Abstract
This study introduces a novel techno-economic framework integrating machine learning (ML) into energy systems to enhance their operational efficiency and reliability. With the increasing complexity and dynamic nature of modern energy grids, there is a pressing need for innovative solutions that ensure stability and adaptability. Our proposed framework leverages advanced ML algorithms to improve grid management, ranging from demand forecasting and renewable energy integration to real-time optimization and reliability assessment. Through a comprehensive analysis, we demonstrate the effectiveness of ML in accurately predicting energy patterns, optimizing resource allocation, and managing the grid in response to fluctuating demands. The results reveal that ML not only increases the precision of energy system models but also drives substantial improvements in both economic and environmental performance. The iterative development and validation process outlined confirms the potential of ML to transform energy systems into more responsive, efficient, and robust networks. As energy providers seek sustainable and cost-effective solutions, this framework marks a significant step toward a smarter energy future.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ackermann, T., Andersson, G., Söder, L.: Distributed generation: a definition. Electr. Power Syst. Res. 57, 195–204 (2001). https://doi.org/10.1016/S0378-7796(01)00101-8
Danish, M.S.S., Nazari, Z., Senjyu, T.: AI-coherent data-driven forecasting model for a combined cycle power plant. Energy Convers. Manag. 286, 117063 (2023). https://doi.org/10.1016/j.enconman.2023.117063
Al-Najideen, M.I., Alrwashdeh, S.S.: Design of a solar photovoltaic system to cover the electricity demand for the faculty of engineering- Mu’tah University in Jordan. Resour.-Effic. Technol. 3, 440–445 (2017). https://doi.org/10.1016/j.reffit.2017.04.005
Furukakoi, M., Sediqi, M.M., Senjyu, T., Danish, M.S.S., Howlader, A.M., Hassan, M.A.M., Funabashi, T.: Optimum capacity of energy storage system considering solar radiation forecast error and demand response. In: 2017 IEEE 3rd International Future Energy Electronics Conference and ECCE Asia (IFEEC 2017 – ECCE Asia), pp. 997–1001. IEEE, Kaohsiung (2017). https://doi.org/10.1109/IFEEC.2017.7992177
Danish, M.S.S., Senjyu, T.S.: Green building efficiency and sustainability indicators. In: Green Building Management and Smart Automation, pp. 128–145. IGI Global, Hershey (2020). https://doi.org/10.4018/978-1-5225-9754-4.ch006
Danish, M.S.S., Zaheb, H., Sabory, N.R., Tomonobu, S., Ahmadi, M., Sadat, S.H.: Empowering Develo** Nations and Sustainable Development: Case Studies and Synthesis. REPA – Research and Education Promotion Association, Japan (2020)
Danish, M.S.S., Senjyu, T.S.: System of green resilience eco-oriented land uses in urban socio-ecosystems. In: Eco-Friendly Energy Processes and Technologies for Achieving Sustainable Development, pp. 1–23. IGI Global, Hershey (2021). https://doi.org/10.4018/978-1-7998-4915-5
Ahmadi, M., Adewuyi, O.B., Danish, M.S.S., Mandal, P., Yona, A., Senjyu, T.: Optimum coordination of centralized and distributed renewable power generation incorporating battery storage system into the electric distribution network. Int. J. Electr. Power Energy Syst. 125, 106458 (2021). https://doi.org/10.1016/j.ijepes.2020.106458
