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Prof. KONSTANTINOS MOUSTRIS
Assoc Prof, Dr

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Research Keywords & Expertise

0 Air Pollution
0 Environmental Management
0 Biometeorology
0 artificial neural networks modeling
0 renewable energy modeling

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artificial neural networks modeling
Air Pollution

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Short Biography

Dr. Konstantinos P. Moustris holds a BSc in Physics, MSc in Environmental Physics and Meteorology and a PhD in environmental sciences and artificial neural networks modeling. Today, he is an Associate Professor at the University of West Attica, Athens, Greece, at the Department of Mechanical Engineering. He has 37 publications in scientific refereed journals; 60 papers/works in international refereed conferences and 9 papers in national refereed conferences. He is also Reviewer in fifteen (15) at least international scientific journals in the field of energy and environment. Dr. Moustris specializes in Artificial neural network modelling; biometeorology; air pollution; forecasting; solar radiation variation and forecasting; wind power prediction, fluid mechanics. Furthermore, he has enough experience in scientific research programs. He is Member of the Greek Meteorological Society (EMTE), Member of the Balkan Environmental Association (B.EN.A), Member of the Organizing Committee of the 11th, 12th,13th, 14th and 15th International Conference on Meteorology, Climatology and Atmospheric Physics. Finally, he holds two European awards:  European Commission, 2017, “EU Sustainable Energy Award” in the energy islands category, project TILOS Island - Horizon 2020.  European Commission, 2017, “EU Sustainable Energy Citizens Award” in the citizens category, project TILOS Island - Horizon 2020.

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Preprint
Published: 01 June 2021
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Most people living in Europe's cities are still exposed to levels of air pollution deemed harmful by the World Health Organization. In the modern world, air pollution is the foremost concern because of its impact in human health and economy. This strong connection appears gaining a lot of concern, driven by new installed low-cost electrochemical sensors monitoring systems. Highly accuracy, real-time monitoring, daily and yearly statistics, data access from experts or simple users, low-cost equipment and forecasting needs, enforce the market to develop new air quality monitoring systems using advanced technologies and protocols. In this study, a comparison via low-cost electrochemical sensors and of static, fixed site measurement monitoring station, is taking place in Athens, Greece, along with the data quality and Air Quality Index (AQI) including data accuracy and quality of data concerning adverse health effects due to air pollution. The findings presented in this work, relate to different flexible and affordable alternatives adopted during the evaluation and calibration of low-cost gas sensors for the monitoring. The significance of the positive results is particularly useful, especially considering the founding for interference, environmental conditions affections and air quality information including indexes and health recommendations for a specific location.

ACS Style

Georgios C. Spyropoulos; Panagiotis T. Nastos; Konstantinos P. Moustris. Performance of Low-Cost Sensors for Air Pollution Measurements in Urban Environments. Accuracy Evaluation Applying the Air Quality Index (AQI). 2021, 1 .

AMA Style

Georgios C. Spyropoulos, Panagiotis T. Nastos, Konstantinos P. Moustris. Performance of Low-Cost Sensors for Air Pollution Measurements in Urban Environments. Accuracy Evaluation Applying the Air Quality Index (AQI). . 2021; ():1.

Chicago/Turabian Style

Georgios C. Spyropoulos; Panagiotis T. Nastos; Konstantinos P. Moustris. 2021. "Performance of Low-Cost Sensors for Air Pollution Measurements in Urban Environments. Accuracy Evaluation Applying the Air Quality Index (AQI)." , no. : 1.

Journal article
Published: 05 March 2021 in Entropy
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This paper utilises statistical and entropy methods for the investigation of a 17-year PM10 time series recorded from five stations in Athens, Greece, in order to delineate existing stochastic and self-organisation trends. Stochastic patterns are analysed via lumping and sliding, in windows of various lengths. Decreasing trends are found between Windows 1 and 3500–4000, for all stations. Self-organisation is studied through Boltzmann and Tsallis entropy via sliding and symbolic dynamics in selected parts. Several values are below −2 (Boltzmann entropy) and 1.18 (Tsallis entropy) over the Boltzmann constant. A published method is utilised to locate areas for which the PM10 system is out of stochastic behaviour and, simultaneously, exhibits critical self-organised tendencies. Sixty-six two-month windows are found for various dates. From these, nine are common to at least three different stations. Combining previous publications, two areas are non-stochastic and exhibit, simultaneously, fractal, long-memory and self-organisation patterns through a combination of 15 different fractal and SOC analysis techniques. In these areas, block-entropy (range 0.650–2.924) is significantly lower compared to the remaining areas of non-stochastic but self-organisation trends. It is the first time to utilise entropy analysis for PM10 series and, importantly, in combination with results from previously published fractal methods.

ACS Style

Dimitrios Nikolopoulos; Aftab Alam; Ermioni Petraki; Michail Papoutsidakis; Panayiotis Yannakopoulos; Konstantinos Moustris. Stochastic and Self-Organisation Patterns in a 17-Year PM10 Time Series in Athens, Greece. Entropy 2021, 23, 307 .

