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Although machine learning (ML) has been successfully implemented for various HP applications, optimization of household hot water demand forecasting remains understudied. This paper addresses this problem by introducing a novel approach that combines predictive ML with anomaly detection to create adaptive hot water production strategies based on household-specific consumption patterns. Our key contributions include: (1) a composite approach combining ML and isolation forest (iForest) to forecast household demand for hot water and steer responsive HP operations; (2) multi-step feature selection with advanced time series analysis to capture complex usage patterns; (3) application and tuning of three ML models: light gradient boosting machine (LightGBM), long short-term memory (LSTM), and bidirectional LSTM with the self-attention mechanism on data from different types of real HP installations; and (4) experimental validation on six real household installations. Our experiments show that the best-performing model LightGBM achieves superior performance, with RMSE improvements of up to 9.37% compared to LSTM variants with <jats:inline-formula>\n              <jats:alternatives>\n                <jats:tex-math>$$R^2$$<\/jats:tex-math>\n                <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msup>\n                    <mml:mi>R<\/mml:mi>\n                    <mml:mn>2<\/mml:mn>\n                  <\/mml:msup>\n                <\/mml:math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula> values between 0.748<jats:inline-formula>\n              <jats:alternatives>\n                <jats:tex-math>$$-$$<\/jats:tex-math>\n                <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mo>-<\/mml:mo>\n                <\/mml:math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula>0.983. For anomaly detection, our iForest implementation achieved an F1-score of 0.87 with a false alarm rate of only 5.2%, demonstrating strong generalization capabilities across different household types and consumption patterns, making it suitable for real-world HP deployments.<\/jats:p>","DOI":"10.1007\/s00521-025-11318-y","type":"journal-article","created":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T01:20:15Z","timestamp":1748740815000},"page":"16203-16229","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Data-driven heat pump management: combining machine learning with anomaly detection for residential hot water systems"],"prefix":"10.1007","volume":"37","author":[{"given":"Manal","family":"Rahal","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9051-7609","authenticated-orcid":false,"given":"Bestoun S.","family":"Ahmed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roger","family":"Renstr\u00f6m","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert","family":"Stener","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Albrecht","family":"Wurtz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,1]]},"reference":[{"key":"11318_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.erss.2020.101764","volume":"71","author":"AS Gaur","year":"2021","unstructured":"Gaur AS, Fitiwi DZ, Curtis J (2021) Heat pumps and our low-carbon future: a comprehensive review. 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