Danish, M.S.S.: A framework for modeling and optimization of data-driven energy systems using machine learning. IEEE Trans. Artif. Intell. 1–10 (2023). https://doi.org/10.1109/TAI.2023.3322395
Danish, M.S.S.: AI and expert insights for sustainable energy future. Energies. 16, 3309 (2023). https://doi.org/10.3390/en16083309
Danish, M.S.S., Bhattacharya, A., Stepanova, D., Mikhaylov, A., Grilli, M.L., Khosravy, M., Senjyu, T.: A systematic review of metal oxide applications for energy and environmental sustainability. Metals. 10, 1604 (2020). https://doi.org/10.3390/met10121604
Danish, M.S.S., Senjyu, T.: AI-enabled energy policy for a sustainable future. Sustain. For. 15, 7643 (2023). https://doi.org/10.3390/su15097643
Danish, M.S.S., Elsayed, M.E.L., Ahmadi, M., Senjyu, T., Karimy, H., Zaheb, H.: A strategic-integrated approach for sustainable energy deployment. Energy Rep. 6, 40–44 (2020). https://doi.org/10.1016/j.egyr.2019.11.039
Danish, M.S.S., Senjyu, T.: Sha** the future of sustainable energy through AI-enabled circular economy policies. Circ. Econ. 2, 100040 (2023). https://doi.org/10.1016/j.cec.2023.100040
Danish, M.S.S., Senjyu, T., Danish, S.M.S., Sabory, N.R., Narayanan, K., Mandal, P.: A recap of voltage stability indices in the past three decades. Energies. 12, 1544 (2019). https://doi.org/10.3390/en12081544
Danish, M.S.S., Senjyu, T., Faisal, N., Stannikzai, M.Z., Nazari, A.M., Vargas-Hernández, J.G.: A review on environmental-friendly energy multidisciplinary exposition from goals to action. J. Environ. Sci. Revolut. 2, 1–9 (2021). https://doi.org/10.37357/1068/jesr.2.1.01
Danish, M.S.S., Senjyu, T., Funabashia, T., Ahmadi, M., Ibrahimi, A.M., Ohta, R., Rashid Howlader, H.O., Zaheb, H., Sabory, N.R., Sediqi, M.M.: A sustainable microgrid: a sustainability and management-oriented approach. Energy Procedia. 159, 160–167 (2019). https://doi.org/10.1016/j.egypro.2018.12.045
Danish, M.S.S., Senjyu, T., Ibrahimi, A.M., Ahmadi, M., Howlader, A.M.: A managed framework for energy-efficient building. J. Build. Eng. 21, 120–128 (2019). https://doi.org/10.1016/j.jobe.2018.10.013
Danish, M.S.S., Senjyu, T., Ibrahimi, A.M., Bhattacharya, A., Nazari, Z., Danish, S.M.S., Ahmadi, M.: Sustaining energy systems using metal oxide composites as photocatalysts. J. Sustain. Energy Revolut. 2(6–15), 6 (2021). https://doi.org/10.37357/1068/jser.2.1.02
Danish, M.S.S., Senjyu, T., Nazari, M., Zaheb, H., Nassor, T.S., Danish, S.M.S., Karimy, H.: Smart and sustainable building appraisal. J. Sustain. Energy Revolut. 2, 1–5 (2021). https://doi.org/10.37357/1068/jser.2.1.01
Danish, M.S.S., Senjyu, T., Zaheb, H., Sabory, N.R., Ibrahimi, A.M., Matayoshi, H.: A novel transdisciplinary paradigm for municipal solid waste to energy. J. Clean. Prod. 233, 880–892 (2019). https://doi.org/10.1016/j.jclepro.2019.05.402
Danish, M.S.S., Yona, A., Senjyu, T.: Pre-design and life cycle cost analysis of a hybrid power system for rural and remote communities in Afghanistan. J. Eng. IET. 2014, 438–444 (2014). https://doi.org/10.1049/joe.2014.0172
Furukakoi, M., Danish, M.S.S., Howlader, A.M., Senjyu, T.: Voltage stability improvement of transmission systems using a novel shunt capacitor control. Int. J. Emerg. Electr. Power Syst. 19, 1–12 (2018). https://doi.org/10.1515/ijeeps-2017-0112
