4,109
Views
3
CrossRef citations to date
0
Altmetric
Research Articles

AI-based forecasting for optimised solar energy management and smart grid efficiency

, , &
Pages 4623-4644 | Received 15 Jun 2023, Accepted 27 Sep 2023, Published online: 16 Oct 2023

References

  • Adeh, Elnaz H., Stephen P. Good, Marc Calaf, and Chad W. Higgins. 2019. “Solar PV Power Potential is Greatest Over Croplands.” Scientific Reports 9 (1): 11442. https://doi.org/10.1038/s41598-019-47803-3.
  • Aghelinejad, MohammadMohsen, Yassine Ouazene, and Alice Yalaoui. 2018. “Production Scheduling Optimisation with Machine State and Time-dependent Energy Costs.” International Journal of Production Research 56 (16): 5558–5575. https://doi.org/10.1080/00207543.2017.1414969.
  • Akhter, Muhammad Naveed, Saad Mekhilef, Hazlie Mokhlis, and Noraisyah Mohamed Shah. 2019. “Review on Forecasting of Photovoltaic Power Generation Based on Machine Learning and Metaheuristic Techniques.” IET Renewable Power Generation 13 (7): 1009–1021.
  • Al-falahi, Monaaf D. A., S. D. G. Jayasinghe, and H. Enshaei. 2017. “A Review on Recent Size Optimization Methodologies for Standalone Solar and Wind Hybrid Renewable Energy System.” Energy Conversion and Management 143: 252–274. https://doi.org/10.1016/j.enconman.2017.04.019.
  • Alam, Md. Morshed, Md. Habibur Rahman, Md. Faisal Ahmed, Mostafa Zaman Chowdhury, and Yeong Min Jang. 2022. “Deep Learning Based Optimal Energy Management for Photovoltaic and Battery Energy Storage Integrated Home Micro-grid System.” Scientific Reports 12 (1): 15133. https://doi.org/10.1038/s41598-022-19147-y.
  • Alsadi, Samer, and Tamer Khatib. 2018. “Photovoltaic Power Systems Optimization Research Status: A Review of Criteria, Constrains, Models, Techniques, and Software Tools.” Applied Sciences 8 (10): 1761. https://doi.org/10.3390/app8101761.
  • Antonanzas, J., N. Osorio, R. Escobar, R. Urraca, F. J. Martinez-de-Pison, and F. Antonanzas-Torres. 2016. “Review of Photovoltaic Power Forecasting.” Solar Energy 136: 78–111.
  • Apogee Instruments, Inc. 2021. Owner's Manual, Pyranometer, Models SP-110 and SP-230.
  • Aryaputera, A. W., D. Yang, and W. M. Walsh. 2015. “Day-Ahead Solar Irradiance Forecasting in a Tropical Environment.” Journal of Solar Energy Engineering 137 (5051009. https://doi.org/10.1115/1.4030231.
  • Black, Fischer, and Myron Scholes. 1973. “The Pricing of Options and Corporate Liabilities.” Journal of Political Economy 81 (3): 637–654. https://doi.org/10.1086/260062.
  • Bostrom, Nick. 2014. Superintelligence: Paths, Dangers, Strategies. 1st. ed. Oxford: Oxford University Press. OCLC: ocn881706835.
  • Boxwell, Michael. 2012. Solar Electricity Handbook: A Simple Practical Guide to Solar Energy: How to Design and Install Photovoltaic Solar Electric Systems. Ryton On Dunsmore, Warwickshire, U.K.: Greenstream Publishing.
  • Busby, Joshua W., Kyri Baker, Morgan D. Bazilian, Alex Q. Gilbert, Emily Grubert, Varun Rai, Joshua D. Rhodes, et al. 2021. “Cascading Risks: Understanding the 2021 Winter Blackout in Texas.” Energy Research & Social Science 77:102106. https://doi.org/10.1016/j.erss.2021.102106.
  • Chandola, D., H. Gupta, V. A. Tikkiwal, and M. K. Bohra. 2020. “Multi-step Ahead Forecasting of Global Solar Radiation for Arid Zones Using Deep Learning.” Procedia Computer Science 167:626–635. https://doi.org/10.1016/j.procs.2020.03.329.
