S. Pappas - Academia.edu (original) (raw)

Papers by S. Pappas

Research paper thumbnail of Hybrid wind speed forecasting using ARMA models and support vector machines

An alternative electric power source, such as wind power, has to be both reliable and autonomous.... more An alternative electric power source, such as wind power, has to be both reliable and autonomous. An accurate wind speed forecasting method plays the key role in achieving the aforementioned properties and also is a valuable tool in overcoming a variety of economic and technical problems connected to wind power production. The method proposed is based on the reformulation of the problem in the standard state space form and on implementing a bank of Kalman filters (KF), each fitting an ARMA model of different order. The proposed method is to be applied to a greenhouse unit which incorporates an automatized use of renewable energy sources including wind speed power.

Research paper thumbnail of Multivariate Arma Order Estimation via Multi-Model Partition Theory

summarizes the parametric model uncertainty into an unknown, finite dimensional parameter vector ... more summarizes the parametric model uncertainty into an unknown, finite dimensional parameter vector whose values are assumed to lie within a known set of finite cardinality. It is not restricted to the Gaussian case and it is also applicable to on line/adaptive operation. By applying this method a new computationally efficient order selection criterion for Multivariate ARMA models will be proposed, developed and justified as an extension to the model order selection criterion for MV AR (AutoRegressive) models . Finally it will be shown that the proposed method is also successful in tracking model order changes in real time.

Research paper thumbnail of Adaptive MV ARMA identification under the presence of noise

Page 1. Chapter 17 Adaptive MV ARMA Identification Under the Presence of Noise Stylianos Sp. Papp... more Page 1. Chapter 17 Adaptive MV ARMA Identification Under the Presence of Noise Stylianos Sp. Pappas, Vassilios C. Moussas, and Sokratis K. Katsikas Abstract An adaptive method for simultaneous order estimation and parameter ...

Research paper thumbnail of Application of the multi-model partitioning theory for simultaneous order and parameter estimation of multivariate ARMA models

International Journal of Modelling, Identification and Control, 2008

In this paper, a study on how to perform simultaneous order and parameter estimation of multivari... more In this paper, a study on how to perform simultaneous order and parameter estimation of multivariate (MV) ARMA (autoregressive moving average) models under the presence of noise is addressed. The proposed method, which is computationally efficient, is an extension of a previously presented method for MV AR models and is based on the well established and widely applied multi-model partitioning theory. A series of computer simulations indicate that the method is infallible in selecting the correct model order in very few steps. The simultaneous estimation of the multivariate ARMA parameters is also another benefit of the proposed method. The results are compared with two other established order selection criteria namely Akaike's Information Crieterion (AIC) and Schwarz's Bayesian Information Criterion (BIC). Finally, it is shown that the method is also successful in tracking model order changes, in real time.

Research paper thumbnail of Multivariate Arma Order Estimation via Multi-Model Partition Theory

summarizes the parametric model uncertainty into an unknown, finite dimensional parameter vector ... more summarizes the parametric model uncertainty into an unknown, finite dimensional parameter vector whose values are assumed to lie within a known set of finite cardinality. It is not restricted to the Gaussian case and it is also applicable to on line/adaptive operation. By applying this method a new computationally efficient order selection criterion for Multivariate ARMA models will be proposed, developed and justified as an extension to the model order selection criterion for MV AR (AutoRegressive) models . Finally it will be shown that the proposed method is also successful in tracking model order changes in real time.

Research paper thumbnail of Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models

Energy, 2008

This study addresses the problem of modeling the electricity demand loads in Greece. The provided... more This study addresses the problem of modeling the electricity demand loads in Greece. The provided actual load data is deseasonilized and an AutoRegressive Moving Average (ARMA) model is fitted on the data off-line, using the Akaike Corrected Information Criterion (AICC). The developed model fits the data in a successful manner. Difficulties occur when the provided data includes noise or errors and also when an on-line/adaptive modeling is required. In both cases and under the assumption that the provided data can be represented by an ARMA model, simultaneous order and parameter estimation of ARMA models under the presence of noise are performed. The produced results indicate that the proposed method, which is based on the multi-model partitioning theory, tackles successfully the studied problem. For validation purposes the produced results are compared with three other established order selection criteria, namely AICC, Akaike's Information Criterion (AIC) and Schwarz's Bayesian Information Criterion (BIC). The developed model could be useful in the studies that concern electricity consumption and electricity prices forecasts.

