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PEOPLE@HES-SO – Directory and Skills inventory

PEOPLE@HES-SO
Directory and Skills inventory

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Zarkadis Nikos

Zarkadis Nikos

Professeur HES associé

Main skills

Building performance

Building Physics

Thermal & Visual Comfort

Energy modeling and simulation

Smart Building

Machine Learning

User behaviour and well-being

  • Contact

  • Teaching

  • Publications

  • Conferences

Main contract

Professeur HES associé

Desktop: Bi13

Haute école du paysage, d'ingénierie et d'architecture de Genève
Rue de la Prairie 4, 1202 Genève, CH
hepia
Faculty
Technique et IT
Main Degree Programme
Technique des bâtiments

Professor Nikos Zarkadis was born in 1979 in Athens, Greece. He holds a bachelor degree in Energy and Environmental Technology (Technological Educational Institute of Crete, 2003), a MSc in Environmental Protection Technologies (University of Crete, 2008) and a PhD from Ecole Polytechnique Fédérale de Lausanne (EPFL, 2015) in predictive control of advanced building systems and occupant comfort in buildings. Since 2015, he is lecturer at the School of Landscape, Engineering and Architecture (hepia) at the University of Applied Sciences of Geneva (HES-SO), where he teaches Energy performances of buildings. Between 2003 and 2010, he has also worked as an engineer and consultant in the private sector. His work and research has been focused on optimising (the control of) energy-demanding and energy-sensitive building systems (i.e. envelope, artificial lighting, HVAC, shades, blinds, etc.) in a way that they respect user wishes, adapt to the building's own characteristics and to the building's environment, respond well to outdoor variations, reduce the energy demand and provide for a better visual and thermal environment for the users.

Research fields:

  • Energy performance & efficiency of buildings 
  • Avanced/intelligent control systems and smart buildings
  • Building Physics
  • Occupants' visual & thermal comfort
  • Energy impacts of user behaviour
  • Data retrieval, handling, management and analysis (data mining & machine learning)
  • Energy audits, thermal building modelling & simulation
BA HES-SO en Architecture - Haute école du paysage, d'ingénierie et d'architecture de Genève
  • Energy performance of buildings
  • Control Systems Basics
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
Scientific paper

Antonio Ridi, Zarkadis Nikos, Gisler Christophe, Hennebert Jean

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

Link to the publication

2015

Duration models for activity recognition and prediction in buildings using hidden Markov models
Conference 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 to the conference

Summary:

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
Conference 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 to the conference

Summary:

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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