  • Choi, Tsan-Ming, Alexandre Dolgui, Dmitry Ivanov, and Erwin Pesch. 2022. “OR and Analytics for Digital, Resilient, and Sustainable Manufacturing 4.0.” Annals of Operations Research 310 (1): 1–6. https://doi.org/10.1007/s10479-022-04536-3.
  • Chollet, F. 2015. “Keras.”.
  • Chu, Y., B. Urquhart, S. M. I. Gohari, H. T. C. Pedro, J. Kleissl, and C. F. M. Coimbra. 2014. “Short-term Reforecasting of Power Output From a 48 MWe Solar PV Plant.” Solar Energy 112: 68–77.
  • Das, Utpal Kumar, Kok Soon Tey, Mehdi Seyedmahmoudian, Saad Mekhilef, Moh Yamani Idna Idris, Willem Van Deventer, Bend Horan, and Alex Stojcevski. 2018. “Forecasting of Photovoltaic Power Generation and Model Optimization: A Review.” Renewable and Sustainable Energy Reviews81:912–928. https://doi.org/10.1016/j.rser.2017.08.017.
  • Dileep, G. 2020. “A Survey on Smart Grid Technologies and Applications.” Renewable Energy146:2589–2625. https://doi.org/10.1016/j.renene.2019.08.092.
  • Dinçer, M. E., and F. Mera. 2010. “Critical Factors that Affecting Efficiency of Solar Cells.” Smart Grid and Renewable Energy 1 (1): 47–50. https://doi.org/10.4236/sgre.2010.11007.
  • Doi, Masao, and Samuel Frederick Edwards. 1988. The Theory of Polymer Dynamics. Vol. 73. Oxford: Oxford University Press.
  • Dolgui, Alexandre, and Dmitry Ivanov. 2022. “5G in Digital Supply Chain and Operations Management: Fostering Flexibility, End-to-end Connectivity and Real-time Visibility Through Internet-of-everything.” International Journal of Production Research 60 (2): 442–451. https://doi.org/10.1080/00207543.2021.2002969.
  • Elexon. 2019. The Electricity Trading Arrangements, A Beginner's Guide.
  • Elibol, Erdem, Özge Tüzün Özmen, Nedim Tutkun, and Oğuz Köysal. 2017. “Outdoor Performance Analysis of Different PV Panel Types.” Renewable and Sustainable Energy Reviews 67:651–661. https://doi.org/10.1016/j.rser.2016.09.051.
  • Engerer, N., and F. Mills. 2014. “K-PV: A Clear-sky Index for Photovoltaics.” Solar Energy 105:679–693. https://doi.org/10.1016/j.solener.2014.04.019.
  • ESO. 2021. Net Zero Market Reform. Accessed May 13, 2023. https://www.nationalgrideso.com/document/189356/download.
  • Feng, C., and J. Zhang. 2020. “SolarNet: A Sky Image-based Deep Convolutional Neural Network for Intra-hour Solar Forecasting.” Solar Energy 204:71–78. https://doi.org/10.1016/j.solener.2020.03.083.
  • Fleuret, F. 2021. “UNIGE 14x050 – EPFL EE-559 – Deep Learning.”.
  • Fortune Business Insights. 2023. “Machine Learning Market Size, Share, Growth & Trends.” Mar. Accessed May 13, 2023. https://www.fortunebusinessinsights.com/machine-learning-market-102226.
  • France, Ryan M., John F. Geisz, Tao Song, Waldo Olavarria, Michelle Young, Alan Kibbler, and Myles A. Steiner. 2022. “Triple-junction Solar Cells with 39.5% Terrestrial and 34.2% Space Efficiency Enabled by Thick Quantum Well Superlattices.” Joule 6 (5): 1121–1135. https://doi.org/10.1016/j.joule.2022.04.024.
  • Gamarro, H., J. E. Gonzalez, and L. E. Ortiz. 2019. “On the Assessment of a Numerical Weather Prediction Model for Solar Photovoltaic Power Forecasts in Cities.” Journal of Energy Resources Technology1 141 (6): 061203. https://doi.org/10.1115/1.4042972.