Research paper thumbnail of EVOLUTIONARY DESIGN OF A GENETIC BASED SELF ORGANIZING NEURAL NETWORK FOR WIND SPEED PREDICTION

epsmso.gr

Wind speed prediction is considered as the most crucial task in the implementation of an alternat... more Wind speed prediction is considered as the most crucial task in the implementation of an alternative but at the same time reliable and autonomous electric power source. Accurate wind speed forecasting methods are a significant tool in overcoming a variety of economic and technical problems connected to wind power production. This paper addresses the problem of wind speed forecasting by applying a technique based on the Genetics-Based Self-Organising Network (GBSON) method. Real data were used and real cases were tested based on the measurements of the wind speed provided by Vestas Hellas. The wind speed time series prediction is reformulated to a system identification problem, where the input is the past values of the time series and the output the future values of a time series. This method has been applied in the past to various time series prediction problems giving satisfactory results.

Research paper thumbnail of Modeling of the grounding resistance variation using ARMA models

Simulation Modelling Practice and Theory, 2008

This study addresses the problem of modeling the variation of the grounding resistance during the... more This study addresses the problem of modeling the variation of the grounding resistance during the year. An AutoRegressive Moving Average (ARMA) model is fitted (off-line) on the provided actual data using the Corrected Akaike Information Criterion (AICC). The developed model is shown to fit the data in a successful manner. Difficulties occur when the provided data includes noise or errors and also when an on line/adaptive modeling is required. In both cases, and under the assumption that the provided data can be represented by an ARMA model, simultaneous order and parameter estimation of ARMA models under the presence of noise is necessary. In this paper, a new method based on the multi-model partitioning theory which is also applicable to on line/adaptive operation, is used for the solution of the above mentioned problem. The simulations show that the proposed method succeeds in selecting the correct ARMA model order and estimates the parameters accurately in very few steps and even with a small sample size. For validation purposes the method introduced is compared with three other established order selection criteria presenting very good results. The proposed method can be extremely useful in the studies of electrical engineer designers, since the variation of the grounding resistance during the year affects significantly power systems performance and must be definitely considered.

Research paper thumbnail of Electricity demand load forecasting of the Hellenic power system using an ARMA model

Electric Power Systems Research, 2010

Effective modeling and forecasting requires the efficient use of the information contained in the... more Effective modeling and forecasting requires the efficient use of the information contained in the available data so that essential data properties can be extracted and projected into the future. As far as electricity demand load forecasting is concerned time series analysis has the advantage of being statistically adaptive to data characteristics compared to econometric methods which quite often are subject to errors and uncertainties in model specification and knowledge of causal variables. This paper presents a new method for electricity demand load forecasting using the multi-model partitioning theory and compares its performance with three other well established time series analysis techniques namely Corrected Akaike Information Criterion (AICC), Akaike's Information Criterion (AIC) and Schwarz's Bayesian Information Criterion (BIC). The suitability of the proposed method is illustrated through an application to actual electricity demand load of the Hellenic power system, proving the reliability and the effectiveness of the method and making clear its usefulness in the studies that concern electricity consumption and electricity prices forecasts.

Research paper thumbnail of Adaptive load forecasting of the Hellenic electric grid

Journal of Zhejiang University SCIENCE A, 2008

Designers are required to plan for future expansion and also to estimate the grid's future utiliz... more Designers are required to plan for future expansion and also to estimate the grid's future utilization. This means that an effective modeling and forecasting technique, which will use efficiently the information contained in the available data, is required, so that important data properties can be extracted and projected into the future. This study proposes an adaptive method based on the multi-model partitioning algorithm (MMPA), for short-term electricity load forecasting using real data. The grid's utilization is initially modeled using a multiplicative seasonal ARIMA (autoregressive integrated moving average) model. The proposed method uses past data to learn and model the normal periodic behavior of the electric grid. Either ARMA (autoregressive moving average) or state-space models can be used for the load pattern modeling. Load anomalies such as unexpected peaks that may appear during the summer or unexpected faults (blackouts) are also modeled. If the load pattern does not match the normal behavior of the load, an anomaly is detected and, furthermore, when the pattern matches a known case of anomaly, the type of anomaly is identified. Real data were used and real cases were tested based on the measurement loads of the Hellenic Public Power Cooperation S.A., Athens, Greece. The applied adaptive multi-model filtering algorithm identifies successfully both normal periodic behavior and any unusual activity of the electric grid. The performance of the proposed method is also compared to that produced by the ARIMA model.

