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Nowadays, energy demand data are data coming from smart meters and have to be processed in real-time for more efficient demand management. In addition, electricity prices data can present changes over time such as new patterns and new trends. Therefore, real-time forecasting algorithms for both demand and prices have to adapt and adjust to online data in order to provide timely and accurate responses. This work presents a new algorithm for electricity demand and prices forecasting in real-time. The proposed algorithm generates a prediction model based on the k-nearest neighbors algorithm, which is incrementally updated in an online scenario considering both changes to existing patterns and adding new detected patterns to the model. Both time-frequency and error threshold based model updates have been evaluated. Results using energy demand from 2007 to 2016 and prices data for different time periods from the Spanish electricity market are reported and compared with other benchmark algorithms.<\/jats:p>","DOI":"10.1007\/s00521-024-10876-x","type":"journal-article","created":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T14:31:18Z","timestamp":1737729078000},"page":"22923-22940","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Online forecasting using neighbor-based incremental learning for electricity markets"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7609-0450","authenticated-orcid":false,"given":"L.","family":"Melgar-Garc\u00eda","sequence":"first","affiliation":[]},{"given":"D.","family":"Guti\u00e9rrez-Avil\u00e9s","sequence":"additional","affiliation":[]},{"given":"C.","family":"Rubio-Escudero","sequence":"additional","affiliation":[]},{"given":"A.","family":"Troncoso","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,24]]},"reference":[{"key":"10876_CR1","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1016\/j.jmsy.2019.11.004","volume":"54","author":"R Sahal","year":"2020","unstructured":"Sahal R, Breslin JG, Ali MI (2020) Big data and stream processing platforms for Industry 4.0 requirements mapping for a predictive maintenance use case. 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