Ibrahimi, A.M., Howlader, H.O.R., Danish, M.S.S., Shigenobu, R., Sediqi, M.M., Senjyu, T.: Optimal unit commitment with concentrated solar power and thermal energy storage in Afghanistan electrical system. Int. J. Emerg. Electr. Power Syst. 20 (2019). https://doi.org/10.1515/ijeeps-2018-0264
Ibrahimi, A.M., Sediqi, M.M., Howlader, H.O.R., Danish, M.S.S., Chakraborty, S., Senjyu, T.: Generation expansion planning considering renewable energy integration and optimal unit commitment: a case study of Afghanistan. AIMS Energy. 7, 441–464 (2019). https://doi.org/10.3934/energy.2019.4.441
Danish, M.S.S., Senjyu, T., Ahmadi, M., Ludin, G.A., Ahadi, M.H., Karimy, H., Khosravy, M.: A review on energy efficiency for pathetic environmental trends mitigation. J. Sustain. Outreach. 2, 1–8 (2021). https://doi.org/10.37357/1068/jso.2.1.01
Kräuchi, P., Dahinden, C., Jurt, D., Wouters, V., Menti, U.-P., Steiger, O.: Electricity consumption of building automation. Energy Procedia. 122, 295–300 (2017). https://doi.org/10.1016/j.egypro.2017.07.325
Karagiannopoulos, S., Dobbe, R., Aristidou, P., Callaway, D., Hug, G.: Data-driven control design schemes in active distribution grids: capabilities and challenges. In: 2019 IEEE Milan PowerTech, pp. 1–6 (2019). https://doi.org/10.1109/PTC.2019.8810586
Hernández-Callejo, L., Gallardo-Saavedra, S., Alonso-Gómez, V.: A review of photovoltaic systems: design, operation and maintenance. Sol. Energy. 188, 426–440 (2019). https://doi.org/10.1016/j.solener.2019.06.017
Furukakoi, M., Adewuyi, O.B., Danish, M.S.S., Howlader, A.M., Senjyu, T., Funabashi, T.: Critical boundary index (CBI) based on active and reactive power deviations. Int. J. Electr. Power Energy Syst. 100, 50–57 (2018). https://doi.org/10.1016/j.ijepes.2018.02.010
Ahmed, R., Sreeram, V., Mishra, Y., Arif, M.D.: A review and evaluation of the state-of-the-art in PV solar power forecasting: techniques and optimization. Renew. Sust. Energ. Rev. 124, 109792 (2020). https://doi.org/10.1016/j.rser.2020.109792
Shams, S., Danish, M.S.S., Sabory, N.R.: Solar energy market and policy instrument analysis to support sustainable development. In: Danish, M.S.S., Senjyu, T., Sabory, N.R. (eds.) Sustainability Outreach in Develo** Countries, pp. 113–132. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-7179-4_8
Sward, J.A., Siff, J., Gu, J., Zhang, K.M.: Strategic planning for utility-scale solar photovoltaic development – historical peak events revisited. Appl. Energy. 250, 1292–1301 (2019). https://doi.org/10.1016/j.apenergy.2019.04.178
Nazim, S.F., Danish, M.S.S., Senjyu, T.: A brief review of the future of smart mobility using 5G and IoT. J. Sustain. Outreach. 19–30, 10.37357/1068/jso/3.1.02 (2022)
Brenna, M., Falvo, M.C., Foiadelli, F., Martirano, L., Poli, D.: Sustainable energy microsystem (SEM): preliminary energy analysis. In: 2012 IEEE PES Innovative Smart Grid Technologies (ISGT), pp. 1–6. IEEE, Washington (2012). https://doi.org/10.1109/ISGT.2012.6175735
Berizzi, A., Finazzi, P.: First and second order methods for voltage collapse assessment and security enhancement. IEEE Trans. Power Syst. 13, 543–551 (1998). https://doi.org/10.1109/59.667380
Gao, B., Morison, G.K., Kundur, P.: Voltage stability evaluation using modal analysis. IEEE Trans. Power Syst. 7, 1529–1542 (1992). https://doi.org/10.1109/59.207377
Danish, M.S.S.: AI in energy: overcoming unforeseen obstacles. AI. 4, 406–425 (2023). https://doi.org/10.3390/ai4020022
