{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T03:21:13Z","timestamp":1787800873130,"version":"build-2784847793"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100010822","name":"Chengdu Science and Technology Bureau","doi-asserted-by":"publisher","award":["No. 2023-JB00-00002-SN"],"award-info":[{"award-number":["No. 2023-JB00-00002-SN"]}],"id":[{"id":"10.13039\/501100010822","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010822","name":"Chengdu Science and Technology Bureau","doi-asserted-by":"publisher","award":["No. 2023-JB00-00002-SN"],"award-info":[{"award-number":["No. 2023-JB00-00002-SN"]}],"id":[{"id":"10.13039\/501100010822","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010822","name":"Chengdu Science and Technology Bureau","doi-asserted-by":"publisher","award":["No. 2023-JB00-00002-SN"],"award-info":[{"award-number":["No. 2023-JB00-00002-SN"]}],"id":[{"id":"10.13039\/501100010822","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010822","name":"Chengdu Science and Technology Bureau","doi-asserted-by":"publisher","award":["No. 2023-JB00-00002-SN"],"award-info":[{"award-number":["No. 2023-JB00-00002-SN"]}],"id":[{"id":"10.13039\/501100010822","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Membr Comput"],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s41965-024-00138-z","type":"journal-article","created":{"date-parts":[[2024,3,6]],"date-time":"2024-03-06T13:02:57Z","timestamp":1709730177000},"page":"16-28","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Medium-long-term electricity load forecasting based on NSNP systems and attention mechanism"],"prefix":"10.1007","volume":"6","author":[{"given":"Lin","family":"Guo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunzhu","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,3,6]]},"reference":[{"issue":"10","key":"138_CR1","doi-asserted-by":"publisher","first-page":"2050008","DOI":"10.1142\/S0129065720500082","volume":"30","author":"H Peng","year":"2020","unstructured":"Peng, H., Lv, Z., Li, B., Luo, X., Wang, J., Song, X., Wang, T., P\u00e9rez-Jim\u00e9nez, M. J., & Riscos-N\u00fa\u00f1ez, A. (2020). Nonlinear spiking neural p systems. International Journal of Neural Systems, 30(10), 2050008.","journal-title":"International Journal of Neural Systems"},{"key":"138_CR2","doi-asserted-by":"publisher","first-page":"142239","DOI":"10.1109\/ACCESS.2021.3120731","volume":"9","author":"A Azeem","year":"2021","unstructured":"Azeem, A., Ismail, I., Jameel, S. M., & Harindran, V. R. (2021). Electrical load forecasting models for different generation modalities: A review. IEEE Access, 9, 142239\u2013142263.","journal-title":"IEEE Access"},{"key":"138_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2019.109408","volume":"203","author":"H Dagdougui","year":"2019","unstructured":"Dagdougui, H., Bagheri, F., Le, H., & Dessaint, L. (2019). Neural network model for short-term and very-short-term load forecasting in district buildings. Energy and Buildings, 203, 109408.","journal-title":"Energy and Buildings"},{"key":"138_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2021.121492","volume":"236","author":"W Zhang","year":"2021","unstructured":"Zhang, W., Chen, Q., Yan, J., Zhang, S., & Xu, J. (2021). A novel asynchronous deep reinforcement learning model with adaptive early forecasting method and reward incentive mechanism for short-term load forecasting. Energy, 236, 121492.","journal-title":"Energy"},{"key":"138_CR5","unstructured":"Tamura, Y., Zhang, D., Umeda, N., & Sakashita, K. (1992). Load forecasting using grey dynamic model. Journal of Grey System 4(1)."},{"key":"138_CR6","doi-asserted-by":"crossref","unstructured":"Olabode, O., Amole, O., Ajewole, T., & Okakwu, I. (2020). Medium-term load forecasting in a nigerian electricity distribution region using regression analysis techniques. In 2020 International Conference in Mathematics, Computer Engineering and Computer Science (ICMCECS), (pp. 1\u20135).","DOI":"10.1109\/ICMCECS47690.2020.240907"},{"issue":"1","key":"138_CR7","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1016\/j.apenergy.2009.04.024","volume":"87","author":"M AlRashidi","year":"2010","unstructured":"AlRashidi, M., & El-Naggar, K. (2010). Long term electric load forecasting based on particle swarm optimization. Applied Energy, 87(1), 320\u2013326.","journal-title":"Applied Energy"},{"key":"138_CR8","doi-asserted-by":"publisher","first-page":"776","DOI":"10.1016\/j.energy.2017.12.049","volume":"144","author":"EM Oliveira","year":"2018","unstructured":"Oliveira, E. M., & Oliveira, F. L. C. (2018). Forecasting mid-long term electric energy consumption through bagging arima and exponential smoothing methods. Energy, 144, 776\u2013788.","journal-title":"Energy"},{"issue":"16","key":"138_CR9","doi-asserted-by":"publisher","first-page":"6539","DOI":"10.3390\/su12166539","volume":"12","author":"D-H Kim","year":"2020","unstructured":"Kim, D.