R., Sczygiol, N., Urbański, M., 2020. Ukraine energy sector management using hybrid renewable energy systems. Energies, 13(7). DOI: 10.3390/en1307177610.3390/en13071776
]Search in Google Scholar
[
Sharmila, P., Baskaran, J., Nayanatara, C., Maheswari, R., 2019. A hybrid technique of machine learning and data analytics for optimized distribution of renewable energy resources targeting smart energy management. Procedia Computer Science, 165. DOI: 10.1016/j.procs.2020.01.07610.1016/j.procs.2020.01.076
]Search in Google Scholar
[
Smirnova, E., Kot, S., Kolpak, E., Shestak, V., 2021. Governmental support and renewable energy production: A cross-country review. Energy, 230. DOI: 10.1016/j.energy.2021.12090310.1016/j.energy.2021.120903
]Search in Google Scholar
[
Smirnova, E., Szczepańska-Woszczyna, K., Yessetova, S., Samusenkov, V., Rogulin, R., 2021. Supplying energy to vulnerable segments of the population: Macro-financial risks and public welfare. Energies, 14(7). DOI: 10.3390/en1407183410.3390/en14071834
]Search in Google Scholar
[
Szkutnik, J., Jakubiak, D., 2012. New trends in consumption management of electric energy. Polish Journal of Management Studies, 5.
]Search in Google Scholar
[
Taherian, H., Aghaebrahimi, M.R., Baringo, L., Goldani, S.R., 2021. Optimal dynamic pricing for an electricity retailer in the price-responsive environment of smart grid. International Journal of Electrical Power and Energy Systems, 130. DOI: 10.1016/j.ijepes.2021.10700410.1016/j.ijepes.2021.107004
]Search in Google Scholar
[
Ullah, Z., Al-Turjman, F., Mostarda, L., Gagliardi, R., 2020. Applications of Artificial Intelligence and Machine learning in smart cities. In Computer Communications, 154. DOI: 10.1016/j.comcom.2020.02.06910.1016/j.comcom.2020.02.069
]Search in Google Scholar
[
Ulewicz, R., Siwiec, D., Pacana, A., Tutak, M., Brodny, J., 2021. Multi-criteria method for the selection of renewable energy sources in the polish industrial sector, Energies, 14(9), 2386. DOI 10.3390/en1409238610.3390/en14092386
]Search in Google Scholar
[
Ungureanu, S., Topa, V., Cziker, A., 2019. Industrial load forecasting using machine learning in the context of smart grid. 2019 54th International Universities Power Engineering Conference, UPEC 2019 - Proceedings. DOI: 10.1109/UPEC.2019.889354010.1109/UPEC.2019.8893540
]Search in Google Scholar
[
van Kooten, G.C., 2013. Economic analysis of feed- in tariffs for generating electricity from renewable energy sources. In Handbook on Energy and Climate Change. DOI: 10.4337/9780857933690.0001710.4337/9780857933690.00017
]Search in Google Scholar
[
Wall, W.P., Khalid, B., Urbański, M., Kot, M., 2021. Factors influencing consumer’s adoption of renewable energy. Energies, 14(17). DOI: 10.3390/en1417542010.3390/en14175420
]Search in Google Scholar
[
Zekić-Sušac, M., Mitrović, S., Has, A., 2021. Machine learning based system for managing energy efficiency of public sector as an approach towards smart cities. International Journal of Information Management, 58. DOI: 10.1016/j.ijinfomgt.2020.10207410.1016/j.ijinfomgt.2020.102074
]Search in Google Scholar
[
Zou, H., Tao, J., Elsayed, S.K., Elattar, E.E., Almalaq, A., Mohamed, M.A., 2021. Stochastic multi-carrier energy management in the smart islands using reinforcement learning and unscented transform. International Journal of Electrical Power and Energy Systems, 130. DOI: 10.1016/j.ijepes.2021.10698810.1016/j.ijepes.2021.106988
]Search in Google Scholar