  • Gautier, Axel, Julien Jacqmin, and Jean-Christophe Poudou. 2018. “The Prosumers and the Grid.” Journal of Regulatory Economics 53 (1): 100–126. https://doi.org/10.1007/s11149-018-9350-5.
  • Gbémou, S., J. Eynard, S. Thil, E. Guillot, and S. Grieu. 2021. “A Comparative Study of Machine Learning-Based Methods for Global Horizontal Irradiance Forecasting.” Energies 14 (11): 3192. https://doi.org/10.3390/en14113192.
  • Gers, Felix A., Jürgen Schmidhuber, and Fred Cummins. 2000. “Learning to Forget: Continual Prediction with LSTM.” Neural Computation 12 (10): 2451–2471. https://doi.org/10.1162/089976600300015015.
  • Goel, Narendra S., and Nira Richter-Dyn. 2016. Stochastic Models in Biology. Amsterdam: Elsevier.
  • Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. Cambridge, MA: MIT Press.
  • Graves, Alex, Navdeep Jaitly, and Abdel-rahman Mohamed. 2013. “Hybrid Speech Recognition with Deep Bidirectional LSTM.” In 2013 IEEE Workshop on Automatic Speech Recognition and Understanding, 273–278. IEEE.
  • Green, Martin A., Ewan D. Dunlop, Jochen Hohl-Ebinger, Masahiro Yoshita, Nikos Kopidakis, Karsten Bothe, David Hinken, Michael Rauer, and Xiaojing Hao. 2022. “Solar Cell Efficiency Tables (Version 60).” Progress in Photovoltaics: Research and Applications 30 (7): 687–701. https://doi.org/10.1002/pip.v30.7.
  • Hewamalage, Hansika, Klaus Ackermann, and Christoph Bergmeir. 2023. “Forecast Evaluation for Data Scientists: Common Pitfalls and Best Practices.” Data Mining and Knowledge Discovery 37 (2): 788–832. https://doi.org/10.1007/s10618-022-00894-5.
  • Hochreiter, Sepp, and Jürgen Schmidhuber. 1997. “Long Short-term Memory.” Neural Computation9 (8): 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735.
  • Holmgren, F. W., C. W. Hansen, and M. A. Mikofski. 2018. “pvlib Python: a Python Package for Modeling Solar Energy Systems.” Journal of Open Source Software 3 (29): 884. https://doi.org/10.21105/joss.
  • Huang, X., C. Zhang, Q. Li, Y. Tai, B. Gao, and J. Shi. 2020. “A Comparison of Hour-Ahead Solar Irradiance Forecasting Models Based on LSTM Network.” Mathematical Problems in Engineering 2020: 1–15.
  • Hyndman, R. J., and A. B. Koehler. 2006. “Another Look At Measures of Forecast Accuracy.” International Journal of Forecasting 22 (4): 679–688. https://doi.org/10.1016/j.ijforecast.2006.03.001.
  • Ibrahim, O., N. Z. Yahaya, N. Saad, and M. Umar. 2015. “Matlab/Simulink Model of Solar PV Array With Perturb and Observe MPPT for Maximising PV Array Efficiency.” 2015 IEEE Conference on Energy Conversion (CENCON), 254–258.
  • IEA. 2022. “How to Avoid Gas Shortages in the European Union in 2023.” International Energy Agency Paris. Accessed May 13, 2023.
  • IEA. 2023. “How the European Union can Avoid Natural Gas Shortage.” International Energy Agency Paris. Accessed May 13, 2023.
  • IMF. 2022. “Europe Must Address a Toxic Mix of High Inflation and Flagging Growth.” International Monetary Fun Blog. Accessed May 13, 2023.
  • Ivanov, Dmitry. 2018. “Revealing Interfaces of Supply Chain Resilience and Sustainability: a Simulation Study.” International Journal of Production Research 56 (10): 3507–3523. https://doi.org/10.1080/00207543.2017.1343507.