Abdolrasol, M.G.M., Hussain, S.M.S., Ustun, T.S., Sarker, M.R., Hannan, M.A., Mohamed, R., Ali, J.A., Mekhilef, S., Milad, A.: Artificial neural networks based optimization techniques: a review. Electronics. 10, 2689 (2021). https://doi.org/10.3390/electronics10212689
Danish, M.S.S., Sabory, N.R., Ibrahimi, A.M., Senjyu, T., Ahadi, M.H., Stanikzai, M.Z.: A concise overview of energy development within sustainability requirements. In: Danish, M.S.S., Senjyu, T., Sabory, N.R. (eds.) Sustainability Outreach in Develo** Countries, pp. 15–27. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-7179-4_2
Alpaydin, E.: Introduction to Machine Learning. The MIT Press, Cambridge (2009)
Gutierrez-Corea, F.-V., Manso-Callejo, M.-A., Moreno-Regidor, M.-P., Manrique-Sancho, M.-T.: Forecasting short-term solar irradiance based on artificial neural networks and data from neighboring meteorological stations. Sol. Energy. 134, 119–131 (2016). https://doi.org/10.1016/j.solener.2016.04.020
Sufizada, Z., Oryakheill, A.A., Kohnaward, M.H., Fazli, N., Zadran, H., Sabory, N.R., Danish, M.S.S.: From consumers to producers: energy efficiency as a tool for sustainable development in the context of informal settlements. In: Danish, M.S.S., Senjyu, T., Sabory, N.R. (eds.) Sustainability Outreach in Develo** Countries, pp. 169–187. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-7179-4_11
Senjyu, T., Takara, H., Uezato, K., Funabashi, T.: One-hour-ahead load forecasting using neural network. IEEE Trans. Power Syst. 17, 113–118 (2002). https://doi.org/10.1109/59.982201
Bhattacharyya, S.C.: Energy Economics: Concepts, Issues, Markets and Governance. Springer, London (2019)
Brahma, S., Kavasseri, R., Cao, H., Chaudhuri, N.R., Alexopoulos, T., Cui, Y.: Real-time identification of dynamic events in power systems using PMU data, and potential applications – models, promises, and challenges. IEEE Trans. Power Deliv. 32, 294–301 (2017). https://doi.org/10.1109/TPWRD.2016.2590961
Khalid, H.M., Flitti, F., Mahmoud, M.S., Hamdan, M.M., Muyeen, S.M., Dong, Z.Y.: Wide area monitoring system operations in modern power grids: a median regression function-based state estimation approach towards cyber attacks. Sustain. Energy Grids Netw. 34, 101009 (2023). https://doi.org/10.1016/j.segan.2023.101009
Danish, M.S.S., Senjyu, T. (eds.): Eco-Friendly and Agile Energy Strategies and Policy Development. IGI Global, Hershey (2022)
Danish, M.S.S.: Voltage Stability in Electric Power System: a Practical Introduction. Logos Verlag Berlin GmbH, Berlin (2015)
Blackouts, P.W.: A three-stage procedure for controlled islanding to prevent wide-area blackouts. Energies. 11, 1–15 (2018). https://doi.org/10.3390/en11113066
Danish, M.S.S., Yona, A., Senjyu, T.: A review of voltage stability assessment techniques with an improved voltage stability indicator. Int. J. Emerg. Electr. Power Syst. 16, 107–115 (2015). https://doi.org/10.1515/ijeeps-2014-0167
Danish, S.M.S., Ahmadi, M., Danish, M.S.S., Mandal, P., Yona, A., Senjyu, T.: A coherent strategy for peak load shaving using energy storage systems. J. Energy Storage. 32, 101823 (2020). https://doi.org/10.1016/j.est.2020.101823
Ahmadi, M., Danish, M.S.S., Lotfy, M.E., Yona, A., Hong, Y.-Y., Senjyu, T.: Multi-objective time-variant optimum automatic and fixed type of capacitor bank allocation considering minimization of switching steps. AIMS Energy. 7, 792 (2019). https://doi.org/10.3934/energy.2019.6.792
