Journal Home > Volume 2 , Issue 2

The energy sector is enduring a momentous transformation with new technological advancements and increasing demand leading to innovative pathways. Artificial intelligence (AI) is emerging as a critical driver of the change, offering new ways to optimize energy systems operations, control, automation, etc. Developing a competitive policy framework aligned with circular economy practices to adapt to the trends of the rapid revolution is crucial, shaping the future of energy and leading the sector in a sustainable, equitable, and impartial direction. This study aims to propose an AI-driven policy framework that aligns with the circular economy business model to address the transformation trend in the development of energy policies through a multidisciplinary approach. The study identifies key trends, various approaches, and evaluates the potential of AI in addressing the challenges. The AI-driven policy paradigm outlines a comprehensive framework and roadmap to harness the potential of AI through a forward-looking policy framework that considers the rapidly changing landscape and the essence of the circular economy. The proposed novel framework provides a roadmap for researchers, governments, and other stakeholders to navigate the future of energy and unlock the potential of AI for a sustainable energy future.

Full text
About this article

Shaping the future of sustainable energy through AI-enabled circular economy policies

Show Author's information Mir Sayed Shah Danish( )Tomonobu Senjyu
Department of Electrical and Electronics Engineering, University of the Ryukyus, 1 Senbaru, Nishihara, 903-0213, Okinawa, Japan


The energy sector is enduring a momentous transformation with new technological advancements and increasing demand leading to innovative pathways. Artificial intelligence (AI) is emerging as a critical driver of the change, offering new ways to optimize energy systems operations, control, automation, etc. Developing a competitive policy framework aligned with circular economy practices to adapt to the trends of the rapid revolution is crucial, shaping the future of energy and leading the sector in a sustainable, equitable, and impartial direction. This study aims to propose an AI-driven policy framework that aligns with the circular economy business model to address the transformation trend in the development of energy policies through a multidisciplinary approach. The study identifies key trends, various approaches, and evaluates the potential of AI in addressing the challenges. The AI-driven policy paradigm outlines a comprehensive framework and roadmap to harness the potential of AI through a forward-looking policy framework that considers the rapidly changing landscape and the essence of the circular economy. The proposed novel framework provides a roadmap for researchers, governments, and other stakeholders to navigate the future of energy and unlock the potential of AI for a sustainable energy future.

Keywords: Energy policy, Circular economy, Decarbonization, AI-enabled energy policy, Policy tools


Ahmad, S., Tahar, R. M., Muhammad-Sukki, F., Munir, A. B., & Rahim, R. A. (2015). Role of feed-in tariff policy in promoting solar photovoltaic investments in Malaysia: A system dynamics approach. Energy, 84, 808-815.

Ahmad, T., Zhang, D. D., & Huang, C. (2021). Methodological framework for short-and medium-term energy, solar and wind power forecasting with stochastic-based machine learning approach to monetary and energy policy applications. Energy, 231, Article 120911.

Al-Masri, R. A., Chenoweth, J., & Murphy, R. J. (2019). Exploring the status quo of water-energy nexus policies and governance in Jordan. Environmental Science & Policy, 100, 192-204.

Anyoha, R. (2017). The history of artificial intelligence. Science in the News, 28.

Arcelay, I., Goti, A., Oyarbide-Zubillaga, A., Akyazi, T., Alberdi, E., & Garcia-Bringas, P. (2021). Definition of the future skills needs of job profiles in the renewable energy sector. Energies, 14, 2609.

Ardakanian, O., Wong, V. W. S., Dobbe, R., Low, S. H., von Meier, A., Tomlin, C. J., & Yuan, Y. (2019). On identification of distribution grids. IEEE Transactions on Control of Network Systems, 6, 950-960.

Bansal, M., Goyal, A., & Choudhary, A. (2022). A comparative analysis of k-nearest neighbor, genetic, support vector machine, decision tree, and long short term memory algorithms in machine learning. Decision Analytics Journal, 3, Article 100071.

Barlas, P., Lanning, I., & Heavey, C. (2015). A survey of open source data science tools. International Journal of Intelligent Computing and Cybernetics, 8, 232-261.