  • Ivanov, Dmitry. 2022. “Viable Supply Chain Model: Integrating Agility, Resilience and Sustainability Perspectives – lessons From and Thinking Beyond the COVID-19 Pandemic.” Annals of Operations Research 319 (1): 1411–1431. https://doi.org/10.1007/s10479-020-03640-6.
  • Ivanov, Dmitry. 2023. “The Industry 5.0 Framework: Viability-based Integration of the Resilience, Sustainability, and Human-centricity Perspectives.” International Journal of Production Research 61 (5): 1683–1695. https://doi.org/10.1080/00207543.2022.2118892.
  • Ivanov, Dmitry, and Alexandre Dolgui. 2022. “The Shortage Economy and Its Implications for Supply Chain and Operations Management.” International Journal of Production Research 60 (24): 7141–7154. https://doi.org/10.1080/00207543.2022.2118889.
  • Ivanov, Dmitry, Alexandre Dolgui, Jennifer V. Blackhurst, and Tsan-Ming Choi. 2023. “Toward Supply Chain Viability Theory: From Lessons Learned Through COVID-19 Pandemic to Viable Ecosystems.”
  • Ivanov, Dmitry, Alexandre Dolgui, and Boris Sokolov. 2022. “Cloud Supply Chain: Integrating Industry 4.0 and Digital Platforms in the “Supply Chain-as-a-Service”.” Transportation Research Part E: Logistics and Transportation Review 160:102676. https://doi.org/10.1016/j.tre.2022.102676.
  • Ivanov, Dmitry, and Burcu B. Keskin. 2023. “Post-pandemic Adaptation and Development of Supply Chain Viability Theory.” Omega 116:102806. https://doi.org/10.1016/j.omega.2022.102806.
  • Jang, H. S., K. Y. Bae, H. Park, and D. K. Sung. 2016. “Solar Power Prediction Based on Satellite Images and Support Vector Machine.” IEEE Transactions on Sustainable Energy 7 (3): 1255–1263. https://doi.org/10.1109/TSTE.2016.2535466.
  • Jingyi, Chi, and Yeping Yin. 2021. “State Grid Vows to Ensure Power Demand for Livelihood Needs.” Global Times. Accessed May 13, 2023. https://www.globaltimes.cn/page/202109/1235270.shtml#:~:text=In%20response%20to%20the%20power,the%20best%20of%20their%20abilities.
  • Kettunen, J., E. Nematollahi, and Y. Zinchenko. 2022. “Why Do Energy Markets in Europe Rely on One Instrument?.” Production and Operations Management 31 (4): 1473–1491. https://doi.org/10.1111/poms.v31.4.
  • Khan, Faizan A., Nitai Pal, and Syed H. Saeed. 2018. “Review of Solar Photovoltaic and Wind Hybrid Energy Systems for Sizing Strategies Optimization Techniques and Cost Analysis Methodologies.” Renewable and Sustainable Energy Reviews 92:937–947. https://doi.org/10.1016/j.rser.2018.04.107.
  • Kohavi, R. 1995. “A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection.” Vol. 14. IJCAI.
  • Lara-Fanego, V., J. A. Ruiz-Arias, D. Pozo-Vázquez, F. J. Santos-Alamillos, and J. Tovar-Pescador. 2012. “Evaluation of the WRF Model Solar Irradiance Forecasts in Andalusia (southern Spain).” Solar Energy 86 (8): 2200–2217. https://doi.org/10.1016/j.solener.2011.02.014.
  • LaValle, S. M., M. S. Branicky, and S. R. Lindemann. 2004. “On the Relationship Between Classical Grid Search and Probabilistic Roadmaps.” The International Journal of Robotics Research 23 (7–8): 673–692. https://doi.org/10.1177/0278364904045481.
  • Lee, H. L., V. Padmanabhan, and S. Whang. 2004. “Information Distortion in a Supply Chain: The Bullwhip Effect.” Management Science 50 (12 supplement): 1875–1886. https://doi.org/10.1287/mnsc.1040.0266.