Research paper thumbnail of Hybrid wind speed forecasting using ARMA models and support vector machines

An alternative electric power source, such as wind power, has to be both reliable and autonomous.... more An alternative electric power source, such as wind power, has to be both reliable and autonomous. An accurate wind speed forecasting method plays the key role in achieving the aforementioned properties and also is a valuable tool in overcoming a variety of economic and technical problems connected to wind power production. The method proposed is based on the reformulation of the problem in the standard state space form and on implementing a bank of Kalman filters (KF), each fitting an ARMA model of different order. The proposed method is to be applied to a greenhouse unit which incorporates an automatized use of renewable energy sources including wind speed power.

Research paper thumbnail of Multivariate Arma Order Estimation via Multi-Model Partition Theory

summarizes the parametric model uncertainty into an unknown, finite dimensional parameter vector ... more summarizes the parametric model uncertainty into an unknown, finite dimensional parameter vector whose values are assumed to lie within a known set of finite cardinality. It is not restricted to the Gaussian case and it is also applicable to on line/adaptive operation. By applying this method a new computationally efficient order selection criterion for Multivariate ARMA models will be proposed, developed and justified as an extension to the model order selection criterion for MV AR (AutoRegressive) models . Finally it will be shown that the proposed method is also successful in tracking model order changes in real time.

Research paper thumbnail of Adaptive MV ARMA identification under the presence of noise

Page 1. Chapter 17 Adaptive MV ARMA Identification Under the Presence of Noise Stylianos Sp. Papp... more Page 1. Chapter 17 Adaptive MV ARMA Identification Under the Presence of Noise Stylianos Sp. Pappas, Vassilios C. Moussas, and Sokratis K. Katsikas Abstract An adaptive method for simultaneous order estimation and parameter ...

Research paper thumbnail of Application of the multi-model partitioning theory for simultaneous order and parameter estimation of multivariate ARMA models

International Journal of Modelling, Identification and Control, 2008

In this paper, a study on how to perform simultaneous order and parameter estimation of multivari... more In this paper, a study on how to perform simultaneous order and parameter estimation of multivariate (MV) ARMA (autoregressive moving average) models under the presence of noise is addressed. The proposed method, which is computationally efficient, is an extension of a previously presented method for MV AR models and is based on the well established and widely applied multi-model partitioning theory. A series of computer simulations indicate that the method is infallible in selecting the correct model order in very few steps. The simultaneous estimation of the multivariate ARMA parameters is also another benefit of the proposed method. The results are compared with two other established order selection criteria namely Akaike's Information Crieterion (AIC) and Schwarz's Bayesian Information Criterion (BIC). Finally, it is shown that the method is also successful in tracking model order changes, in real time.

Research paper thumbnail of Multivariate Arma Order Estimation via Multi-Model Partition Theory

summarizes the parametric model uncertainty into an unknown, finite dimensional parameter vector ... more summarizes the parametric model uncertainty into an unknown, finite dimensional parameter vector whose values are assumed to lie within a known set of finite cardinality. It is not restricted to the Gaussian case and it is also applicable to on line/adaptive operation. By applying this method a new computationally efficient order selection criterion for Multivariate ARMA models will be proposed, developed and justified as an extension to the model order selection criterion for MV AR (AutoRegressive) models . Finally it will be shown that the proposed method is also successful in tracking model order changes in real time.

Research paper thumbnail of Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models

Energy, 2008

This study addresses the problem of modeling the electricity demand loads in Greece. The provided... more This study addresses the problem of modeling the electricity demand loads in Greece. The provided actual load data is deseasonilized and an AutoRegressive Moving Average (ARMA) model is fitted on the data off-line, using the Akaike Corrected Information Criterion (AICC). The developed model fits the data in a successful manner. Difficulties occur when the provided data includes noise or errors and also when an on-line/adaptive modeling is required. In both cases and under the assumption that the provided data can be represented by an ARMA model, simultaneous order and parameter estimation of ARMA models under the presence of noise are performed. The produced results indicate that the proposed method, which is based on the multi-model partitioning theory, tackles successfully the studied problem. For validation purposes the produced results are compared with three other established order selection criteria, namely AICC, Akaike's Information Criterion (AIC) and Schwarz's Bayesian Information Criterion (BIC). The developed model could be useful in the studies that concern electricity consumption and electricity prices forecasts.