Beeck, V. N. (1999). Classification of Energy Models. Tilburg, Netherlands: Tilburg University.
Bellizio, F., Karagiannopoulos, S., Aristidou, P., & Hug, G. (2018). Optimized local control for active distribution grids using machine learning techniques. In: Proceedings of the 2018 IEEE Power & Energy Society General Meeting.
Birol, F. (2022). World Energy Outlook 2022 (Technical). Paris: International Energy Agency. Available at

Brahma, S., Kavasseri, R., Cao, H., Chaudhuri, N. R., Alexopoulos, T., & Cui, Y. (2017). Real-time identification of dynamic events in power systems using PMU data, and potential applications-Models, promises, and challenges. IEEE Transactions on Power Delivery, 32, 294-301.

Brunner, R. D. (2006). A paradigm for practice. Policy Sciences, 39, 135-167.

Busari, G. A., & Lim, D. H. (2021). Crude oil price prediction: A comparison between AdaBoost-LSTM and AdaBoost-GRU for improving forecasting performance. Computers & Chemical Engineering, 155, Article 107513.

Cao, K. Y., Xu, X. P., Wu, Q., & Zhang, Q. P. (2017). Optimal production and carbon emission reduction level under cap-and-trade and low carbon subsidy policies. Journal of Cleaner Production, 167, 505-513.

Castrillón-Mendoza, R., Rey-Hernández, J. M., & Rey-Martínez, F. J. (2020). Industrial decarbonization by a new energy-baseline methodology. Case study. Sustainability, 12, 1960.

Chan, Y. T. (2020). On the impacts of anticipated carbon policies: A dynamic stochastic general equilibrium model approach. Journal of Cleaner Production, 256, Article 120342.

Chawla, Y., Shimpo, F., & Sokolowski, M. M. (2022). Artificial intelligence and information management in the energy transition of India: Lessons from the global IT heart. Digital Policy, Regulation and Governance, 24, 17-29.

Chen, P. Y. (2019). On the diversity-based weighting method for risk assessment and decision-making about natural hazards. Entropy, 21, 269.

Danish, M. S. S., Elsayed, M. E. L., Ahmadi, M., Senjyu, T., Karimy, H., & Zaheb, H. (2020). A strategic-integrated approach for sustainable energy deployment. Energy Reports, 6, 40-44.

Danish, M. S. S., Senjyu, T., Funabashia, T., Ahmadi, M., Ibrahimi, A. M., Ohta, R., … Sediqi, M. M. (2019). A sustainable microgrid: A sustainability and management-oriented approach. Energy Procedia, 159, 160-167.

Danish, M. S. S., Senjyu, T., Ibrahimi, A. M., Ahmadi, M., Howlader, A. M. (2019). A managed framework for energy-efficient building. Journal of Building Engineering, 21, 120-128.

Danish, M. S. S., Senjyu, T., Zaheb, H., Sabory, N. R., Ibrahimi, A. M., & Matayoshi, H. (2019). A novel transdisciplinary paradigm for municipal solid waste to energy. Journal of Cleaner Production, 233, 880-892.

Danish, M., Yona, A., & Senjyu, T. (2015). A review of voltage stability assessment techniques with an improved voltage stability indicator. International Journal of Emerging Electric Power Systems, 16, 107-115.

Daradkeh, M., Abualigah, L., Atalla, S., & Mansoor, W. (2022). Scientometric analysis and classification of research using convolutional neural networks: A case study in data science and analytics. Electronics, 11, 2066.

de Oliveira Musse, J., Homrich, A. S., de Mello, R., & Carvalho, M. M. (2018). Applying backcasting and system dynamics towards sustainable development: The housing planning case for low-income citizens in Brazil. Journal of Cleaner Production, 193, 97-114.

Deka, D., Backhaus, S., & Chertkov, M. (2018). Structure learning in power distribution networks. IEEE Transactions on Control of Network Systems, 5, 1061-1074.

Dobbe, R., Hidalgo-Gonzalez, P., Karagiannopoulos, S., Henriquez-Auba, R., Hug, G., Callaway, D., & Tomlin, C. (2020). Learning to control in power systems: Design and analysis guidelines for concrete safety problems. Electric Power Systems Research, 189, Article 106615.