  • Li, Zhi, Hanyang Guo, Ali Vatankhah Barenji, Wai Ming Wang, Yijiang Guan, and George Q. Huang. 2020. “A Sustainable Production Capability Evaluation Mechanism Based on Blockchain, LSTM, Analytic Hierarchy Process for Supply Chain Network.” International Journal of Production Research58 (24): 7399–7419. https://doi.org/10.1080/00207543.2020.1740342.
  • Li, Hongcheng, Haidong Yang, Bixia Yang, Chengjiu Zhu, and Sihua Yin. 2018. “Modelling and Simulation of Energy Consumption of Ceramic Production Chains with Mixed Flows Using Hybrid Petri Nets.” International Journal of Production Research 56 (8): 3007–3024. https://doi.org/10.1080/00207543.2017.1391415.
  • Liu, Changchun, Haihua Zhu, Dunbing Tang, Qingwei Nie, Shipei Li, Yi Zhang, and Xuan Liu. 2022. “A Transfer Learning CNN-LSTM Network-based Production Progress Prediction Approach in IIoT-enabled Manufacturing.” International Journal of Production Research 1–24. https://doi.org/10.1080/00207543.2022.2138612.
  • Löhndorf, Nils, and David Wozabal. 2023. “The Value of Coordination in Multimarket Bidding of Grid Energy Storage.” Operations Research 71 (1): 1–22. https://doi.org/10.1287/opre.2021.2247.
  • Lorente, Daniel Balsalobre, Kamel Si Mohammed, Javier Cifuentes-Faura, and Umer Shahzad. 2023. “Dynamic Connectedness Among Climate Change Index, Green Financial Assets and Renewable Energy Markets: Novel Evidence From Sustainable Development Perspective.” Renewable Energy 204:94–105. https://doi.org/10.1016/j.renene.2022.12.085.
  • Marquez, R., and C. F. M. Coimbra. 2011. “Forecasting of Global and Direct Solar Irradiance Using Stochastic Learning Methods, Ground Experiments and the NWS Database.” Solar Energy 85 (5): 746–756. https://doi.org/10.1016/j.solener.2011.01.007.
  • Martilli, A., A. Clappier, and M. W. Rotach. 2002. “An Urban Surface Exchange Parameterisation for Mesoscale Models.” Boundary-Layer Meteorology 104 (2): 261–304. https://doi.org/10.1023/A:1016099921195.
  • Mercure, J.-F., Pablo Salas, Pim Vercoulen, Gregor Semieniuk, Aileen Lam, Hector Pollitt, Philip B. Holden. 2021. “Reframing Incentives for Climate Policy Action.” Nature Energy 6 (12): 1133–1143. https://doi.org/10.1038/s41560-021-00934-2.
  • National Grid ESO. 2019. “ESO and The Alan Turing Institute Use Machine Learning to Help Balance the GB Electricity Grid.” ESO. Accessed May 13, 2023.
  • Nishant, Rohit, Mike Kennedy, and Jacqueline Corbett. 2020. “Artificial Intelligence for Sustainability: Challenges, Opportunities, and a Research Agenda.” International Journal of Information Management53:102104. https://doi.org/10.1016/j.ijinfomgt.2020.102104.
  • Obersteiner, Michael, Christian Azar, S. Kossmeier, R. Mechler, K. Moellersten, S. Nilsson, P. Read, Y. Yamagata, and J. Yan. 2001. “Managing Climate Risk.”
  • Olver, P. J. 2014. Introduction to Partial Differential Equations. Berlin: Springer.
  • Ouyang, Jianjun, and Jie Fu. 2020. “Optimal Strategies of Improving Energy Efficiency for An Energy-intensive Manufacturer Considering Consumer Environmental Awareness.” International Journal of Production Research 58 (4): 1017–1033. https://doi.org/10.1080/00207543.2019.1607977.
  • Pal, R. 2017. Chapter 4 – Validation Methodologies. Amsterdam: Academic Press.
  • Palz, Wolfgang. 2010. Power for the World: The Emergence of Electricity from the Sun. Stanford, CA: Pan Stanford Publishing.
  • Pang, Z., F. Niu, and Z. O'Neill. 2020. “Solar Radiation Prediction Using Recurrent Neural Network and Artificial Neural Network: A Case Study with Comparisons.” Renewable Energy 156:279–289. https://doi.org/10.1016/j.renene.2020.04.042.