AMA Style

Dimitrios Nikolopoulos, Aftab Alam, Ermioni Petraki, Michail Papoutsidakis, Panayiotis Yannakopoulos, Konstantinos Moustris. Stochastic and Self-Organisation Patterns in a 17-Year PM10 Time Series in Athens, Greece. Entropy. 2021; 23 (3):307.

Chicago/Turabian Style

Dimitrios Nikolopoulos; Aftab Alam; Ermioni Petraki; Michail Papoutsidakis; Panayiotis Yannakopoulos; Konstantinos Moustris. 2021. "Stochastic and Self-Organisation Patterns in a 17-Year PM10 Time Series in Athens, Greece." Entropy 23, no. 3: 307.

Journal article
Published: 06 October 2020 in Environments
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This work investigates the spatiotemporal variation of suspended particles with aerodynamic diameter less than or equal to 10 μm (PM10) during a nineteen years period. Mean daily PM10 concentrations between 2001 and 2018, from five monitoring stations within the greater Athens area (GAA) are used. The aim is to investigate the impact of the economic crisis and the actions taken by the Greek state over the past decade on the distribution of PM10 within the GAA. Seasonality, intraweek, intraday and spatial variations of the PM10 concentrations as well as trends of data, are statistically studied. The work may assist the formation of PM10 forecasting models of hourly, daily, weekly, monthly and annual horizon. Innovations are alternative ways of statistical treatment and the extended period of data, which, importantly, includes major economic and social events for the GAA. Significant decreasing trend in PM10 series concentrations at all examined stations were found. This may be due to economic and social reasons but also due to measures taken by the state so as to be harmonised with the European Directives concerning the protection of public health and the atmospheric environment of the European Union (EU) members.

ACS Style

Konstantinos P. Moustris; Ermioni Petraki; Kleopatra Ntourou; Georgios Priniotakis; Dimitrios Nikolopoulos. Spatiotemporal Evaluation of PM10 Concentrations within the Greater Athens Area, Greece. Trends, Variability and Analysis of a 19 Years Data Series. Environments 2020, 7, 85 .

AMA Style

Konstantinos P. Moustris, Ermioni Petraki, Kleopatra Ntourou, Georgios Priniotakis, Dimitrios Nikolopoulos. Spatiotemporal Evaluation of PM10 Concentrations within the Greater Athens Area, Greece. Trends, Variability and Analysis of a 19 Years Data Series. Environments. 2020; 7 (10):85.

Chicago/Turabian Style

Konstantinos P. Moustris; Ermioni Petraki; Kleopatra Ntourou; Georgios Priniotakis; Dimitrios Nikolopoulos. 2020. "Spatiotemporal Evaluation of PM10 Concentrations within the Greater Athens Area, Greece. Trends, Variability and Analysis of a 19 Years Data Series." Environments 7, no. 10: 85.

Journal article
Published: 22 September 2020 in Sustainability
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The building sector consumes 36% of the world’s energy and produces around 40% of energy-related carbon emissions. While the building industry moves towards a zero net greenhouse-gas emission policy, ventilation is, and will be, a necessity for the preservation of air quality—especially in climates defined by unsavoury conditions. Therefore, a “mixing mode” cooling system was employed to lower the required energy consumption at an earthen building situated in the premises of Istanbul Technical University. A room of the high-mass earthen building was monitored under different ventilation and shading conditions. Night ventilation was conducted using two modes, 3.2 and 2.3 air changes per hour, and the air conditioning unit, operating from 08:00 to 17:00, had a set temperature of 23 ∘C. Night ventilation was somewhat impactful, reducing the average expected cooling energy demand up to 27%. Furthermore, the earthen building proved to be extremely effective on moderating extremes of temperature under non-ventilated conditions. During a rather hot day, with an outdoor maximum temperature of 35 ∘C, the indoor maximum temperature of the high-mass building was only 25 ∘C, namely within thermal comfort levels. The diurnal temperature proved to be key in the effective application of night ventilation.

ACS Style

Michael Darmanis; Murat Çakan; Konstantinos Moustris; Kosmas Kavadias; Konstantinos-Stefanos Nikas. Utilisation of Mass and Night Ventilation in Decreasing Cooling Load Demand. Sustainability 2020, 12, 7826 .

AMA Style

Michael Darmanis, Murat Çakan, Konstantinos Moustris, Kosmas Kavadias, Konstantinos-Stefanos Nikas. Utilisation of Mass and Night Ventilation in Decreasing Cooling Load Demand. Sustainability. 2020; 12 (18):7826.

Chicago/Turabian Style

Michael Darmanis; Murat Çakan; Konstantinos Moustris; Kosmas Kavadias; Konstantinos-Stefanos Nikas. 2020. "Utilisation of Mass and Night Ventilation in Decreasing Cooling Load Demand." Sustainability 12, no. 18: 7826.

