{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T12:11:55Z","timestamp":1778155915624,"version":"3.51.4"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032253071","type":"print"},{"value":"9783032253088","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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\/978-3-032-25308-8_21","type":"book-chapter","created":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T11:34:11Z","timestamp":1778153651000},"page":"292-308","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Interpretable Load Forecasting with\u00a0Structured State Space Neural Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8022-2232","authenticated-orcid":false,"given":"Matthias","family":"Bittner","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8192-2004","authenticated-orcid":false,"given":"Daniel","family":"Hauer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1877-4114","authenticated-orcid":false,"given":"Matthias","family":"Wess","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4789-2375","authenticated-orcid":false,"given":"Dominik","family":"Dallinger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5834-6526","authenticated-orcid":false,"given":"Daniel","family":"Schn\u00f6ll","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6265-4064","authenticated-orcid":false,"given":"Konrad","family":"Diwold","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2251-0004","authenticated-orcid":false,"given":"Axel","family":"Jantsch","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,8]]},"reference":[{"key":"21_CR1","doi-asserted-by":"publisher","first-page":"100358","DOI":"10.1016\/j.egyai.2024.100358","volume":"16","author":"L Baur","year":"2024","unstructured":"Baur, L., Ditschuneit, K., Schambach, M., Kaymakci, C., Wollmann, T., Sauer, A.: Explainability and interpretability in electric load forecasting using machine learning techniques \u2013 a review. Energy AI 16, 100358 (2024). https:\/\/doi.org\/10.1016\/j.egyai.2024.100358","journal-title":"Energy AI"},{"key":"21_CR2","doi-asserted-by":"publisher","unstructured":"Bittner, M., Hauer, D., Stippel, C., Scheucher, K., Sudhoff, R., Jantsch, A.: Forecasting critical overloads based on heterogeneous smart grid simulation. In: 2023 International Conference on Machine Learning and Applications (ICMLA), pp. 339\u2013346 (2023). https:\/\/doi.org\/10.1109\/ICMLA58977.2023.00054","DOI":"10.1109\/ICMLA58977.2023.00054"},{"key":"21_CR3","unstructured":"David, R., et al.: TensorFlow lite micro: embedded machine learning on TinyML systems. CoRR abs\/2010.08678 (2020). https:\/\/arxiv.org\/abs\/2010.08678"},{"key":"21_CR4","unstructured":"GeoSphere, G.A.: Geosphere data hub \u201cmessstationen zehnminutendaten\u201d (2024). https:\/\/data.hub.geosphere.at\/dataset\/"},{"key":"21_CR5","unstructured":"Goel, K., Gu, A., Donahue, C., R\u2019e, C.: It\u2019s raw! Audio generation with state-space models. In: International Conference on Machine Learning (2022). https:\/\/api.semanticscholar.org\/CorpusID:247011489"},{"key":"21_CR6","unstructured":"Gu, A., Dao, T.: Mamba: linear-time sequence modeling with selective state spaces. ArXiv abs\/2312.00752 (2023). https:\/\/api.semanticscholar.org\/CorpusID:265551773"},{"key":"21_CR7","unstructured":"Gu, A., Dao, T., Ermon, S., Rudra, A., R\u00e9, C.: Hippo: recurrent memory with optimal polynomial projections. CoRR abs\/2008.07669 (2020). https:\/\/arxiv.org\/abs\/2008.07669"},{"key":"21_CR8","unstructured":"Gu, A., Goel, K., R\u2019e, C.: Efficiently modeling long sequences with structured state spaces. ArXiv abs\/2111.00396 (2021). https:\/\/api.semanticscholar.org\/CorpusID:240354066"},{"key":"21_CR9","unstructured":"Gu, A., Gupta, A., Goel, K., R\u00e9, C.: On the parameterization and initialization of diagonal state space models. In: Proceedings of the 36th International Conference on Neural Information Processing Systems, NIPS 2022. Curran Associates Inc., Red Hook (2024)"},{"key":"21_CR10","unstructured":"Gupta, A., Gu, A., Berant, J.: Diagonal state spaces are as effective as structured state spaces (2022)"},{"key":"21_CR11","doi-asserted-by":"publisher","unstructured":"Habbak, H., Mahmoud, M., Metwally, K., Fouda, M.M., Ibrahem, M.I.: Load forecasting techniques and their applications in smart grids. Energies 16(3) (2023). https:\/\/doi.org\/10.3390\/en16031480, https:\/\/www.mdpi.com\/1996-1073\/16\/3\/1480","DOI":"10.3390\/en16031480"},{"key":"21_CR12","doi-asserted-by":"publisher","unstructured":"Hauer, D., et al.: Re-enacting rare multi-modal real-world grid events to generate ml training data sets. In: 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE), pp. 01\u201306 (2021).https:\/\/doi.org\/10.1109\/ISIE45552.2021.9576350","DOI":"10.1109\/ISIE45552.2021.9576350"},{"key":"21_CR13","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1026\u20131034 (2015). https:\/\/api.semanticscholar.org\/CorpusID:13740328","DOI":"10.1109\/ICCV.2015.123"},{"key":"21_CR14","doi-asserted-by":"publisher","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997). https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"21_CR15","unstructured":"Iserles, A.: A First Course in the Numerical Analysis of Differential Equations. Cambridge Texts in Applied Mathematics, 2nd edn. Cambridge University Press (2008)"},{"key":"21_CR16","doi-asserted-by":"crossref","unstructured":"Kainz, J., et al.: Grid-friendly renewable energy communities using operating envelopes provided by DSOs. In: CIRED 2023 - 27th International Conference on Electricity Distribution, no. 11051. IET (2023)","DOI":"10.1049\/icp.2023.1040"},{"key":"21_CR17","unstructured":"Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30, pp. 4765\u20134774. Curran Associates, Inc. (2017). http:\/\/papers.nips.cc\/paper\/7062-a-unified-approach-to-interpreting-model-predictions.pdf"},{"key":"21_CR18","unstructured":"Mehta, H., Gupta, A., Cutkosky, A., Neyshabur, B.: Long range language modeling via gated state spaces (2022)"},{"issue":"12","key":"21_CR19","doi-asserted-by":"publisher","first-page":"3290","DOI":"10.3390\/en13123290","volume":"13","author":"S Meinecke","year":"2020","unstructured":"Meinecke, S., et al.: SimBench-a benchmark dataset of electric power systems to compare innovative solutions based on power flow analysis. Energies 13(12), 3290 (2020)","journal-title":"Energies"},{"key":"21_CR20","unstructured":"Meteoblue: Meteoblue, weather API (2024). www.meteoblue.com"},{"key":"21_CR21","doi-asserted-by":"publisher","unstructured":"Mohaidat, T., Khalil, K.: A survey on neural network hardware accelerators. IEEE Trans. Artif. Intell. 1\u201321 (2024). https:\/\/doi.org\/10.1109\/TAI.2024.3377147","DOI":"10.1109\/TAI.2024.3377147"},{"key":"21_CR22","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1007\/978-3-030-11051-2_33","volume-title":"Intelligent Human Systems Integration 2019","author":"R Mosshammer","year":"2019","unstructured":"Mosshammer, R., Diwold, K., Einfalt, A., Schwarz, J., Zehrfeldt, B.: BIFROST: a smart city planning and simulation tool. In: Karwowski, W., Ahram, T. (eds.) IHSI 2019. AISC, vol. 903, pp. 217\u2013222. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11051-2_33"},{"key":"21_CR23","unstructured":"ONNX: Onnx: open neural network exchange (2024). https:\/\/onnx.ai\/"},{"key":"21_CR24","doi-asserted-by":"publisher","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: \u201cWhy should I trust you?\u201d: explaining the predictions of any classifier. In: Proceedings of the Demonstrations Session, NAACL HLT 2016, The 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego California, USA, 12\u201317 June 2016, pp. 97\u2013101. The Association for Computational Linguistics (2016). https:\/\/doi.org\/10.18653\/V1\/N16-3020","DOI":"10.18653\/V1\/N16-3020"},{"key":"21_CR25","doi-asserted-by":"publisher","unstructured":"Rolnick, D., et\u00a0al.: Tackling climate change with machine learning. ACM Comput. Surv. 55(2), 42:1\u201342:96 (2023). https:\/\/doi.org\/10.1145\/3485128","DOI":"10.1145\/3485128"},{"key":"21_CR26","unstructured":"Smith, J., Warrington, A., Linderman, S.W.: Simplified state space layers for sequence modeling. ArXiv abs\/2208.04933 (2022). https:\/\/api.semanticscholar.org\/CorpusID:251442769"},{"issue":"6","key":"21_CR27","doi-asserted-by":"publisher","first-page":"6510","DOI":"10.1109\/TPWRS.2018.2829021","volume":"33","author":"L Thurner","year":"2018","unstructured":"Thurner, L., et al.: Pandapower-an open-source python tool for convenient modeling, analysis, and optimization of electric power systems. IEEE Trans. Power Syst. 33(6), 6510\u20136521 (2018)","journal-title":"IEEE Trans. Power Syst."},{"key":"21_CR28","unstructured":"xCubeAI: X-cube-AI: AI expansion pack for STM32CubeMX (2023). https:\/\/www.st.com\/en\/embedded-software\/x-cube-ai.html"}],"container-title":["Communications in Computer and Information Science","Machine Learning and Principles and Practice of Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-25308-8_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T11:34:22Z","timestamp":1778153662000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-25308-8_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032253071","9783032253088"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-25308-8_21","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"8 May 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vilnius","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lithuania","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2024.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}