Dobbe, R., Sondermeijer, O., Fridovich-Keil, D., Arnold, D., Callaway, D., & Tomlin, C. (2020). Toward distributed energy services: Decentralizing optimal power flow with machine learning. IEEE Transactions on Smart Grid, 11, 1296-1306.

Duan, C., Jiang, L., Fang, W. L., & Liu, J. (2018). Data-driven affinely adjustable distributionally robust unit commitment. IEEE Transactions on Power Systems, 33, 1385-1398.

Entezari, A., Aslani, A., Zahedi, R., & Noorollahi, Y. (2023). Artificial intelligence and machine learning in energy systems: A bibliographic perspective. Energy Strategy Reviews, 45, Article 101017.

Ernst, D., Glavic, M., & Wehenkel, L. (2004). Power systems stability control: Reinforcement learning framework. IEEE Transactions on Power Systems, 19, 427-435.

Gabr, A. Z., Helal, A., & Abbasy, N. (2020). Economic evaluation of rooftop grid-connected photovoltaic systems for residential building in Egypt. International Transactions on Electrical Energy Systems, 30, Article e12379.

Gladysz, P., Strojny, M., Bartela, Ł., Hacaga, M., & Froehlich, T. (2022). Merging climate action with energy security through CCS-a multi-disciplinary framework for assessment. Energies, 16, 35.

Glavic, M., Fonteneau, R., & Ernst, D. (2017). Reinforcement learning for electric power system decision and control: Past considerations and perspectives. IFAC-PapersOnLine, 50, 6918-6927.

Gu, J. Q., Umar, M., Soran, S., & Yue, X. G. (2020). Exacerbating effect of energy prices on resource curse: Can research and development be a mitigating factor? Resources Policy, 67, Article 101689.

Guo, F. Z., Chen, Z. J., Xiao, F., Li, A., & Shi, J. (2023). Real-time energy performance benchmarking of electric vehicle air conditioning systems using adaptive neural network and Gaussian process regression. Applied Thermal Engineering, 222, Article 119931.

Hameed, K., Chai, D., & Rassau, A. (2021). Texture-based latent space disentanglement for enhancement of a training dataset for ANN-based classification of fruit and vegetables. Information Processing in Agriculture, 10, 85-105.

Hettiarachchi, H., & Kshourad, C. (2019). Promoting waste-to-energy: Nexus thinking, policy instruments, and implications for the environment. In S. Kumar, R. Kumar, & A. Pandey (Eds.), Current developments in biotechnology and bioengineering (pp. 163-184). Elsevier.

Hines, A., Schutte, J., & Romero, M. (2019). Transition scenarios via backcasting. Journal of Future Studies, 24, 1-14.

Hu, M. (2022). Response to another look at “2019 energy benchmarking data for LEED-certified buildings in Washington, D. C.: Simulation and reality”. Journal of Building Engineering, 46, Article 103694.

Huang, H., Nie, S. L., Lin, J., Wang, Y. Y., & Dong, J. (2020). Optimization of peer-to-peer power trading in a microgrid with distributed PV and battery energy storage systems. Sustainability, 12, 923.

Inoue, N., & Matsumoto, S. (2019). An examination of losses in energy savings after the Japanese Top Runner Program? Energy Policy, 124, 312-319.

International Energy Agency. (2021). Empowering cities for a net zero future: Unlocking resilient, smart, sustainable urban energy systems.

Jafari, H., Safarzadeh, S., & Azad-Farsani, E. (2022). Effects of governmental policies on energy-efficiency improvement of hydrogen fuel cell cars: A game-theoretic approach. Energy, 254, Article 124394.

Jiang, Y., & Zhao, C. H. (2022). Attention classification-and-segmentation network for micro-crack anomaly detection of photovoltaic module cells. Solar Energy, 238, 291-304.

Jokar, P., Arianpoo, N., & Leung, V. C. M. (2016). Electricity theft detection in AMI using customers’ consumption patterns. IEEE Transactions on Smart Grid, 7, 216-226.

Kalton, G., & Flores-Cervantes, I. (2003). Weighting methods. Journal of Official Statistics, 19, 81-97.