  • Parker, G. G., B. Tan, and O. Kazan. 2019. “Electric Power Industry: Operational and Public Policy Challenges and Opportunities.” Production and Operations Management 28 (11): 2738–2777. https://doi.org/10.1111/poms.v28.11.
  • Paulescu, Marius, and Eugenia Paulescu. 2019. “Short-term Forecasting of Solar Irradiance.” Renewable Energy 143:985–994. https://doi.org/10.1016/j.renene.2019.05.075.
  • Paulescu, M., E. Paulescu, and V. Badescu. 2021. “Chapter 9 – Nowcasting Solar Irradiance for Effective Solar Power Plants Operation and Smart Grid Management.” In Predictive Modelling for Energy Management and Power Systems Engineering, 249–270. Amsterdam: Elsevier.
  • Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel. 2011. “Scikit-learn: Machine Learning in Python.” Journal of Machine Learning Research 12:2825–2830.
  • Pedro, H. T. C., and C. F. M. Coimbra. 2012. “Assessment of Forecasting Techniques for Solar Power Production with No Exogenous Inputs.” Solar Energy 86 (7): 2017–2028. https://doi.org/10.1016/j.solener.2012.04.004.
  • Perez, R., P. Ineichen, R. Seals, J. Michalsky, and R. Stewart. 1990. “Modeling Daylight Availability and Irradiance Components From Direct and Global Irradiance.” Solar Energy 44 (5): 271–289. https://doi.org/10.1016/0038-092X(90)90055-H.
  • Perron, Pierre. 1988. “Trends and Random Walks in Macroeconomic Time Series: Further Evidence From a New Approach.” Journal of Economic Dynamics and Control 12 (2–3): 297–332. https://doi.org/10.1016/0165-1889(88)90043-7.
  • Pfenninger, S. 2017. “Dealing with Multiple Decades of Hourly Wind and PV Time Series in Energy Models: A Comparison of Methods to Reduce Time Resolution and the Planning Implications of Inter-annual Variability.” Applied Energy 197:1–13. https://doi.org/10.1016/j.apenergy.2017.03.051.
  • Powers, J. G., J. B. Klemp, W. C. Skamarock, C. A. Davis, J. Dudhia, D. O. Gill, J. L. Coen. 2017. “The Weather Research and Forecasting Model: Overview, System Efforts, and Future Directions.” Bulletin of the American Meteorological Society 98 (8): 1717–1737. https://doi.org/10.1175/BAMS-D-15-00308.1.
  • Prechelt, L. 2012. “Early Stopping – But When?.” Neural Networks: Tricks of the Trade: Second Edition53–67. https://doi.org/10.1007/978-3-642-35289-8.
  • Provost, Foster, and Tom Fawcett. 2013. Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking. Sebastopol, CA: O'Reilly Media, Inc.
  • Rai, Rahul, Manoj Kumar Tiwari, Dmitry Ivanov, and Alexandre Dolgui. 2021. “Machine Learning in Manufacturing and Industry 4.0 Applications.”
  • Ramos-Hernanz, J., I. Uriarte, J. M. Lopez-Guede, U. Fernandez-Gamiz, A. Mesanza, and E. Zulueta. 2020. “Temperature Based Maximum Power Point Tracking for Photovoltaic Modules.” Scientific Reports 10:12476. https://doi.org/10.1038/s41598-020-69365-5.
  • Rana, M., I. Koprinska, and V. G. Agelidis. 2015. “2D-interval Forecasts for Solar Power Production.” Solar Energy 122:191–203. https://doi.org/10.1016/j.solener.2015.08.018.
  • Rising, James, Marco Tedesco, Franziska Piontek, and David A. Stainforth. 2022. “The Missing Risks of Climate Change.” Nature 610 (7933): 643–651. https://doi.org/10.1038/s41586-022-05243-6.
  • Risken, H., and K. Voigtlaender. 1984. “Solutions of the Fokker–Planck Equation Describing the Thermalization of Neutrons in a Heavy Gas Moderator.” Zeitschrift für Physik B Condensed Matter54 (3): 253–262. https://doi.org/10.1007/BF01319191.