-H., Lee, E.-K., & Qureshi, N. B. S. (2020). Peak-load forecasting for small industries: A machine learning approach. Sustainability, 12(16), 6539.","journal-title":"Sustainability"},{"key":"138_CR10","doi-asserted-by":"publisher","first-page":"1144","DOI":"10.1016\/j.energy.2018.10.073","volume":"167","author":"L Tang","year":"2019","unstructured":"Tang, L., Wang, X., Wang, X., Shao, C., Liu, S., & Tian, S. (2019). Long-term electricity consumption forecasting based on expert prediction and fuzzy bayesian theory. Energy, 167, 1144\u20131154.","journal-title":"Energy"},{"issue":"5","key":"138_CR11","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1016\/S0167-6911(82)80025-X","volume":"1","author":"D Ju-Long","year":"1982","unstructured":"Ju-Long, D. (1982). Control problems of grey systems. Systems & Control Letters, 1(5), 288\u2013294.","journal-title":"Systems & Control Letters"},{"issue":"2","key":"138_CR12","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1002\/eej.4391150202","volume":"115","author":"H Morita","year":"1995","unstructured":"Morita, H., Zhang, D.-P., & Tamura, Y. (1995). Long-term load forecasting using grey system theory. Electrical Engineering in Japan, 115(2), 11\u201320.","journal-title":"Electrical Engineering in Japan"},{"key":"138_CR13","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.sepro.2011.11.012","volume":"3","author":"J Kang","year":"2012","unstructured":"Kang, J., & Zhao, H. (2012). Application of improved grey model in long-term load forecasting of power engineering. Systems Engineering Procedia, 3, 85\u201391.","journal-title":"Systems Engineering Procedia"},{"key":"138_CR14","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.epsr.2015.09.001","volume":"130","author":"G Dudek","year":"2016","unstructured":"Dudek, G. (2016). Pattern-based local linear regression models for short-term load forecasting. Electric power systems research, 130, 139\u2013147.","journal-title":"Electric power systems research"},{"issue":"1","key":"138_CR15","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1109\/TPWRS.2006.889130","volume":"22","author":"N Amjady","year":"2007","unstructured":"Amjady, N. (2007). Short-term bus load forecasting of power systems by a new hybrid method. IEEE Transactions on Power Systems, 22(1), 333\u2013341.","journal-title":"IEEE Transactions on Power Systems"},{"issue":"5","key":"138_CR16","doi-asserted-by":"publisher","first-page":"2165","DOI":"10.1016\/j.enconman.2010.10.047","volume":"52","author":"M Shafie-Khah","year":"2011","unstructured":"Shafie-Khah, M., Moghaddam, M. P., & Sheikh-El-Eslami, M. (2011). Price forecasting of day-ahead electricity markets using a hybrid forecast method. Energy Conversion and Management, 52(5), 2165\u20132169.","journal-title":"Energy Conversion and Management"},{"key":"138_CR17","unstructured":"Da-Hai, Z., Shi-Fang, J., Yan-Qiu, B.I., & Gui-Bin, Z. (2003). Study of power system load forecast based on wavelet neural networks. Electric Power Automation Equipment."},{"key":"138_CR18","doi-asserted-by":"publisher","first-page":"886","DOI":"10.1016\/j.energy.2019.03.010","volume":"174","author":"M Barman","year":"2019","unstructured":"Barman, M., & Choudhury, N. B. D. (2019). Season specific approach for short-term load forecasting based on hybrid fa-svm and similarity concept. Energy, 174, 886\u2013896.","journal-title":"Energy"},{"issue":"1","key":"138_CR19","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1109\/TPWRS.2010.2048585","volume":"26","author":"M Hanmandlu","year":"2010","unstructured":"Hanmandlu, M., & Chauhan, B. K. (2010). Load forecasting using hybrid models. IEEE Transactions on Power Systems, 26(1), 20\u201329.","journal-title":"IEEE Transactions on Power Systems"},{"key":"138_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2021.120480","volume":"227","author":"M Imani","year":"2021","unstructured":"Imani, M. (2021). Electrical load-temperature cnn for residential load forecasting. Energy, 227, 120480.","journal-title":"Energy"},{"key":"138_CR21","doi-asserted-by":"crossref","unstructured":"Zheng, J., Xu, C., Zhang, Z., & Li, X. (2017). Electric load forecasting in smart grids using long-short-term-memory based recurrent neural network. In 2017 51st Annual Conference on Information Sciences and Systems (CISS), (pp. 