Karagiannopoulos, S., Aristidou, P., & Hug, G. (2019). Data-driven local control design for active distribution grids using off-line optimal power flow and machine learning techniques. IEEE Trans Smart Grid, 10, 6461-6471.

Karagiannopoulos, S., Dobbe, R., Aristidou, P., Callaway, D., & Hug, G. (2019). Datadriven control design schemes in active distribution grids: Capabilities and challenges. In Proceedings of the 2019 IEEE milan PowerTech (pp. 1-6).

Kaya, M. (2022). State-of-the-art lithium-ion battery recycling technologies. Circular Economy, 1, Article 100015.

Khajuria, A., Atienza, V. A., Chavanich, S., Henning, W., Islam, I., Kral, U.,, … Oyedotun, T. D. T., et al. (2022). Accelerating circular economy solutions to achieve the 2030 agenda for sustainable development goals. Circular Economy, 1, Article 100001.

Ko, Y. C., Fujita, H., & Li, T. R. (2017). An evidential analysis of Altman Z-score for financial predictions: Case study on solar energy companies. Applied Soft Computing, 52, 748-759.

Kok, M., & Lootsma, F. A. (1985). Pairwise-comparison methods in multiple objective programming, with applications in a long-term energy-planning model. European Journal of Operational Research, 22, 44-55.

Kosana, V., Teeparthi, K., & Madasthu, S. (2022). A novel and hybrid framework based on generative adversarial network and temporal convolutional approach for wind speed prediction. Sustainable Energy Technologies and Assessments, 53, Article 102467.

Kotu, V., & Deshpande, B. (2019). Introduction. In Data science (pp. 1-18). Elsevier.

Krarti, M. (2019). Evaluation of energy efficiency potential for the building sector in the Arab region. Energies, 12, 4279.

Kubassova, O., Shaikh, F., Melus, C., & Mahler, M. (2021). History, current status, and future directions of artificial intelligence. In M. Mahler (Ed.), Precision medicine and artificial intelligence (pp. 1-38). Amsterdam: Elsevier.

Lesage-Landry, A., & Taylor, J. A. (2018). Setpoint tracking with partially observed loads. IEEE Transactions on Power Systems, 33, 5615-5627.

Li, J. H., & Xu, G. C. (2022). Circular economy towards zero waste and decarbonization. Circular Economy, 1, Article 100002.

Laghari, J. A., Mokhlis, H., Bakar, A. H. A., & Mohamad, H. (2013). Application of computational intelligence techniques for load shedding in power systems: A review. Energy Conversion and Management, 75, 130-140.

Liao, Y. Z., Weng, Y., Liu, G. Y., & Rajagopal, R. (2019). Urban MV and LV distribution grid topology estimation via group lasso. IEEE Transactions on Power Systems, 34, 12-27.

Lin, W. C., Tsai, C. F., & Chen, H. (2022). Factors affecting text mining based stock prediction: Text feature representations, machine learning models, and news platforms. Applied Soft Computing, 130, Article 109673.

Lowhorn, G. L. (2007). Qualitative and quantitative research: How to choose the best design. In Academic business world international conference.

Lu, R. Z., & Hong, S. H. (2019). Incentive-based demand response for smart grid with reinforcement learning and deep neural network. Applied Energy, 236, 937-949.

Majeed, A., Ahmad, M., Rasheed, M. F., Khan, M. K., Popp, J., & Oláh, J. (2022). The dynamic impact of financial globalization, environmental innovations and energy productivity on renewable energy consumption: Evidence from advanced panel techniques. Frontiers in Environmental Science, 10.

Middleton, P. (2018). Sustainable living education: Techniques to help advance the renewable energy transformation. Solar Energy, 174, 1016-1018.

Mormann, F. (2021). Who is going to pay for and benefit from the expansion of solar power? The National Interest. Available at

Mulholland, E., Rogan, F., & Ó Gallachóir, B. P. (2017). From technology pathways to policy roadmaps to enabling measures-A multi-model approach. Energy, 138, 1030-1041.

Neshat, N., Amin-Naseri, M., & Danesh, F. (2014). Energy models: Methods and characteristics. Journal of Energy in South Africa, 25: 101-111.

