{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T22:40:27Z","timestamp":1760740827287,"version":"build-2065373602"},"publisher-location":"Singapore","reference-count":20,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819534616"},{"type":"electronic","value":"9789819534623"}],"license":[{"start":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T00:00:00Z","timestamp":1760659200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T00:00:00Z","timestamp":1760659200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-3462-3_22","type":"book-chapter","created":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T17:20:23Z","timestamp":1760635223000},"page":"265-272","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Spatio-Temporal Data Generation for\u00a0Power Grid Scenarios Based on\u00a0Conditional Diffusion Models"],"prefix":"10.1007","author":[{"given":"Mingtao","family":"You","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaodong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kedong","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huibiao","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Ren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,17]]},"reference":[{"issue":"2","key":"22_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3543511","volume":"17","author":"H Chen","year":"2023","unstructured":"Chen, H., Rossi, R.A., Mahadik, K., Kim, S., Eldardiry, H.: Graph deep factors for probabilistic time-series forecasting. ACM Trans. Knowl. Discov. Data 17(2), 1\u201330 (2023)","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"22_CR2","unstructured":"Coletta, A., Gopalakrishnan, S., Borrajo, D., Vyetrenko, S.: On the constrained time-series generation problem. In: Advances in Neural Information Processing Systems, vol.\u00a036, pp. 61048\u201361059 (2023)"},{"key":"22_CR3","unstructured":"Dhariwal, P., Nichol, A.Q.: Diffusion models beat gans on image synthesis. In: Advances in Neural Information Processing Systems, vol.\u00a034, pp. 8780\u20138794 (2021)"},{"issue":"2","key":"22_CR4","doi-asserted-by":"publisher","first-page":"1074","DOI":"10.1109\/TSTE.2023.3327497","volume":"15","author":"X Dong","year":"2024","unstructured":"Dong, X., Mao, Z., Sun, Y., Xu, X.: Short-term wind power scenario generation based on conditional latent diffusion models. IEEE Trans. Sustain. Energy 15(2), 1074\u20131085 (2024)","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"22_CR5","doi-asserted-by":"crossref","unstructured":"Feng, S., Miao, C., Zhang, Z., Zhao, P.: Latent diffusion transformer for probabilistic time series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a038, pp. 11979\u201311987 (2024)","DOI":"10.1609\/aaai.v38i11.29085"},{"key":"22_CR6","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of wasserstein gans. In: Advances in Neural Information Processing Systems, vol.\u00a030 (2017)"},{"key":"22_CR7","doi-asserted-by":"crossref","unstructured":"He, H., Zhang, Q., Bai, S., Yi, K., Niu, Z.: CATN: cross attentive tree-aware network for multivariate time series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a036, pp. 4030\u20134038 (2022)","DOI":"10.1609\/aaai.v36i4.20320"},{"key":"22_CR8","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Advances in Neural Information Processing Systems, vol.\u00a033, pp. 6840\u20136851 (2020)"},{"key":"22_CR9","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: International Conference on Learning Representations (2014)"},{"key":"22_CR10","doi-asserted-by":"crossref","unstructured":"Li, R., Li, X., Gao, S., Choy, S.T.B., Gao, J.: Graph convolution recurrent denoising diffusion model for multivariate probabilistic temporal forecasting. In: International Conference on Advanced Data Mining and Applications, vol. 14176, pp. 661\u2013676 (2023)","DOI":"10.1007\/978-3-031-46661-8_44"},{"issue":"1","key":"22_CR11","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1631\/FITEE.2300310","volume":"25","author":"L Lin","year":"2024","unstructured":"Lin, L., Li, Z., Li, R., Li, X., Gao, J.: Diffusion models for time series applications: a survey. Front. Inf. Technol. Electron. Eng. 25(1), 19\u201341 (2024)","journal-title":"Front. Inf. Technol. Electron. Eng."},{"key":"22_CR12","doi-asserted-by":"crossref","unstructured":"Liu, M., Huang, H., Feng, H., Sun, L., Du, B., Fu, Y.: Pristi: a conditional diffusion framework for spatiotemporal imputation. In: 2023 IEEE 39th International Conference on Data Engineering (ICDE), pp. 1927\u20131939 (2023)","DOI":"10.1109\/ICDE55515.2023.00150"},{"key":"22_CR13","unstructured":"Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)"},{"key":"22_CR14","unstructured":"Rasul, K., Seward, C., Schuster, I., Vollgraf, R.: Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting. In: Proceedings of the 38th International Conference on Machine Learning, vol.\u00a0139, pp. 8857\u20138868 (2021)"},{"key":"22_CR15","unstructured":"Shen, L., Kwok, J.T.: Non-autoregressive conditional diffusion models for time series prediction. In: International Conference on Machine Learning, vol.\u00a0202, pp. 31016\u201331029 (2023)"},{"issue":"4","key":"22_CR16","doi-asserted-by":"publisher","first-page":"4259","DOI":"10.1109\/TSG.2024.3366212","volume":"15","author":"Z Wang","year":"2024","unstructured":"Wang, Z., Zhang, H.: Customized load profiles synthesis for electricity customers based on conditional diffusion models. IEEE Trans. Smart Grid 15(4), 4259\u20134270 (2024)","journal-title":"IEEE Trans. Smart Grid"},{"issue":"2","key":"22_CR17","doi-asserted-by":"publisher","first-page":"1777","DOI":"10.1109\/TPWRS.2024.3449032","volume":"40","author":"Z Wang","year":"2025","unstructured":"Wang, Z., Wen, Q., Zhang, C., Sun, L., Wang, Y.: Diffload: uncertainty quantification in electrical load forecasting with the diffusion model. IEEE Trans. Power Syst. 40(2), 1777\u20131789 (2025)","journal-title":"IEEE Trans. Power Syst."},{"key":"22_CR18","doi-asserted-by":"crossref","unstructured":"Wen, H., et al.: Diffstg: probabilistic spatio-temporal graph forecasting with denoising diffusion models. In: Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems, pp. 1\u201312 (2023)","DOI":"10.1145\/3589132.3625614"},{"key":"22_CR19","doi-asserted-by":"crossref","unstructured":"Yan, J., Li, P., Huang, Y.: A short-term wind power scenario generation method based on conditional diffusion model. In: 2023 IEEE Sustainable Power and Energy Conference (iSPEC), pp.\u00a01\u20136 (2023)","DOI":"10.1109\/iSPEC58282.2023.10403004"},{"key":"22_CR20","unstructured":"Yan, T., Gong, H., He, Y., Zhan, Y., Xia, Y.: Probabilistic time series modeling with decomposable denoising diffusion model. In: International Conference on Machine Learning, vol.\u00a0235, pp. 55759\u201355777 (2024)"}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-3462-3_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T22:03:22Z","timestamp":1760738602000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-3462-3_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,17]]},"ISBN":["9789819534616","9789819534623"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-3462-3_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,10,17]]},"assertion":[{"value":"17 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kyoto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adma2025.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}