Original paper
Published: 28 May 2020 in Theoretical and Applied Climatology
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This paper investigates the existence of chaos in concentration dynamics of particulate matter with an aerodynamic diameter less than or equal to \(10\,\upmu \hbox {m}\) (\(\hbox {PM}_{10}\)) in the greater Athens area (GAA), Greece. It reports findings on three 16-year \(\hbox {PM}_{10}\) time series recorded by three different air pollution monitoring stations located in GAA and examines if critical fractal epochs with long memory exist. Detrended Fluctuation Analysis (DFA) and Rescaled Range (R/S) Analysis were used via sliding windows of approximately 1-month duration. In all \(\hbox {PM}_{10}\) time series, several segments were found with critical fractal behaviour and hidden long-memory patterns. All these segments exhibited Hurst exponents above 0.75 and DFA exponents above 1.75. Twelve \(\hbox {PM}_{10}\) segments with fractality and long memory were commonly identified by both techniques. In one case, long memory was identified concurrently across all three air pollution monitoring stations and in another case, across two stations. The importance of the agreement between two different and independent chaos-analysis techniques is discussed in association with the proper selection of threshold values. This is the second time to address chaos in \(\hbox {PM}_{10}\) data series in GAA, and the first time to combine two widely accepted techniques, DFA and R/S analysis.

ACS Style

Dimitrios Nikolopoulos; Kostas P. Moustris; Ermioni Petraki; Demetrios Cantzos. Long-memory traces in $$\hbox {PM}_{10}$$ time series in Athens, Greece: investigation through DFA and R/S analysis. Theoretical and Applied Climatology 2020, 133, 261 -279.

AMA Style

Dimitrios Nikolopoulos, Kostas P. Moustris, Ermioni Petraki, Demetrios Cantzos. Long-memory traces in $$\hbox {PM}_{10}$$ time series in Athens, Greece: investigation through DFA and R/S analysis. Theoretical and Applied Climatology. 2020; 133 (2):261-279.

Chicago/Turabian Style

Dimitrios Nikolopoulos; Kostas P. Moustris; Ermioni Petraki; Demetrios Cantzos. 2020. "Long-memory traces in $$\hbox {PM}_{10}$$ time series in Athens, Greece: investigation through DFA and R/S analysis." Theoretical and Applied Climatology 133, no. 2: 261-279.

Journal article
Published: 02 November 2019 in Computers and Electronics in Agriculture
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In the present study, moisture content evolution of cylindrical quince slices during convective drying was modelled by using artificial neural networks (ANN). Quince slices with an average initial moisture content of 81% in wet basis (w.b.) or 4.27 kgwater/kgdry matter in dry basis (d.b.), were dried in a laboratory thermal convective dryer and experimental data of moisture content versus drying time was obtained for nine measurement groups of 40, 50 and 60 °C drying air temperature and 1, 2 and 3 m/s airflow velocity respectively. Different topologies of multilayer perceptron (MLP) ANN models containing a single or two hidden layers with a different number of hidden neurons and different types of transfer functions, have been investigated for predicting the moisture content evolution during drying. A group k-fold cross validation iteration procedure was performed for each developed ANN structure, in order to assess each model’s ability to estimate the moisture content of quinces on unseen data of air-drying temperature and airflow velocity combinations held out of the training process. For the cross validation of the developed ANN models, appropriate statistical evaluation indices were applied. The best performed ANN model based on the cross validation score metrics, contained two hidden layers with the sigmoid, softplus transfer functions and was composed by 90 artificial hidden neurons in each of the two hidden layers. A satisfying agreement of predictions with the experimental data was noticed, achieving coefficients of determination (R2) greater than 99% and root mean square error (RMSE) values less than 0.08 kgwater/kgdry matter.

ACS Style

V.K. Chasiotis; D.A. Tzempelikos; A.E. Filios; K.P. Moustris. Artificial neural network modelling of moisture content evolution for convective drying of cylindrical quince slices. Computers and Electronics in Agriculture 2019, 172, 105074 .

AMA Style

V.K. Chasiotis, D.A. Tzempelikos, A.E. Filios, K.P. Moustris. Artificial neural network modelling of moisture content evolution for convective drying of cylindrical quince slices. Computers and Electronics in Agriculture. 2019; 172 ():105074.

Chicago/Turabian Style

V.K. Chasiotis; D.A. Tzempelikos; A.E. Filios; K.P. Moustris. 2019. "Artificial neural network modelling of moisture content evolution for convective drying of cylindrical quince slices." Computers and Electronics in Agriculture 172, no. : 105074.

Journal article
Published: 26 August 2019 in Renewable Energy
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The objective of the present work is the medium, short and very short-term prognosis of load demand (LD) for the small-scale island of Tilos in Greece. For this purpose, Artificial Neural Network (ANNs) models were developed to forecast the LD of Tilos for different prediction horizons and time intervals, these covering the cases of 24 h ahead in hourly intervals (medium term prognosis), 2 h ahead in 10-min intervals (short term prognosis) and 10-min ahead in 1-min intervals (very short term prognosis). At the same time, stochastic/persistence autoregressive (AR) models were also developed and compared with the respective ANN models with regards to the LD prediction results obtained. For the training of the developed ANNs, meteorological data covering the period 2015–2017 were used, which had been recorded in 1-min intervals by two meteorological masts installed on the island Tilos. Furthermore, the biometeorological human thermal comfort-discomfort index, known as the cooling power index (CP), was also estimated and introduced in the training procedure of the forecasting models, while, for the evaluation of both AR and ANN forecasting models, well established statistical evaluation indices were applied. To this end, results show that in all cases covered, i.e. for both medium and short-term prognoses, the developed ANN forecasting models present a remarkable ability to predict the local LD of the island with high accuracy, enabling in this way the development of advanced energy management tools for both end-users and the system operators.