Danish, S.M.S., Shigenobu, R., Kinjo, M., Mandal, P., Krishna, N., Hemeida, A.M., Senjyu, T.: A real distribution network voltage regulation incorporating auto-tap-changer pole transformer multiobjective optimization. Appl. Sci. 9, 2813 (2019). https://doi.org/10.3390/app9142813
Danish, M.S.S., Sabory, N.R., Funabashi, T., Danish, S.M.S., Noorzad, A.S., Yona, A., Senjyu, T.: Comparative analysis of load flow calculation methods with considering the voltage stability constraints. In: 2016 IEEE International Conference on Power and Energy (PECon), pp. 250–255. IEEE, Melaka (2016). https://doi.org/10.1109/PECON.2016.7951568
Yang, H., Wen, F., Wang, L.: Newton-Raphson on power flow algorithm and Broyden method in the distribution system. In: 2008 IEEE 2nd International Power and Energy Conference, pp. 1613–1618 (2008), https://doi.org/10.1109/PECON.2008.4762737
Sagara, M., Shigenobu, R., Adewuyi, O.B., Yona, A., Senjyu, T., Danish, M.S.S., Funabashi, T.: Voltage stability improvement by demand response. In: TENCON 2017–2017 IEEE Region 10 Conference, pp. 2144–2149. IEEE, Penang (2017). https://doi.org/10.1109/TENCON.2017.8228215
Glavic, M., Fonteneau, R., Ernst, D.: Reinforcement learning for electric power system decision and control: past considerations and perspectives. IFAC-Pap. 50, 6918–6927 (2017). https://doi.org/10.1016/j.ifacol.2017.08.1217
Razmjoo, A.A., Sumper, A., Davarpanah, A.: Energy sustainability analysis based on SDGs for develo** countries. Energy Sources Part Recovery Util. Environ. Eff. 42, 1041–1056 (2020). https://doi.org/10.1080/15567036.2019.1602215
Agajie, T.F., Ali, A., Fopah-Lele, A., Amoussou, I., Khan, B., Velasco, C.L.R., Tanyi, E.: A comprehensive review on techno-economic analysis and optimal sizing of hybrid renewable energy sources with energy storage systems. Energies. 16, 642 (2023). https://doi.org/10.3390/en16020642
Mirbarati, S.H., Heidari, N., Nikoofard, A., Danish, M.S.S., Khosravy, M.: Techno-economic-environmental energy Management of a Micro-Grid: a mixed-integer linear programming approach. Sustain. For. 14, 15036 (2022). https://doi.org/10.3390/su142215036
Lu, R., Hong, S.H.: Incentive-based demand response for smart grid with reinforcement learning and deep neural network. Appl. Energy. 236, 937–949 (2019). https://doi.org/10.1016/j.apenergy.2018.12.061
Danish, M.S.S.: Exploring metal oxides for hydrogen evolution reaction (HER) in the field of nanotechnology. RSC Sustain. 1, 2180 (2023). https://doi.org/10.1039/D3SU00179B
Gorjian, S., Calise, F., Kant, K., Ahamed, M.S., Copertaro, B., Najafi, G., Zhang, X., Aghaei, M., Shamshiri, R.R.: A review on opportunities for implementation of solar energy technologies in agricultural greenhouses. J. Clean. Prod. 285, 124807 (2021). https://doi.org/10.1016/j.jclepro.2020.124807
Waas, T., Hugé, J., Block, T., Wright, T., Benitez-Capistros, F., Verbruggen, A.: Sustainability assessment and indicators: tools in a decision-making strategy for sustainable development. Sustain. For. 6, 5512–5534 (2014). https://doi.org/10.3390/su6095512
Adalı, Z., Danish, M.S.S.: Investigation of the nexus between the electricity consumption and the ecological footprint. In: Dinçer, H., Yüksel, S. (eds.) Circular Economy and the Energy Market: Achieving Sustainable Economic Development Through Energy Policy, pp. 79–89. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-031-13146-2_7