  • Salamanca, F., A. Krpo, A. Martilli, and A. Clappier. 2009. “A New Building Energy Model Coupled with An Urban Canopy Parameterization for Urban Climate Simulations – part I. Formulation, Verification, and Sensitivity Analysis of the Model.” Theoretical and Applied Climatology 99:331–344. https://doi.org/10.1007/s00704-009-0142-9.
  • Saloux, E., and J. A. Candanedo. 2018. “Forecasting District Heating Demand Using Machine Learning Algorithms.” Energy Procedia 149:59–68. https://doi.org/10.1016/j.egypro.2018.08.169.
  • Samuel, A. L. 1959. “Some Studies in Machine Learning Using the Game of Checkers.” IBM Journal of Research and Development 3 (3): 210–229. https://doi.org/10.1147/rd.33.0210.
  • Sharifzadeh, M., A. Sikinioti-Lock, and N. Shah. 2019. “Machine-learning Methods for Integrated Renewable Power Generation: A Comparative Study of Artificial Neural Networks, Support Vector Regression, and Gaussian Process Regression.” Renewable and Sustainable Energy Reviews 108:513–538. https://doi.org/10.1016/j.rser.2019.03.040.
  • Sobri, Sobrina, Sam Koohi-Kamali, and Nasrudin Abd. Rahim. 2018. “Solar Photovoltaic Generation Forecasting Methods: A Review.” Energy Conversion and Management 156:459–497. https://doi.org/10.1016/j.enconman.2017.11.019.
  • Stern, Nicholas, and Chris Taylor. 2007. “Climate Change: Risk, Ethics, and the Stern Review.” Science317 (5835): 203–204. https://doi.org/10.1126/science.1142920.
  • Sugg, Margaret M., Luke Wertis, Sophia C. Ryan, Shannon Green, Devyani Singh, and Jennifer D. Runkle. 2023. “Cascading Disasters and Mental Health: The February 2021 Winter Storm and Power Crisis in Texas, USA.” Science of the Total Environment 880:163231. https://doi.org/10.1016/j.scitotenv.2023.163231.
  • Toffler, A. 1980. The Third Wave. New York City, NY: Bantam Books.
  • Tolba, H., N. Dkhili, J. Nou, J. Eynard, S. Thil, and S. Grieu. 2020. “Multi-Horizon Forecasting of Global Horizontal Irradiance Using Online Gaussian Process Regression: A Kernel Study.” Energies 13 (16): 4184. https://doi.org/10.3390/en13164184.
  • Tollefson, Jeff. 2022. “What the War in Ukraine Means for Energy, Climate and Food.” Nature 604 (7905): 232–233. https://doi.org/10.1038/d41586-022-00969-9.
  • Tuo, Junbo, Fei Liu, and Peiji Liu. 2019. “Key Performance Indicators for Assessing Inherent Energy Performance of Machine Tools in Industries.” International Journal of Production Research 57 (6): 1811–1824. https://doi.org/10.1080/00207543.2018.1508904.
  • Umar, Muhammad, Yasir Riaz, and Imran Yousaf. 2022. “Impact of Russian-Ukraine War on Clean Energy, Conventional Energy, and Metal Markets: Evidence From Event Study Approach.” Resources Policy 79:102966. https://doi.org/10.1016/j.resourpol.2022.102966.
  • Van Kampen, Nicolaas Godfried. 1992. Stochastic Processes in Physics and Chemistry. Vol. 1. Amsterdam: Elsevier.
  • Wang, Seaver, Zeke Hausfather, Steven Davis, Juzel Lloyd, Erik B. Olson, Lauren Liebermann, Guido D. Núñez-Mujica, and Jameson McBride. 2023. “Future Demand for Electricity Generation Materials Under Different Climate Mitigation Scenarios.” Joule 7 (2): 309–332. https://doi.org/10.1016/j.joule.2023.01.001.
  • Wang, F., Z. Mi, S. Su, and H. Zhao. 2012. “Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters.” Energies 5 (5): 1355–1370. https://doi.org/10.3390/en5051355.