ACS Style

K. Moustris; K.A. Kavadias; D. Zafirakis; J.K. Kaldellis. Medium, short and very short-term prognosis of load demand for the Greek Island of Tilos using artificial neural networks and human thermal comfort-discomfort biometeorological data. Renewable Energy 2019, 147, 100 -109.

AMA Style

K. Moustris, K.A. Kavadias, D. Zafirakis, J.K. Kaldellis. Medium, short and very short-term prognosis of load demand for the Greek Island of Tilos using artificial neural networks and human thermal comfort-discomfort biometeorological data. Renewable Energy. 2019; 147 ():100-109.

Chicago/Turabian Style

K. Moustris; K.A. Kavadias; D. Zafirakis; J.K. Kaldellis. 2019. "Medium, short and very short-term prognosis of load demand for the Greek Island of Tilos using artificial neural networks and human thermal comfort-discomfort biometeorological data." Renewable Energy 147, no. : 100-109.

Journal article
Published: 26 February 2019 in Environments
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This work examines if chaos and long memory exist in PM10 concentrations recorded in Athens, Greece. The algorithms of Katz, Higuchi, and Sevcik were employed for the calculation of fractal dimensions and Rescaled Range (R/S) analysis for the calculation of the Hurst exponent. Windows of approximately two months’ duration were employed, sliding one sample forward until the end of each utilized signal. Analysis was applied to three long PM10 time series recorded by three different stations located around Athens. Analysis identified numerous dynamical complex fractal time-series segments with patterns of long memory. All these windows exhibited Hurst exponents above 0.8 and fractal dimensions below 1.5 for the Katz and Higuchi algorithms, and 1.2 for the Sevcik algorithm. The paper discusses the importance of threshold values for the postanalysis of the discrimination of fractal and long-memory windows. After setting thresholds, computational calculations were performed on all possible combinations of two or more techniques for the data of all or two stations under study. When all techniques were combined, several common dates were found for the data of the two combinations of two stations. When the three techniques were combined, more common dates were found if the Katz algorithm was not included in the meta-analysis. Excluding Katz’s algorithm, 12 common dates were found for the data from all stations. This is the first time that the results from sliding-window chaos and long-memory techniques in PM10 time series were combined in this manner.

ACS Style

Dimitrios Nikolopoulos; Konstantinos Moustris; Ermioni Petraki; Dionysios Koulougliotis; Demetrios Cantzos. Fractal and Long-Memory Traces in PM10 Time Series in Athens, Greece. Environments 2019, 6, 29 .

AMA Style

Dimitrios Nikolopoulos, Konstantinos Moustris, Ermioni Petraki, Dionysios Koulougliotis, Demetrios Cantzos. Fractal and Long-Memory Traces in PM10 Time Series in Athens, Greece. Environments. 2019; 6 (3):29.

Chicago/Turabian Style

Dimitrios Nikolopoulos; Konstantinos Moustris; Ermioni Petraki; Dionysios Koulougliotis; Demetrios Cantzos. 2019. "Fractal and Long-Memory Traces in PM10 Time Series in Athens, Greece." Environments 6, no. 3: 29.

Journal article
Published: 01 February 2019 in Energy Procedia
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Variability of energy production is considered to be the main shortcoming in the operation of renewable energy systems. Combination of different Renewable Energy Sources (RES), employment of energy storage and application of Demand Side Management (DSM), are all elements used to encounter the problem of RES variability. Exploitation of such elements in an effective manner challenges the development of advanced Energy Management Systems (EMSs), especially in the case of island microgrids with high shares of RES, lacking the flexibility and capacity of centralized electricity systems to facilitate increased RES penetration. In this work, and in the framework of the Horizon 2020 TILOS project, an advanced Forecasting System (FS) has been developed, able to provide reliable predictions of load demand, wind power and solar power production. The specific variables are independently predicted through a set of forecasting models that produce both deterministic and probabilistic results for different time horizons and time resolutions, fully adjustable to the requirements of any given island microgrid. The developed FS has been deployed and tested considering the smart microgrid of Tilos island, in the SE Aegean Sea, with results obtained demonstrating its ability to provide sufficient and accurate forecasts for all studied variables.

ACS Style

Daniel H. Alamo; Rafael N. Medina; Santiago D. Ruano; Salvador S. García; Kostas P. Moustris; Kosmas Kavadias; Dimitris Zafirakis; George Tzanes; Effrosyni Zafeiraki; Georgios Spyropoulos; John K. Kaldellis; Gilles Notton; Jean-Laurent Duchaud; Marie-Laure Nivet; Alexis Fouilloy; Sylvain Lespinats. An Advanced Forecasting System for the Optimum Energy Management of Island Microgrids. Energy Procedia 2019, 159, 111 -116.