New York Power Authority. (2021). Sustainability plan 2021-2025: Advancing the ESG foundational pillar of vision 2030. Available at
Novirdoust, A., Bichler, M., Bojung, C., Buhl, H. U., Fridgen, G., Gretschko, V.,, … Neuhoff, K., et al. (2021). Electricity spot market design 2030-2050.

Odagiri, H., Nakamura, Y., & Shibuya, M. (1997). Research consortia as a vehicle for basic research: The case of a fifth generation computer project in Japan. Research Policy, 26, 191-207.

Omitaomu, O., & Niu, H. R. (2021). Artificial intelligence techniques in smart grid: A survey. Smart Cities, 4, 548-568.

Orman, T. F. (2016). “Paradigm” as a central concept in Thomas Kuhn’s thought. International Journal of Humanities and Social Science, 6, 47-52.

Pai, P. F., & Chen, T. C. (2009). Rough set theory with discriminant analysis in analyzing electricity loads. Expert Systems with Applications, 36, 8799-8806.

Pandey, N., de Coninck, H., & Sagar, A. (2022). Beyond technology transfer: Innovation cooperation to advance sustainable development in developing countries. Wiley Interdisciplinary Reviews: Energy & Environment, 11, e422.

Park, C. H., Kim, E. H., Jung, D. H., Chung, H., Park, J. C., Shin, S. K., … Lee, Y. C. (2016). The new modified ABCD method for gastric neoplasm screening. Gastric Cancer, 19, 128-135.

Pavitt, K. (1972). Analytical techniques in government science policy. Futures, 4, 5-12.

Project Management Institute. (2021). The project management and a guide to the project management body of knowledge (7th ed.).

Pujahari, A., & Sisodia, D. S. (2020). Pair-wise preference relation based probabilistic matrix factorization for collaborative filtering in recommender system. Knowledge-Based Systems, 196, Article 105798.

Pyka, A., Cardellini, G., van Meijl, H., & Verkerk, P. J. (2022). Modelling the bioeconomy: Emerging approaches to address policy needs. Journal of Cleaner Production, 330, Article 129801.

Rajendran, D. P. D., & Sundarraj, R. (2021). Using topic models with browsing history in hybrid collaborative filtering recommender system: Experiments with user ratings. International Journal of Information Management Data Insights, 1, Article 100027.

Raza, M. A., Khatri, K. L., Ul Haque, M. I., Shahid, M., Rafique, K., & Waseer, T. A. (2022). Holistic and scientific approach to the development of sustainable energy policy framework for energy security in Pakistan. Energy Reports, 8, 4282-4302.

Redfoot, E. K., Verner, K. M., & Borrelli, R. A. (2022). Applying analytic hierarchy process to industrial process design in a Nuclear Renewable Hybrid Energy System. Progress in Nuclear Energy, 145, Article 104083.

Rizos, V., & Bryhn, J. (2022). Implementation of circular economy approaches in the electrical and electronic equipment (EEE) sector: Barriers, enablers and policy insights. Journal of Cleaner Production, 338, Article 130617.

Rosati, F., & Faria, L. G. D. (2019). Addressing the SDGs in sustainability reports: The relationship with institutional factors. Journal of Cleaner Production, 215, 1312-1326.

Rose, K. H. (2017). A guide to the project management body of knowledge (6th ed.). Pennsylvania, USA: Project Management Institute.

Runge, J., & Zmeureanu, R. (2019). Forecasting energy use in buildings using artificial neural networks: A review. Energies, 12, 3254.

Sabory, N. R., Senjyu, T., Momand, A. H., Waqfi, H., Saboor, N., Mobarez, R., & Razeqi, F. (2021). LEED scores of residential buildings in poor cities: Kabul city case. Sustainability, 13, 6959.

Safarzadeh, S., & Rasti-Barzoki, M. (2019). A game theoretic approach for pricing policies in a duopolistic supply chain considering energy productivity, industrial rebound effect, and government policies. Energy, 167, 92-105.

Sala, S., Ciuffo, B., & Nijkamp, P. (2015). A systemic framework for sustainability assessment. Ecological Economics, 119, 314-325.