1\u20136).","DOI":"10.1109\/CISS.2017.7926112"},{"key":"138_CR22","doi-asserted-by":"publisher","first-page":"1352","DOI":"10.1016\/j.rser.2015.04.065","volume":"50","author":"MQ Raza","year":"2015","unstructured":"Raza, M. Q., & Khosravi, A. (2015). A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renewable and Sustainable Energy Reviews, 50, 1352\u20131372.","journal-title":"Renewable and Sustainable Energy Reviews"},{"key":"138_CR23","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.segan.2016.02.005","volume":"6","author":"E Mocanu","year":"2016","unstructured":"Mocanu, E., Nguyen, P. H., Gibescu, M., & Kling, W. L. (2016). Deep learning for estimating building energy consumption. Sustainable Energy, Grids and Networks, 6, 91\u201399.","journal-title":"Sustainable Energy, Grids and Networks"},{"key":"138_CR24","doi-asserted-by":"publisher","first-page":"115945","DOI":"10.1109\/ACCESS.2022.3218374","volume":"10","author":"M Liu","year":"2022","unstructured":"Liu, M., Qin, H., Cao, R., & Deng, S. (2022). Short-term load forecasting based on improved tcn and densenet. IEEE Access, 10, 115945\u2013115957.","journal-title":"IEEE Access"},{"key":"138_CR25","doi-asserted-by":"publisher","first-page":"4045","DOI":"10.1109\/ACCESS.2018.2888978","volume":"7","author":"L Han","year":"2018","unstructured":"Han, L., Peng, Y., Li, Y., Yong, B., Zhou, Q., & Shu, L. (2018). Enhanced deep networks for short-term and medium-term load forecasting. IEEE Access, 7, 4045\u20134055.","journal-title":"IEEE Access"},{"key":"138_CR26","doi-asserted-by":"publisher","first-page":"1008","DOI":"10.1016\/j.energy.2018.07.084","volume":"160","author":"T Ahmad","year":"2018","unstructured":"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, 1008\u20131020.","journal-title":"Energy"},{"key":"138_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2019.114145","volume":"259","author":"X Wang","year":"2020","unstructured":"Wang, X., & Ahn, S.-H. (2020). Real-time prediction and anomaly detection of electrical load in a residential community. Applied Energy, 259, 114145.","journal-title":"Applied Energy"},{"issue":"1","key":"138_CR28","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1002\/2475-8876.12135","volume":"3","author":"B Nepal","year":"2020","unstructured":"Nepal, B., Yamaha, M., Yokoe, A., & Yamaji, T. (2020). Electricity load forecasting using clustering and arima model for energy management in buildings. Japan Architectural Review, 3(1), 62\u201376.","journal-title":"Japan Architectural Review"},{"key":"138_CR29","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.energy.2017.07.150","volume":"139","author":"Y Liu","year":"2017","unstructured":"Liu, Y., Wang, W., & Ghadimi, N. (2017). Electricity load forecasting by an improved forecast engine for building level consumers. Energy, 139, 18\u201330.","journal-title":"Energy"},{"issue":"5","key":"138_CR30","doi-asserted-by":"publisher","first-page":"1399","DOI":"10.3390\/s20051399","volume":"20","author":"ZA Khan","year":"2020","unstructured":"Khan, Z. A., Hussain, T., Ullah, A., Rho, S., Lee, M., & Baik, S. W. (2020). Towards efficient electricity forecasting in residential and commercial buildings: A novel hybrid cnn with a lstm-ae based framework. Sensors, 20(5), 1399.","journal-title":"Sensors"},{"issue":"8","key":"138_CR31","doi-asserted-by":"publisher","first-page":"6473","DOI":"10.1109\/TIE.2019.2939988","volume":"67","author":"AO Hoori","year":"2019","unstructured":"Hoori, A. O., Al Kazzaz, A., Khimani, R., Motai, Y., & Aved, A. J. (2019). Electric load forecasting model using a multicolumn deep neural networks. IEEE Transactions on Industrial Electronics, 67(8), 6473\u20136482.","journal-title":"IEEE Transactions on Industrial Electronics"},{"key":"138_CR32","doi-asserted-by":"publisher","first-page":"1010","DOI":"10.1016\/j.apenergy.2019.01.127","volume":"238","author":"Y Yang","year":"2019","unstructured":"Yang, Y., Che, J., Deng, C., & Li, L. (2019). Sequential grid approach based support vector regression for short-term electric load forecasting. Applied Energy, 238, 1010\u20131021.","journal-title":"Applied Energy"},{"key":"138_CR33","unstructured":"Ionescu, M., P\u0103un, G., & Yokomori, T. (2006). Spiking neural p systems. Fundamenta informaticae 71(2-3), 279\u2013308."},{"key":"138_CR34","unstructured":"Hu, Y., Dong, J., Zhang, G., Wu, Y., Rong, H., & Zhu, M. (2024). Cancer gene selection with adaptive optimization spiking neural p systems and hybrid classifiers. Journal of Membrane Computing, 1\u201314."