AMA Style

Daniel H. Alamo, Rafael N. Medina, Santiago D. Ruano, Salvador S. García, Kostas P. Moustris, Kosmas Kavadias, Dimitris Zafirakis, George Tzanes, Effrosyni Zafeiraki, Georgios Spyropoulos, John K. Kaldellis, Gilles Notton, Jean-Laurent Duchaud, Marie-Laure Nivet, Alexis Fouilloy, Sylvain Lespinats. An Advanced Forecasting System for the Optimum Energy Management of Island Microgrids. Energy Procedia. 2019; 159 ():111-116.

Chicago/Turabian Style

Daniel H. Alamo; Rafael N. Medina; Santiago D. Ruano; Salvador S. García; Kostas P. Moustris; Kosmas Kavadias; Dimitris Zafirakis; George Tzanes; Effrosyni Zafeiraki; Georgios Spyropoulos; John K. Kaldellis; Gilles Notton; Jean-Laurent Duchaud; Marie-Laure Nivet; Alexis Fouilloy; Sylvain Lespinats. 2019. "An Advanced Forecasting System for the Optimum Energy Management of Island Microgrids." Energy Procedia 159, no. : 111-116.

Journal article
Published: 25 January 2019 in Energy Procedia
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The liberation of the Greek electricity market by the law 4426/2015 started a new area for the producers and the consumers of electricity, in order to improve energy efficiency and saving, promotes competitiveness and finally leads to the protection of the environment. The main objective of this work is the development of a forecasting tool with the use of Artificial Neural Networks (ANNs), in order to forecast the energy consumption in the building of Regulation Authority of Energy (RAE) in Athens city, Greece. More specifically, nine different scenarios and artificial neural networks respectively developed and trained in order give a sufficient prediction for the consumption of electricity, the consumption of natural gas during the cold period of the year and finally the chilling loads during the warm period of the year, 24hours ahead in hourly basis. For this purpose, hourly meteorological data from the nearest meteorological station belonging to the National Observatory of Athens as well as energy consumption data from the specific building in hourly bases were used. Results showed that ANNs present a remarkable forecasting ability to predict the energy consumption of the specific building 24hours ahead.

ACS Style

A.L. Katsatos; Kostas P. Moustris. Application of Artificial Neuron Networks as energy consumption forecasting tool in the building of Regulatory Authority of Energy, Athens, Greece. Energy Procedia 2019, 157, 851 -861.

AMA Style

A.L. Katsatos, Kostas P. Moustris. Application of Artificial Neuron Networks as energy consumption forecasting tool in the building of Regulatory Authority of Energy, Athens, Greece. Energy Procedia. 2019; 157 ():851-861.

Chicago/Turabian Style

A.L. Katsatos; Kostas P. Moustris. 2019. "Application of Artificial Neuron Networks as energy consumption forecasting tool in the building of Regulatory Authority of Energy, Athens, Greece." Energy Procedia 157, no. : 851-861.

Journal article
Published: 01 January 2019 in Energy Procedia
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ACS Style

Eleni Papazoglou; Konstantinos P. Moustris; Konstantinos-Stefanos P. Nikas; Panagiotis T. Nastos; John C. Statharas. Assessment of human thermal comfort perception in a non-air-conditioned school building in Athens, Greece. Energy Procedia 2019, 157, 1343 -1352.

AMA Style

Eleni Papazoglou, Konstantinos P. Moustris, Konstantinos-Stefanos P. Nikas, Panagiotis T. Nastos, John C. Statharas. Assessment of human thermal comfort perception in a non-air-conditioned school building in Athens, Greece. Energy Procedia. 2019; 157 ():1343-1352.

Chicago/Turabian Style

Eleni Papazoglou; Konstantinos P. Moustris; Konstantinos-Stefanos P. Nikas; Panagiotis T. Nastos; John C. Statharas. 2019. "Assessment of human thermal comfort perception in a non-air-conditioned school building in Athens, Greece." Energy Procedia 157, no. : 1343-1352.

Original paper
Published: 11 April 2018 in International Journal of Biometeorology
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The present study deals with the development and application of artificial neural network models (ANNs) to estimate the values of a complex human thermal comfort-discomfort index associated with urban heat and cool island conditions inside various urban clusters using as only inputs air temperature data from a standard meteorological station. The index used in the study is the Physiologically Equivalent Temperature (PET) index which requires as inputs, among others, air temperature, relative humidity, wind speed, and radiation (short- and long-wave components). For the estimation of PET hourly values, ANN models were developed, appropriately trained, and tested. Model results are compared to values calculated by the PET index based on field monitoring data for various urban clusters (street, square, park, courtyard, and gallery) in the city of Athens (Greece) during an extreme hot weather summer period. For the evaluation of the predictive ability of the developed ANN models, several statistical evaluation indices were applied: the mean bias error, the root mean square error, the index of agreement, the coefficient of determination, the true predictive rate, the false alarm rate, and the Success Index. According to the results, it seems that ANNs present a remarkable ability to estimate hourly PET values within various urban clusters using only hourly values of air temperature. This is very important in cases where the human thermal comfort-discomfort conditions have to be analyzed and the only available parameter is air temperature.

ACS Style

Konstantinos Moustris; Ioannis X. Tsiros; Areti Tseliou; Panagiotis Nastos. Development and application of artificial neural network models to estimate values of a complex human thermal comfort index associated with urban heat and cool island patterns using air temperature data from a standard meteorological station. International Journal of Biometeorology 2018, 62, 1265 -1274.

