[
Amrouche, B., & Le Pivert, X. (2014). Artificial neural network based daily local forecasting for global solar radiation, Applied Energy, 130, 333-341.10.1016/j.apenergy.2014.05.055
]Search in Google Scholar
[
Bahgat, A.B.G., Helwa, N.H., Ahamd, G.E., & El Shenawy, E.T. (2004). Estimation of the maximum power and normal operating power of a photovoltaic module by neural networks, Renewable Energy, 29(3), 443-457.10.1016/S0960-1481(03)00126-5
]Search in Google Scholar
[
Barrera, J.M., Reina, A.,Maté, A.,& Trujillo, J.C. (2020). Solar Energy Prediction Model Based on Artificial Neural Networks and Open Data, Sustainability, 12(17), 6915.10.3390/su12176915
]Search in Google Scholar
[
Botea, R. (2020). Energiile regenerabile au acoperit 42% din consumul de energie al României, cu 10 puncteprocentualepeste media europeană. Ziarul Financiar. https://www.zf.ro/eveniment/energiile-regenerabile-au-acoperit-42-din-consumul-de-energie-al-romanieicu-10-puncte-procentuale-peste-media-europeana-18764797.
]Search in Google Scholar
[
Chollet, F. (2017). Deep Learning with Python, Greenwich, CT, Manning Publications.
]Search in Google Scholar
[
Cococcioni, M., D’Andrea, E., & Lazzerini, B. (2011). 24-h-ahead forecasting of energy production in solar PV systems. 11th International Conference on Intelligent Systems Design and Applications, Cordoba, Spain, 1276-1281, http://doi.org/10.1109/ISDA.2011.6121835.10.1109/ISDA.2011.6121835
]Search in Google Scholar
[
European Commission. (n.d.) EU climate action and the European Green Deal. https://ec.europa.eu/clima/policies/eu-climate-action_en.
]Search in Google Scholar
[
European Court of Auditors. (2019). Table B – Percentage share of renewable energy in gross final energy consumption in the four Member States examined between 2010 and 2017 and trajectories until 2020. Wind and solar power for electricity generation: significant action needed if EU targets to be met. https://op.europa.eu/webpub/eca/special-reports/wind-solar-power-generation-8-2019/ro/index.html#h2table5.
]Search in Google Scholar
[
Fawcett, T., & Provost, F. (2013). Data Science for Business, Newton, MA: O’Relly.
]Search in Google Scholar
[
Gala, Y., Fernández, A.,Dorronsoro, J., García, M.,&Rodríguez, C. (2014). Machine Learning Prediction of Global Photovoltaic Energy in Spain, Renewable Energy and Power Quality Journal, 12, 605-610.10.24084/repqj12.423
]Search in Google Scholar
[
Hanski, J., Uusitalo, T., Vainio, H., Kunttu, S., Valkokari, P., Kortelainen, H., & Koskinen, K. (2018). Smart asset management as a service Deliverable 2.0. [pdf], Retrieved from http://doi.org/10.13140/RG.2.2.31027.94244.
]Search in Google Scholar
[
IBM Corporation (2019). CRISP-DM Help Overview.IBM SPSS Modeler CRISP-DM Guide. https://www.ibm.com/support/knowledgecenter/SS3RA7_sub/modeler_crispdm_ddita/clementine/crisp_help/crisp_overview.html.
]Search in Google Scholar
[
Jarvis, A.J., Leedal, D.T., & Hewitt, C.N. (2012). Climate-society feedbacks and the avoidance of dangerous climate change, Nature Climate Change, 2, 668-671.10.1038/nclimate1586
]Search in Google Scholar
[
Jawaid, F., & Junejo, K.N. (2016). Predicting Daily Mean Solar Power Using Machine Learning Regression Techniques. 2016 Sixth International Conference on Innovative Computing Technology (INTECH), Dublin, Ireland, 355-360, http://doi.org/10.1109/INTECH.2016.7845051.10.1109/INTECH.2016.7845051
]Search in Google Scholar
[
Kahan, A. (2019, September 24). EIA projects nearly 50% increase in world energy usage by 2050, led by growth in Asia, U.S. Energy Information Administration. https://www.eia.gov/todayinenergy/detail.php?id=41433.
]Search in Google Scholar
[
Krishna, P.G., Ravi, K.S., Kishore, K.H., Veni, K.K., Rao, K.N.S.,& Prasad, R.D. (2018). Design and development of bi-directional IoT gateway using ZigBee and Wi-Fi technologies with MQTT protocol, International Journal of Engineering & Technology, 7(2.8), 125-129.10.14419/ijet.v7i2.8.10344
]Search in Google Scholar
[
Laouafi, A.,Mordjaoui, M., & Dib, D. (2015). One-hour ahead electric load and wind-solar power generation forecasting using artificial neural network. 6th International Renewable Energy Congress, Sousse, Tunisia, 1-6. http://doi.org/10.1109/IREC.2015.7110894.10.1109/IREC.2015.7110894
]Search in Google Scholar
[
McCulloch, W.S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity, Bulletin of Mathematical Biophysics, 5(5), 115-133.10.1007/BF02478259
]Search in Google Scholar
[
Mellit, A., Benghanem, M., & Bendekhis, M. (2005). Artificial neural network model for prediction solar radiation data: Application for sizing stand-alone photovoltaic power system. 2005 IEEE Power Engineering Society General Meeting, San Francisco, CA, 1, 40-44, http://doi.org/10.1109/PES.2005.1489526.10.1109/PES.2005.1489526
]Search in Google Scholar
[
Mellit, A., Saglam, S., & Kalogirou, S.A. (2013). Artificial neural network-based model for estimating the produced power of a photovoltaic module, Renewable Energy, 60, 71-78.10.1016/j.renene.2013.04.011
]Search in Google Scholar
[
Open Power System Data. (2021). Open Power System Data: A free and open data platform for power system modelling, https://open-power-system-data.org/.
]Search in Google Scholar
[
Quiles, E., Roldán-Blay, C., Escrivá-Escrivá, G., & Porta, C.R. (2020). Accurate Sizing of Residential Stand-Alone Photovoltaic Systems Considering System Reliability, Sustainability, 12, 1274.10.3390/su12031274
]Search in Google Scholar
[
Rumelhart, D.E., Hinton, G.E., & Williams, R.J. (1986). Learning representations by back-propagating errors, Nature, 323, 533-536.10.1038/323533a0
]Search in Google Scholar
[
Singh, V. (2020). 10 Benefits of Using Cloud Storage. Cloud Academy. https://cloudacademy.com/blog/10-benefits-of-using-cloud-storage/.
]Search in Google Scholar
[
Voon, J. (2017). Create, collect and consume: the fundamentals of the internet of things. Wired. https://www.wired.co.uk/article/create-collect-and-consume-the-fundamentals-of-iot.
]Search in Google Scholar