},{"issue":"4","key":"138_CR35","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1007\/s41965-022-00113-6","volume":"4","author":"H Zhang","year":"2022","unstructured":"Zhang, H., Liu, X., & Shao, Y. (2022). Chinese dialect tone\u2019s recognition using gated spiking neural p systems. Journal of Membrane Computing, 4(4), 284\u2013292.","journal-title":"Journal of Membrane Computing"},{"key":"138_CR36","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1007\/s41965-021-00087-x","volume":"3","author":"S Mi","year":"2021","unstructured":"Mi, S., Zhang, L., Peng, H., & Wang, J. (2021). Medical image fusion based on dtnp systems and laplacian pyramid. Journal of Membrane Computing, 3, 284\u2013295.","journal-title":"Journal of Membrane Computing"},{"issue":"1","key":"138_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s41965-022-00109-2","volume":"5","author":"W Yu","year":"2023","unstructured":"Yu, W., Wu, J., Chen, Y., & Wu, Y. (2023). Fuzzy tissue-like p systems with promoters and their application in power coordinated control of microgrid. Journal of Membrane Computing, 5(1), 1\u201311.","journal-title":"Journal of Membrane Computing"},{"issue":"1","key":"138_CR38","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/s41965-023-00118-9","volume":"5","author":"W Yu","year":"2023","unstructured":"Yu, W., Xiao, X., Wu, J., Chen, F., Zheng, L., & Zhang, H. (2023). Application of fuzzy spiking neural dp systems in energy coordinated control of multi-microgrid. Journal of Membrane Computing, 5(1), 69\u201380.","journal-title":"Journal of Membrane Computing"},{"key":"138_CR39","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1007\/s41965-020-00035-1","volume":"3","author":"Y Huang","year":"2021","unstructured":"Huang, Y., Wang, T., Wang, J., & Peng, H. (2021). Reliability evaluation of distribution network based on fuzzy spiking neural p system with self-synapse. Journal of Membrane Computing, 3, 51\u201362.","journal-title":"Journal of Membrane Computing"},{"key":"138_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107656","volume":"235","author":"Q Liu","year":"2022","unstructured":"Liu, Q., Long, L., Yang, Q., Peng, H., Wang, J., & Luo, X. (2022). Lstm-snp: A long short-term memory model inspired from spiking neural p systems. Knowledge-Based Systems, 235, 107656.","journal-title":"Knowledge-Based Systems"},{"key":"138_CR41","doi-asserted-by":"crossref","unstructured":"Dehalwar, V., Kalam, A., Kolhe, M.L., & Zayegh, A. (2016) Electricity load forecasting for urban area using weather forecast information. In 2016 IEEE International Conference on Power and Renewable Energy (ICPRE), (pp. 355\u2013359).","DOI":"10.1109\/ICPRE.2016.7871231"},{"key":"138_CR42","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.epsr.2016.08.031","volume":"142","author":"KG Boroojeni","year":"2017","unstructured":"Boroojeni, K. G., Amini, M. H., Bahrami, S., Iyengar, S., Sarwat, A. I., & Karabasoglu, O. (2017). A novel multi-time-scale modeling for electric power demand forecasting: From short-term to medium-term horizon. Electric Power Systems Research, 142, 58\u201373.","journal-title":"Electric Power Systems Research"},{"key":"138_CR43","doi-asserted-by":"crossref","unstructured":"Wilms, H., Cupelli, M., & Monti, A. (2018). Combining auto-regression with exogenous variables in sequence-to-sequence recurrent neural networks for short-term load forecasting. In 2018 IEEE 16th International Conference on Industrial Informatics (INDIN), (pp. 673\u2013679).","DOI":"10.1109\/INDIN.2018.8471953"}],"container-title":["Journal of Membrane Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41965-024-00138-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41965-024-00138-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41965-024-00138-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,9]],"date-time":"2024-04-09T13:15:54Z","timestamp":1712668554000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41965-024-00138-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3]]},"references-count":43,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["138"],"URL":"https:\/\/doi.org\/10.1007\/s41965-024-00138-z","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-3719302\/v1","asserted-by":"object"}]},"ISSN":["2523-8906","2523-8914"],"issn-type":[{"value":"2523-8906","type":"print"},{"value":"2523-8914","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3]]},"assertion":[{"value":"7 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 February 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 March 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors disclosed no relevant relationships. The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}