AMA Style

Konstantinos Moustris, Ioannis X. Tsiros, Areti Tseliou, Panagiotis Nastos. Development and application of artificial neural network models to estimate values of a complex human thermal comfort index associated with urban heat and cool island patterns using air temperature data from a standard meteorological station. International Journal of Biometeorology. 2018; 62 (7):1265-1274.

Chicago/Turabian Style

Konstantinos Moustris; Ioannis X. Tsiros; Areti Tseliou; Panagiotis Nastos. 2018. "Development and application of artificial neural network models to estimate values of a complex human thermal comfort index associated with urban heat and cool island patterns using air temperature data from a standard meteorological station." International Journal of Biometeorology 62, no. 7: 1265-1274.

Journal article
Published: 01 February 2018 in International Journal of Heat and Fluid Flow
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The determination of the critical Weg number separating the different breakup regimes has been extensively studied in several experimental and numerical works, while empirical and semi-analytical approaches have been proposed to relate the critical Weg number with the Ohl number. Nevertheless, under certain conditions, the Reg number and the density ratio ε may become important. The present work provides a simple but reliable enough methodology to determine the critical Weg number as a function of the aforementioned parameters in an effort to fill this gap in knowledge. It considers the main forces acting on the droplet (aerodynamic, surface tension and viscous) and provides a general criterion for breakup to occur but also for the transition among the different breakup regimes. In this light, the present work proposes the introduction of a new set of parameters named as Weg,eff and Cal monitored in a new breakup plane. This plane provides a direct relation between gas inertia and liquid viscosity forces, while the secondary effects of Reg number and density ratio have been embedded inside the effective Weg number (Weg,eff)

ACS Style

George Strotos; Ilias Malgarinos; Nikos Nikolopoulos; Manolis Gavaises; Konstantinos-Stefanos Nikas; Konstantinos Moustris. Determination of the aerodynamic droplet breakup boundaries based on a total force approach. International Journal of Heat and Fluid Flow 2018, 69, 164 -173.

AMA Style

George Strotos, Ilias Malgarinos, Nikos Nikolopoulos, Manolis Gavaises, Konstantinos-Stefanos Nikas, Konstantinos Moustris. Determination of the aerodynamic droplet breakup boundaries based on a total force approach. International Journal of Heat and Fluid Flow. 2018; 69 ():164-173.

Chicago/Turabian Style

George Strotos; Ilias Malgarinos; Nikos Nikolopoulos; Manolis Gavaises; Konstantinos-Stefanos Nikas; Konstantinos Moustris. 2018. "Determination of the aerodynamic droplet breakup boundaries based on a total force approach." International Journal of Heat and Fluid Flow 69, no. : 164-173.

Journal article
Published: 20 January 2017 in Urban Science
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The main objective of this work is the assessment of the annual number of hospital admissions for respiratory diseases (HARD) due to the exposure to inhalable particulate matter (PM10), within the greater Athens area (GAA), Greece. To achieve this aim, on the one hand, time series of the particulate matter with aerodynamic diameter less than 10 μm (PM10) recorded in six monitoring stations located in the GAA, for a 13-year period 2001–2013, have been statistically analyzed. On the other hand, the AirQ2.2.3 software developed by the World Health Organization (WHO) was used to evaluate adverse health effects by PM10 in the GAA during the examined period. The results show that, during the examined period, PM10 concentrations present a significant decreasing trend. Also, the mean annual HARD cases per 100,000 inhabitants ranged between 20 (suburban area) and 40 (city center area). Approximately 70% of the annual HARD cases are due to city center residents. In all examined sites, a declining trend in the annual number of HARD cases appears. Moreover, a strong relation between the annual number of HARD cases and the annual number of days exceeding the European Union daily PM10 threshold value was found.

ACS Style

Konstantinos P. Moustris; Kleopatra Ntourou; Panagiotis T. Nastos. Estimation of Particulate Matter Impact on Human Health within the Urban Environment of Athens City, Greece. Urban Science 2017, 1, 6 .

AMA Style

Konstantinos P. Moustris, Kleopatra Ntourou, Panagiotis T. Nastos. Estimation of Particulate Matter Impact on Human Health within the Urban Environment of Athens City, Greece. Urban Science. 2017; 1 (1):6.

Chicago/Turabian Style

Konstantinos P. Moustris; Kleopatra Ntourou; Panagiotis T. Nastos. 2017. "Estimation of Particulate Matter Impact on Human Health within the Urban Environment of Athens City, Greece." Urban Science 1, no. 1: 6.

Preprint
Published: 25 December 2016
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The Predicted Mean Vote (PMV) and the Predicted Percentage of Dissatisfied (PPD) indices are used to assess the indoor environment in terms of human thermal comfort-discomfort. In this study, an experimental combined objective and subjective investigation of thermal comfort perception has been performed in students between 16-18 years old, in a non-air-conditioned school building. The objective approach included instrumentation measurements and data processing according to ISO 7730, whereas, the subjective one was based on answers collection following ISO 10551. The study is mainly devoted to the verification of Fanger’s approach in a building, in free running conditions, under a mild (moderate) climate.The comparison between instrumentation data and questionnaire results presented an underestimation of the mean vote, predicting a cooler sensation than the actual one.

