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PEOPLE@HES-SO - Verzeichnis der Mitarbeitenden und Kompetenzen
PEOPLE@HES-SO - Verzeichnis der Mitarbeitenden und Kompetenzen

PEOPLE@HES-SO
Verzeichnis der Mitarbeitenden und Kompetenzen

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Zarkadis Nikos

Zarkadis Nikos

Professeur HES associé

Hauptkompetenzen

Building performance

Building Physics

Thermal & Visual Comfort

Energy modeling and simulation

Smart Building

Machine Learning

User behaviour and well-being

  • Kontakt

  • Lehre

  • Publikationen

  • Konferenzen

Hauptvertrag

Professeur HES associé

Büro: Bi13

Haute école du paysage, d'ingénierie et d'architecture de Genève
Rue de la Prairie 4, 1202 Genève, CH
hepia
Bereich
Technique et IT
Hauptstudiengang
Technique des bâtiments
BA HES-SO en Architecture - Haute école du paysage, d'ingénierie et d'architecture de Genève
  • Performances énergétiques des bâtiments 1 et 2
  • Bases de régulation et mesures
MSc HES-SO en Engineering - HES-SO Master
  • Fondements de l'énergie et du génie environnemental

2015

Duration models for activity recognition and prediction in buildings using hidden markov models
Wissenschaftlicher Artikel

Antonio Ridi, Zarkadis Nikos, Gisler Christophe, Hennebert Jean

2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2015 , pp.  1-10

Link zur Publikation

2015

Duration models for activity recognition and prediction in buildings using hidden Markov models
Konferenz ArODES

Antonio Ridi, Nikos Zarkadis, Christophe Gisler, Jean Hennebert

Proceedings of 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 19-21 October 2015, Paris, France

Link zur Konferenz

Zusammenfassung:

Activity recognition and prediction in buildings can have multiple positive effects in buildings: improve elderly monitoring, detect intrusions, maximize energy savings and optimize occupant comfort. In this paper we apply human activity recognition by using data coming from a network of motion and door sensors distributed in a Smart Home environment. We use Hidden Markov Models (HMM) as the basis of a machine learning algorithm on data collected over an 8-month period from a single-occupant home available as part of the WSU CASAS Smart Home project. In the first implementation the HMM models 24 hours of activities and classifies them in 8 distinct activity categories with an accuracy rate of 84.6%. To improve the identification rate and to help detect potential abnormalities related with the duration of an activity (i.e. when certain activities last too much), we implement minimum duration modeling where the algorithm is forced to remain in a certain state for a specific amount of time. Two subsequent implementations of the minimum duration HMM (mean-based length modeling and quantile length modeling) yield a further 2% improvement of the identification rate. To predict the sequence of activities in the future, Artificial Neural Networks (ANN) are employed and identified activities clustered in 3 principal activity groups with an average accuracy rate of 71-77.5%, depending on the forecasting window. To explore the energy savings potential, we apply thermal dynamic simulations on buildings in central European climate for a period of 65 days during the winter and we obtain energy savings for space heating of up to 17% with 3-hour forecasting for two different types of buildings.

2013

Towards reliable stochastic data-driven models applied to the energy saving in buildings
Konferenz ArODES

Antonio Ridi, Nikos Zarkadis, Gérôme Bovet, Nicolas Morel, Jean Hennebert

Proceedings of CISBAT 2013 - Cleantech for Smart Cities Buildings - From Nano to Urban Scale, 4-6 September 2013, Lausanne, Switzerland

Link zur Konferenz

Zusammenfassung:

We aim at the elaboration of Information Systems able to optimize energy consumption in buildings while preserving human comfort. Our focus is in the use of state-based stochas- tic modeling applied to temporal signals acquired from heterogeneous sources such as distributed sensors, weather web services, calendar information and user triggered events. Our general scienti_c objectives are: (1) global instead of local optimization of building automation sub-systems (heating, ventilation, cooling, solar shadings, electric lightings), (2) generalization to unseen building con_guration or usage through self-learning data- driven algorithms and (3) inclusion of stochastic state-based modeling to better cope with seasonal and building activity patterns. We leverage on state-based models such as Hidden Markov Models (HMMs) to be able to capture the spatial (states) and temporal (sequence of states) characteristics of the signals. We envision several application layers as per the intrinsic nature of the signals to be modeled. We also envision room-level systems able to leverage on a set of distributed sensors (temperature, presence, electricity consumption, etc.). A typical example of room-level system is to infer room occupancy information or activities done in the rooms as a function of time. Finally, building-level systems can be composed to infer global usage and to propose optimization strategies for the building as a whole. In our approach, each layer may be fed by the output of the previous layers. More speci_cally in this paper, we report on the design, conception and validation of several machine learning applications. We present three di_erent applications of state- based modeling. In the _rst case we report on the identi_cation of consumer appliances through an analysis of their electric loads. In the second case we perform the activity recognition task, representing human activities through state-based models. The third case concerns the season prediction using building data, building characteristic parameters and meteorological data.

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