Research paper thumbnail of EVOLUTIONARY DESIGN OF A GENETIC BASED SELF ORGANIZING NEURAL NETWORK FOR WIND SPEED PREDICTION

epsmso.gr

Wind speed prediction is considered as the most crucial task in the implementation of an alternat... more Wind speed prediction is considered as the most crucial task in the implementation of an alternative but at the same time reliable and autonomous electric power source. Accurate wind speed forecasting methods are a significant tool in overcoming a variety of economic and technical problems connected to wind power production. This paper addresses the problem of wind speed forecasting by applying a technique based on the Genetics-Based Self-Organising Network (GBSON) method. Real data were used and real cases were tested based on the measurements of the wind speed provided by Vestas Hellas. The wind speed time series prediction is reformulated to a system identification problem, where the input is the past values of the time series and the output the future values of a time series. This method has been applied in the past to various time series prediction problems giving satisfactory results.

Research paper thumbnail of Modeling of the grounding resistance variation using ARMA models

Simulation Modelling Practice and Theory, 2008

This study addresses the problem of modeling the variation of the grounding resistance during the... more This study addresses the problem of modeling the variation of the grounding resistance during the year. An AutoRegressive Moving Average (ARMA) model is fitted (off-line) on the provided actual data using the Corrected Akaike Information Criterion (AICC). The developed model is shown to fit the data in a successful manner. Difficulties occur when the provided data includes noise or errors and also when an on line/adaptive modeling is required. In both cases, and under the assumption that the provided data can be represented by an ARMA model, simultaneous order and parameter estimation of ARMA models under the presence of noise is necessary. In this paper, a new method based on the multi-model partitioning theory which is also applicable to on line/adaptive operation, is used for the solution of the above mentioned problem. The simulations show that the proposed method succeeds in selecting the correct ARMA model order and estimates the parameters accurately in very few steps and even with a small sample size. For validation purposes the method introduced is compared with three other established order selection criteria presenting very good results. The proposed method can be extremely useful in the studies of electrical engineer designers, since the variation of the grounding resistance during the year affects significantly power systems performance and must be definitely considered.

Research paper thumbnail of Electricity demand load forecasting of the Hellenic power system using an ARMA model

Electric Power Systems Research, 2010

Effective modeling and forecasting requires the efficient use of the information contained in the... more Effective modeling and forecasting requires the efficient use of the information contained in the available data so that essential data properties can be extracted and projected into the future. As far as electricity demand load forecasting is concerned time series analysis has the advantage of being statistically adaptive to data characteristics compared to econometric methods which quite often are subject to errors and uncertainties in model specification and knowledge of causal variables. This paper presents a new method for electricity demand load forecasting using the multi-model partitioning theory and compares its performance with three other well established time series analysis techniques namely Corrected Akaike Information Criterion (AICC), Akaike's Information Criterion (AIC) and Schwarz's Bayesian Information Criterion (BIC). The suitability of the proposed method is illustrated through an application to actual electricity demand load of the Hellenic power system, proving the reliability and the effectiveness of the method and making clear its usefulness in the studies that concern electricity consumption and electricity prices forecasts.

Research paper thumbnail of Adaptive load forecasting of the Hellenic electric grid

Journal of Zhejiang University SCIENCE A, 2008

Designers are required to plan for future expansion and also to estimate the grid's future utiliz... more Designers are required to plan for future expansion and also to estimate the grid's future utilization. This means that an effective modeling and forecasting technique, which will use efficiently the information contained in the available data, is required, so that important data properties can be extracted and projected into the future. This study proposes an adaptive method based on the multi-model partitioning algorithm (MMPA), for short-term electricity load forecasting using real data. The grid's utilization is initially modeled using a multiplicative seasonal ARIMA (autoregressive integrated moving average) model. The proposed method uses past data to learn and model the normal periodic behavior of the electric grid. Either ARMA (autoregressive moving average) or state-space models can be used for the load pattern modeling. Load anomalies such as unexpected peaks that may appear during the summer or unexpected faults (blackouts) are also modeled. If the load pattern does not match the normal behavior of the load, an anomaly is detected and, furthermore, when the pattern matches a known case of anomaly, the type of anomaly is identified. Real data were used and real cases were tested based on the measurement loads of the Hellenic Public Power Cooperation S.A., Athens, Greece. The applied adaptive multi-model filtering algorithm identifies successfully both normal periodic behavior and any unusual activity of the electric grid. The performance of the proposed method is also compared to that produced by the ARIMA model.