ACS Style

Eleni Papazoglou; Konstantinos Moustris; Konstantinos-Stefanos Nikas; Panagiotis Nastos; John Statharas. An Experimental Combined Objective and Subjective Investigation of Thermal Comfort Perception in a Non-Air-Conditioned Lyceum School Building. 2016, 1 .

AMA Style

Eleni Papazoglou, Konstantinos Moustris, Konstantinos-Stefanos Nikas, Panagiotis Nastos, John Statharas. An Experimental Combined Objective and Subjective Investigation of Thermal Comfort Perception in a Non-Air-Conditioned Lyceum School Building. . 2016; ():1.

Chicago/Turabian Style

Eleni Papazoglou; Konstantinos Moustris; Konstantinos-Stefanos Nikas; Panagiotis Nastos; John Statharas. 2016. "An Experimental Combined Objective and Subjective Investigation of Thermal Comfort Perception in a Non-Air-Conditioned Lyceum School Building." , no. : 1.

Preprint
Published: 18 December 2016
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The main objective of this work is to investigate the temporal variation of PM10 concentrations within the urban area of Athens during the years 2001-2015. For this purpose, the time series of the particulate matter with aerodynamic diameter less than 10μm (PM10) is recorded for a 15-year period (2001-2015) in two different monitoring stations located in the urban area of Athens. The results show a totally different behavior of PM10 concentrations between the Athens city center and the suburban areas. It seems that in the city center the main sources of PM10 are traffic and heating systems especially during the cold period of the year. Furthermore, in the city center a significant seasonal variation was found with high concentrations during the cold period of the year and lower concentrations during the warm period of the year. Moreover, it was found that during the weekends, there is a decrease in PM10 concentrations probably due to the fact that majority of people do not use their vehicles. Finally, for both locations a significant temporal decreasing trend of the mean annual PM10 concentrations was found which indicates that during the last years, there have been improvements towards a better air quality.

ACS Style

Hina Najam; Konstantinos Moustris; Panagiotis Nastos. Temporal Variation of Particulate Matter Concentrations within the Urban Area of Athens, Greece. 2016, 1 .

AMA Style

Hina Najam, Konstantinos Moustris, Panagiotis Nastos. Temporal Variation of Particulate Matter Concentrations within the Urban Area of Athens, Greece. . 2016; ():1.

Chicago/Turabian Style

Hina Najam; Konstantinos Moustris; Panagiotis Nastos. 2016. "Temporal Variation of Particulate Matter Concentrations within the Urban Area of Athens, Greece." , no. : 1.

Preprint
Published: 14 November 2016
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The main objective of this work is the assessment of the annual number of hospital admissions for respiratory disease (HARD) due to the exposure to in-healable particulate matter (PM10), within the greater Athens area (GAA), Greece. Towards this aim, the time series of the particulate matter with aerodynamic diameter less than 10μm (PM10) recorded in six monitoring stations located in the GAA, for a 13-year period 2001-2013, is used. Initially, a descriptive statistical treatment of PM10 concentrations took place. Furthermore, the AirQ2.2.3 software developed by the WHO was used to evaluate adverse health effects by PM10 in the GAA during the examined period. The results show that, during the examined period PM10 concentrations present a significant decreasing trend. Also, the mean annual HARD cases per 100,000 inhabitants ranged between 20 (suburban location) and 40 (city centre location). Approximately 70% of the annual HARD cases are due to city centre residents. In all examined locations, a declining trend in the annual number of HARD cases is appeared. Moreover, a strong relation between the annual number of HARD cases and the annual number of days exceeding the European Union daily PM10 threshold value was found.

ACS Style

Konstantinos P. Moustris; Kleopatra Ntourou; Panagiotis Nastos. Estimation of Particulate Matter Impact on Human Health within the Urban Environment of Athens City, Greece. 2016, 1 .

AMA Style

Konstantinos P. Moustris, Kleopatra Ntourou, Panagiotis Nastos. Estimation of Particulate Matter Impact on Human Health within the Urban Environment of Athens City, Greece. . 2016; ():1.

Chicago/Turabian Style

Konstantinos P. Moustris; Kleopatra Ntourou; Panagiotis Nastos. 2016. "Estimation of Particulate Matter Impact on Human Health within the Urban Environment of Athens City, Greece." , no. : 1.

Book chapter
Published: 10 September 2016 in Springer Atmospheric Sciences
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Remote areas are usually fed-in terms of electricity supply-from conventional generators that run on diesel. Recently, there is increasing interest on hybrid RES-based systems, including wind and solar power coupled with energy storage. To this end, optimum dispatching of such configurations is largely based on the capacity of prognostic tools employed in the respective energy management system. Acknowledging this, the aim of this work is the prediction of wind speed, 24-h ahead on an hourly basis, for the optimum operation of hybrid power stations (HPS) with the use of artificial neural networks (ANN). For this purpose, hourly data of wind speed have been used at a specific location (Tilos Island, Greece) where a HPS is going to be installed, including also a wind turbine of 800 kW. More specifically, an ANN which is fed with historical wind and air pressure data was developed in order to predict the wind speed at hub height on an hourly basis for the next 24 h. Results indicate that the proposed methodology gives an adequate forecast of wind speed in order to design an automated wind power information tool that could much facilitate the tasks of the energy management system.

