يعرض 1 - 20 نتائج من 27 نتيجة بحث عن '"building management systems (BMS)"', وقت الاستعلام: 0.58s تنقيح النتائج
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    Conference

    المؤلفون: Lucbert, Adrien, Onderzoeker, Niet,van der, Juliën, Lid lectoraat, Corson, Albert, Onderzoeker, Weij, Michael, Student, Elst,van der, Ramon Isaac, Lid lectoraat, Martínez de Juan, Jesús Mª, Onderzoeker, Baldiri Salcedo Rahola, Tadeo, Docent

    المساهمون: Lectoraat Energy in Transition, De Haagse Hogeschool@@@Faculteit Technologie, Innovatie & Samenleving

    المصدر: Time Series Building Energy Systems Data Imputation. CLIMA 2022 Conference.. :1-8

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    Conference
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    Academic Journal
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    Academic Journal
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    Conference

    المساهمون: Seventh Framework Programme

    وصف الملف: application/pdf

    Relation: International Conference on Applied Energy (ICAE 2013), Pretoria, South Africa, 1-4 July 2013; Costa, Andrea, Sterling, Raymond, Blanes, Luis M., Howley, Martin, & Keane, Marcus M. (2013). A SWOT framework to investigate the integration between building management systems and fault detection and diagnosis tools. Paper presented at the International Conference on Applied Energy (ICAE 2013), Pretoria, South Africa, 01-04 July.; http://hdl.handle.net/10379/16562

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    Dissertation/ Thesis
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    Academic Journal
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    المصدر: SMARTCOMP

    مصطلحات موضوعية: Computer science, 020209 energy, long short term memory, forecasting, 02 engineering and technology, generalized additive model, 010501 environmental sciences, Machine learning, computer.software_genre, 01 natural sciences, 0202 electrical engineering, electronic engineering, information engineering, Feature (machine learning), Time series, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, Building management system, gas forecasting, business.industry, Deep learning, Generalized additive model, deep learning, Missing data, Time series behavior of gas consumption is highly irregular, non-stationary, and volatile due to its dependency on the weather, users’ habits and lifestyle. This complicates the modeling and forecasting of gas consumption with most of the existing time series modeling techniques, specifically when missing values and outliers are present. To demonstrate and overcome these problems, we investigate two approaches to model the gas consumption, namely Generalized Additive Models (GAM) and Long Short-Term Memory (LSTM). We perform our evaluations on two building datasets from two different conti-nents. We present each selected feature’s influence, the tuning parameters, and the characteristics of the gas consumption on their forecasting abilities. We compare the performances of GAM and LSTM with other state-of-the-art forecasting approaches. We show that LSTM outperforms GAM and other existing approaches, however, GAM provides better interpretable results for building management systems (BMS), Recurrent neural network, Outlier, Artificial intelligence, business, computer

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    Academic Journal
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    Academic Journal
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    Conference

    المساهمون: DE CARLI, Michele, DE GIULI, Valeria

    وصف الملف: ELETTRONICO

    Relation: info:eu-repo/semantics/altIdentifier/isbn/9780947649401; ispartofbook:Proceedings of Building Simulation 2009; The 11th International Building Performance Simulation Association Conference; firstpage:1797; lastpage:1805; numberofpages:9; http://hdl.handle.net/11577/2474243

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    Academic Journal

    المصدر: Ghadi, YY, Rasul, MG & Khan, MMK 2014, 'Potential of saving energy using advanced fuzzy logic controllers in smart buildings in subtropical climates in Australia', Energy Procedia, vol. 61, pp. 290-293, http://dx.doi.org/10.1016/j.egypro.2014.11.1110

    Relation: Energy procedia. Netherlands : Elsevier, 2014. Vol. 61, (2014), p. 290-293 4 pages Refereed 1876-6102; ACQUIRE [electronic resource] : Central Queensland University Institutional Repository.; cqu:12278; http://hdl.cqu.edu.au/10018/1030528

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    Academic Journal
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