Ohno, T., Imai, S.: The 1987 Tokyo blackout. In: 2006 IEEE PES Power Systems Conference and Exposition, pp. 314–318. IEEE (2006). https://doi.org/10.1109/PSCE.2006.296325
Nimpitiwan, N., Heydt, G.T., Ayyanar, R., Suryanarayanan, S.: Fault current contribution from synchronous machine and inverter based distributed generators. IEEE Trans. Power Deliv. 22, 634–641 (2007). https://doi.org/10.1109/TPWRD.2006.881440
Castrillón-Mendoza, R., Rey-Hernández, J.M., Rey-MartÃnez, F.J.: Industrial decarbonization by a new energy-baseline methodology. Case Study. Sustain. 12, 1960 (2020). https://doi.org/10.3390/su12051960
Danish, M.S.S., Senjyu, T., Sabory, N.R., Danish, S.M.S., Ludin, G.A., Noorzad, A.S., Yona, A.: Afghanistan’s aspirations for energy independence: water resources and hydropower energy. Renew. Energy. 113, 1276–1287 (2017). https://doi.org/10.1016/j.renene.2017.06.090
Driesen, J., Katiraei, F.: Design for distributed energy resources. IEEE Power Energy Mag. 6, 30–40 (2008). https://doi.org/10.1109/MPE.2008.918703
Kumar, R., Ojha, K., Ahmadi, M.H., Raj, R., Aliehyaei, M., Ahmadi, A., Nabipour, N.: A review status on alternative arrangements of power generation energy resources and reserve in India. Int. J. Low-Carbon Technol. 15, 224–240 (2020). https://doi.org/10.1093/ijlct/ctz066
Duan, C., Jiang, L., Fang, W., Liu, J.: Data-driven Affinely adjustable distributionally robust unit commitment. IEEE Trans. Power Syst. 33, 1385–1398 (2018). https://doi.org/10.1109/TPWRS.2017.2741506
Tong, W., Mu, D., Zhao, F., Mendis, G.P., Sutherland, J.W.: The impact of cap-and-trade mechanism and consumers’ environmental preferences on a retailer-led supply chain. Resour. Conserv. Recycl. 142, 88–100 (2019). https://doi.org/10.1016/j.resconrec.2018.11.005
Ahmadi, M., Lotfy, M.E., Howlader, A.M., Yona, A., Senjyu, T.: Centralised multi-objective integration of wind farm and battery energy storage system in real-distribution network considering environmental, technical and economic perspective. Transm. Distrib. IET Gener. 13, 5207–5217 (2019). https://doi.org/10.1049/iet-gtd.2018.6749
El-Moursi, M.S., Sharaf, A.M.: Novel controllers for the 48-pulse VSC STATCOM and SSSC for voltage regulation and reactive power compensation. IEEE Trans. Power Syst. 20, 1985–1997 (2005). https://doi.org/10.1109/TPWRS.2005.856996
Kurita, A., Sakuraj, T.: The power system failure on July 23, 1987 in Tokyo. In: Proceedings of the 27th IEEE Conference on Decision and Control, pp. 2093–2097. IEEE, Austin (1988). https://doi.org/10.1109/CDC.1988.194703
Aboagye, B., Gyamfi, S., Ofosu, E.A., Djordjevic, S.: Investigation into the impacts of design, installation, operation and maintenance issues on performance and degradation of installed solar photovoltaic (PV) systems. Energy Sustain. Dev. 66, 165–176 (2022). https://doi.org/10.1016/j.esd.2021.12.003
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning: Adaptive Computation and Machine Learning Series. The MIT Press, Cambridge (2016)
Ahmadi, M., Lotfy, M.E., Shigenobu, R., Howlader, A.M., Senjyu, T.: Optimal sizing of multiple renewable energy resources and PV inverter reactive power control encompassing environmental, technical, and economic issues. IEEE Syst. J. 13, 3026–3037 (2019). https://doi.org/10.1109/JSYST.2019.2918185
Fulginei, F.R., Salvini, A., Parodi, M.: Learning optimization of neural networks used for MIMO applications based on multivariate functions decomposition. Inverse Probl. Sci. Eng. 20, 29–39 (2012). https://doi.org/10.1080/17415977.2011.629047