ACS Style

K. P. Moustris; D. Zafirakis; D. H. Alamo; R. J. Nebot Medina; J. K. Kaldellis. 24-h Ahead Wind Speed Prediction for the Optimum Operation of Hybrid Power Stations with the Use of Artificial Neural Networks. Springer Atmospheric Sciences 2016, 409 -414.

AMA Style

K. P. Moustris, D. Zafirakis, D. H. Alamo, R. J. Nebot Medina, J. K. Kaldellis. 24-h Ahead Wind Speed Prediction for the Optimum Operation of Hybrid Power Stations with the Use of Artificial Neural Networks. Springer Atmospheric Sciences. 2016; ():409-414.

Chicago/Turabian Style

K. P. Moustris; D. Zafirakis; D. H. Alamo; R. J. Nebot Medina; J. K. Kaldellis. 2016. "24-h Ahead Wind Speed Prediction for the Optimum Operation of Hybrid Power Stations with the Use of Artificial Neural Networks." Springer Atmospheric Sciences , no. : 409-414.

Book chapter
Published: 10 September 2016 in Springer Atmospheric Sciences
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The main objective of this work is the assessment of the annual number of hospital admissions for respiratory disease (HARD) due to the exposure to inhalable particulate matter (PM10), within the greater Athens area (GAA), Greece. Towards this aim, the time series of the particulate matter with aerodynamic diameter less than 10 μm (PM10) recorded in six monitoring stations located in the GAA, for a 13-year period 2001–2013, is used. In this study AirQ2.2.3 software developed by the WHO, was used to evaluate adverse health effects by PM10 in the GAA during the examined period. The results show that, the mean annual HARD cases per 100,000 inhabitants ranged between 20 (suburban location) and 40 (city centre location). Approximately 70 % of the annual HARD cases are due to city centre residents. In all examined locations, a declining trend in the annual number of HARD cases is appeared. Moreover, a strong relation between the annual number of HARD cases and the annual number of days exceeding the European Union daily PM10 threshold value was found.

ACS Style

K. Ntourou; K. P. Moustris; M. Giannouli; P. T. Nastos; Athanasios. G. Paliatsos. Estimation of Hospital Admissions Respiratory Disease Attributed to PM10 Exposure Using the AirQ Model Within the Greater Athens Area. Springer Atmospheric Sciences 2016, 1105 -1110.

AMA Style

K. Ntourou, K. P. Moustris, M. Giannouli, P. T. Nastos, Athanasios. G. Paliatsos. Estimation of Hospital Admissions Respiratory Disease Attributed to PM10 Exposure Using the AirQ Model Within the Greater Athens Area. Springer Atmospheric Sciences. 2016; ():1105-1110.

Chicago/Turabian Style

K. Ntourou; K. P. Moustris; M. Giannouli; P. T. Nastos; Athanasios. G. Paliatsos. 2016. "Estimation of Hospital Admissions Respiratory Disease Attributed to PM10 Exposure Using the AirQ Model Within the Greater Athens Area." Springer Atmospheric Sciences , no. : 1105-1110.

Conference paper
Published: 01 January 2016 in Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion (MedPower 2016)
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One of the main parameters affecting the reliability of the renewable energy sources (RES) system, compared to the local conventional power station, is the ability to forecast the RES availability for a few hours ahead. To this end, the main objective of this work is the prognosis of the mean, maximum and minimum hourly wind power (WP) 8hours ahead. For this purpose, Artificial Neural Networks (ANN) modeling is applied. For the appropriate training of the developed ANN models hourly meteorological data are used. These data have been recorded by a meteorological mast in Tilos Island, Greece. For the evaluation of the developed ANN forecasting models proper statistical evaluation indices are used. According to the results, the coefficient of the determination ranges from 0.285 up to 0.768 (mean hourly WP), from 0.227 up to 0.798 (maximum hourly WP) and from 0.025 up to 0.398 (minimum hourly WP). Furthermore, the proposed forecasting methodology shows that is able to give sufficient and adequate prognosis of WP by a wind turbine in a specific location 8 hours ahead. This will be a useful tool for the operator of a RES system in order to achieve a better monitoring and a better management of the whole system

ACS Style

K.P. Moustris; D. Zafirakis; K.A. Kavvadias; J.K. Kaldellis. Wind power forecasting using historical data and artificial neural networks modeling. Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion (MedPower 2016) 2016, 1 .

AMA Style

K.P. Moustris, D. Zafirakis, K.A. Kavvadias, J.K. Kaldellis. Wind power forecasting using historical data and artificial neural networks modeling. Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion (MedPower 2016). 2016; ():1.

Chicago/Turabian Style

K.P. Moustris; D. Zafirakis; K.A. Kavvadias; J.K. Kaldellis. 2016. "Wind power forecasting using historical data and artificial neural networks modeling." Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion (MedPower 2016) , no. : 1.