Shams, S., Danish, M. S. S., & Sabory, N. R. (2021). Solar energy market and policy instrument analysis to support sustainable development. In M. S. S. Danish, T. Senjyu, & N. R. Sabory (Eds.), Sustainability outreach in developing countries (pp. 113-132). Singapore: Springer.

Shi, Z., Zhu, J., & Wei, H. N. (2022). SARSA-based delay-aware route selection for SDN-enabled wireless-PLC power distribution IoT. Alexandria Engineering Journal, 61, 5795-5803.

Singh, G. G., Cottrell, R. S., Eddy, T. D., & Cisneros-Montemayor, A. M. (2021). Governing the land-sea interface to achieve sustainable coastal development. Frontiers in Marine Science, 8, Article 709947.

Sitepu, M. H., McKay, A., & Holt, R. J. (2019). An approach for the formulation of sustainable replanting policies in the Indonesian natural rubber industry. Journal of Cleaner Production, 241, Article 118357.

Soria-Lara, J. A., & Banister, D. (2018). Collaborative backcasting for transport policy scenario building. Futures, 95, 11-21.

Sotiriou, C., & Zachariadis, T. (2021). A multi-objective optimisation approach to explore decarbonisation pathways in a dynamic policy context. Journal of Cleaner Production, 319, Article 128623.

Stefano, S., & Michèle, L. (2022). Deep learning and artificial neural networks for spacecraft dynamics, navigation and control. Drones, 6, 270.

Steinwandter, V., Borchert, D., & Herwig, C. (2019). Data science tools and applications on the way to Pharma 4.0. Drug Discovery Today, 24, 1795-1805.

Sulaimany, S., & Mafakheri, A. (2023). Visibility graph analysis of web server log files. Physica A: Statistical Mechanics and its Applications, 611: Article 128448.

Sun, M. Y., Cremer, J., & Strbac, G. (2018). A novel data-driven scenario generation framework for transmission expansion planning with high renewable energy penetration. Applied Energy, 228, 546-555.

Suzuki, M., Kanie, N., & Iguchi, M. (2016). New approaches for transitions to low fossil carbon societies: Promoting opportunities for effective development, diffusion and implementation of technologies, policies and strategies. Journal of Cleaner Production, 128, 1-5.

Tarmanini, C., Sarma, N., Gezegin, C., & Ozgonenel, O. (2022). Short term load forecasting based on ARIMA and ANN approaches. Energy Reports, 9, 550-557.

Tebenkov, E., & Prokhorov, I. (2021). Machine learning algorithms for teaching AI chat bots. Procedia Computer Science, 190, 735-744.

Tolentino-Zondervan, F., Bogers, E., van de Sande, L. (2021). A managerial and behavioral approach in aligning stakeholder goals in sustainable last mile logistics: A case study in the Netherlands. Sustainability, 13, 4434.

Tong, W., Mu, D., Zhao, F., Mendis, G. P., & Sutherland, J. W. (2019). The impact of cap-and-trade mechanism and consumers’ environmental preferences on a retailer-led supply Chain. Resources, Conservation and Recycling, 142, 88-100.

Tryggestad, C., Sharma, N., Rolser, O., Smeets, B., Wilthaner, M., Staaij, J. van de, Gruenewald, T., Noffsinger, J., & Tiemersma, L. (2022). Global energy perspective 2022: Executive summary. New York: McKinsey & Company. Available at /media/McKinsey/Industries/Oil and Gas/Our Insights/Global Energy Perspective 2022/Global-Energy-Perspective-2022-ExecutiveSummary.pdf.
Turing, A. M. (2009). Computing machinery and intelligence. In R. Epstein, G. Roberts, & G. Beber (Eds.), Parsing the turing test: Philosophical and methodological Issues in the Quest for the thinking computer (pp. 23-65). Dordrecht: Springer Netherlands.

Valle-Cruz, D., Criado, J. I., Sandoval-Almazán, R., & Ruvalcaba-Gomez, E. A. (2020). Assessing the public policy-cycle framework in the age of artificial intelligen From agenda-setting to policy evaluation. Government Information Quarterly, 37, Article 101509.

Van Schoubroeck, S., Springael, J., Van Dael, M., Malina, R., & Van Passel, S. (2019). Sustainability indicators for biobased chemicals: A Delphi study using Multi-Criteria Decision Analysis. Resources, Conservation and Recycling, 144, 198-208.