Runge, J., Zmeureanu, R.: Forecasting energy use in buildings using artificial neural networks: a review. Energies. 12, 3254 (2019). https://doi.org/10.3390/en12173254
Kessel, P., Glavitsch, H.: Estimating the voltage stability of a power system. IEEE Trans. Power Deliv. 1, 346–354 (1986). https://doi.org/10.1109/TPWRD.1986.4308013
Wang, J.-J., **g, Y.-Y., Zhang, C.-F., Zhao, J.-H.: Review on multi-criteria decision analysis aid in sustainable energy decision-making. Renew. Sust. Energ. Rev. 13, 2263–2278 (2009). https://doi.org/10.1016/j.rser.2009.06.021
Cheng, C., Liu, B., Chau, K.-W., Li, G., Liao, S.: China’s small hydropower and its dispatching management. Renew. Sust. Energ. Rev. 42, 43–55 (2015). https://doi.org/10.1016/j.rser.2014.09.044
Berizzi, A., Marannino, P., Merlo, M., Pozzi, M., Zanellini, F.: Steady-state and dynamic approaches for the evaluation of loadability margins in the presence of secondary voltage regulation. IEEE Trans. Power Syst. 19, 1048–1057 (2004). https://doi.org/10.1109/TPWRS.2004.825869
Bourdeau, M., Zhai, X., qiang Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: a review of data-driven techniques. Sustain. Cities Soc. 48, 101533 (2019). https://doi.org/10.1016/j.scs.2019.101533
Adli, H.K., Husin, K.A.K., Hanafiah, N.H.M., Remli, M.A., Ernawan, F., Wirawan, P.W.: Forecasting and analysis of solar power output from integrated solar energy and IoT system. In: 2021 5th International Conference on Informatics and Computational Sciences (ICICoS), pp. 222–226. IEEE, Semarang (2021). https://doi.org/10.1109/ICICoS53627.2021.9651831
Wu, D., Wang, Y., Li, L., Lu, P., Liu, S., Dai, C., Pan, Y., Zhang, Z., Lin, Z., Yang, L.: Demand response ability evaluation based on seasonal and trend decomposition using LOESS and S–G filtering algorithms. Energy Rep. 8, 292–299 (2022). https://doi.org/10.1016/j.egyr.2022.02.139
Danish, M.S.S., Matayoshi, H., Howlader, H.O.R., Chakraborty, S., Mandal, P., Senjyu, T.: Microgrid planning and design: resilience to sustainability. In: 2019 IEEE PES GTD Grand International Conference and Exposition Asia (GTD Asia), pp. 253–258. IEEE, Bangkok (2019). https://doi.org/10.1109/GTDAsia.2019.8716010
Susowake, Y., Ibrahimi, A.M., Danish, M.S.S., Senjyu, T., Howlader, A.M., Mandal, P.: Multi-objective design of power system introducing seawater electrolysis plant for remote Island. In: 2018 IEEE Innovative Smart Grid Technologies – Asia (ISGT Asia), pp. 909–911. IEEE, Singapore (2018). https://doi.org/10.1109/ISGT-Asia.2018.8467912
Sabory, N.R., Senjyu, T., Danish, M.S.S., Ahmadi, M., Zaheb, H., Halim, M.: A framework for integration of smart and sustainable energy systems in urban planning processes of low-income develo** countries: Afghanistan case. Sustain. For. 13, 8428 (2021). https://doi.org/10.3390/su13158428
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ahadi, M.H. (2024). Integrating Machine Learning into Energy Systems: A Techno-economic Framework for Enhancing Grid Efficiency and Reliability. In: Danish, M.S.S. (eds) Unified Vision for a Sustainable Future. CEGS 2024. Springer, Cham. https://doi.org/10.1007/978-3-031-53574-1_4
Download citation
DOI: https://doi.org/10.1007/978-3-031-53574-1_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-53573-4
Online ISBN: 978-3-031-53574-1
eBook Packages: EnergyEnergy (R0)