Velvizhi, G., Shanthakumar, S., Das, B., Pugazhendhi, A., Priya, T. S., Ashok, B., … Karthick, C. (2020). Biodegradable and non-biodegradable fraction of municipal solid waste for multifaceted applications through a closed loop integrated refinery platform: Paving a path towards circular economy. The Science of the Total Environment, 731, Article 138049.

Wei, C. C., Zhang, C. G., Vardar, O., Watson, J., & Canbulat, I. (2022). Quantitative assessment of energy changes in underground coal excavations using numerical approach. Geohazard Mechanics, 1, 58-68.

Westreich, D., Lessler, J., & Funk, M. J. (2010). Propensity score estimation: Neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression. Journal of Clinical Epidemiology, 63, 826-833.

Wohlfarth, K., Worrell, E., & Eichhammer, W. (2020). Energy efficiency and demand response-two sides of the same coin? Energy Policy, 137, Article 111070.

Xiong, R., Cao, J. Y., & Yu, Q. Q. (2018). Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle. Applied Energy, 211, 538-548.

Yadranjiaghdam, B., Pool, N., & Tabrizi, N. (2016). A survey on real-time big data analytics: Applications and tools. In Proceedings of the 2016 international conference on computational science and computational intelligence (pp. 404-409).

Yang, X. L., He, L. Y., Xia, Y. F., & Chen, Y. F. (2019). Effect of government subsidies on renewable energy investments: The threshold effect. Energy Policy, 132, 156-166.

Yang, Y. Y., Sun, X. L., Zhu, X. Q., & Xie, Y. J. (2013). Scenario simulation and policy analysis on energy development in Qinghai Province. Procedia Computer Science, 17, 720-728.

York, L., Heffernan, C., & Rymer, C. (2018). A systematic review of policy approaches to dairy sector greenhouse gas (GHG) emission reduction. Journal of Cleaner Production, 172, 2216-2224.

Zeng, L. T., Qiu, D. W., & Sun, M. Y. (2022). Resilience enhancement of multi-agent reinforcement learning-based demand response against adversarial attacks. Applied Energy, 324, Article 119688.

Zeng, X. L., Ogunseitan, O. A., Nakamura, S., Suh, S., Kral, U., Li, J. H., & Geng, Y. (2022). Reshaping global policies for circular economy. Circ Econ, 1, Article 100003.

Zhang, M. M., Tang, Y. M., Liu, L. Y., & Zhou, D. Q. (2022). Optimal investment portfolio strategies for power enterprises under multi-policy scenarios of renewable energy. Renewable and Sustainable Energy Reviews, 154, Article 111879.

Zhao, C. Y., & Guan, Y. P. (2016). Data-driven stochastic unit commitment for integrating wind generation. IEEE Transactions on Power Systems, 31, 2587-2596.

Zhao, X. G., Wang, W., & Wang, J. Y. (2022). The policy effects of demand-pull and technology-push on the diffusion of wind power: A scenario analysis based on system dynamics approach. Energy, 261, Article 125224.

Zhu, X., Liao, B. Y., & Yang, S. L. (2021). An optimal incentive policy for residential prosumers in Chinese distributed photovoltaic market: A Stackelberg game approach. Journal of Cleaner Production, 308, Article 127325.

Zienkiewicz, A. K., Ladu, F., Barton, D. A. W., Porfiri, M., & Bernardo, M. D. (2018). Data-driven modelling of social forces and collective behaviour in zebrafish. Journal of Theoretical Biology, 443, 39-51.

Publication history
Rights and permissions

Publication history

Received: 25 February 2023
Revised: 07 April 2023
Accepted: 14 April 2023
Published: 09 May 2023
Issue date: June 2023


© 2023 The Author(s).



We would like to sincerely express our gratitude to all those who contributed to completing this article. Your unwavering support, insightful comments, and invaluable guidance, especially during critical moments, have played an instrumental role in shaping this work. We are deeply grateful for your contributions and appreciate your willingness to join this journey.

Rights and permissions

This is an open access article under the CC BY-NC-ND license (