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This paper provides evidence on how the variability of the power produced by a wind farm and its revenue are affected by implementing a ramp-rate limitation strategy and by adding a storage device to the system. The wind farm receives penalties whenever the ramp-rate limitations are not respected and may be supported by batteries to avoid this scenario. In this paper, we model the battery usage as a discrete time homogeneous Markov chain with rewards thanks to which it is possible to simulate the state of the charge of the battery and to calculate the amount of penalties suffered by the wind farm during any period. An application is performed considering the power produced by a hypothetical wind turbine located in Sardinia (Italy) using real wind speed data and electricity prices from a period of 10 years. We applied the concept of ramp-rate limitation on our hourly dataset, studying several limitation scenarios and battery capacities.
Guglielmo D’Amico; Filippo Petroni; Salvatore Vergine. An Analysis of a Storage System for a Wind Farm with Ramp-Rate Limitation. Energies 2021, 14, 4066 .
AMA StyleGuglielmo D’Amico, Filippo Petroni, Salvatore Vergine. An Analysis of a Storage System for a Wind Farm with Ramp-Rate Limitation. Energies. 2021; 14 (13):4066.
Chicago/Turabian StyleGuglielmo D’Amico; Filippo Petroni; Salvatore Vergine. 2021. "An Analysis of a Storage System for a Wind Farm with Ramp-Rate Limitation." Energies 14, no. 13: 4066.
Modelling stock prices has been a research topic for many decades and it is still an open question. Different approaches have been used in the literature, the majority of which can be classified within the so‐called econometric framework and sometimes also referred to as the macro‐to‐micro approach. Another strand of literature relies on the modelling of directly observable quantities, the so‐called micro‐to‐macro approach. Based on this second line of research, we propose a new multivariate stochastic process to model simultaneously price returns, trading volumes and the time interval between changes in trades, price and volume. The proposed model is based on a generalization of semi‐Markov chain models and copulas and is motivated by empirical evidence that the three mentioned variables are correlated and long‐range autocorrelated. Utilizing Monte Carlo simulations, we compared our model with real data from the Italian stock market and show that it can reproduce many empirical pieces of evidence. The proposed model can be used in the field of portfolio optimization, development of risk measure and volatility forecasting.
Guglielmo D'Amico; Filippo Petroni. A micro‐to‐macro approach to returns, volumes and waiting times. Applied Stochastic Models in Business and Industry 2021, 37, 767 -789.
AMA StyleGuglielmo D'Amico, Filippo Petroni. A micro‐to‐macro approach to returns, volumes and waiting times. Applied Stochastic Models in Business and Industry. 2021; 37 (4):767-789.
Chicago/Turabian StyleGuglielmo D'Amico; Filippo Petroni. 2021. "A micro‐to‐macro approach to returns, volumes and waiting times." Applied Stochastic Models in Business and Industry 37, no. 4: 767-789.
The COVID-19 pandemic is having a strong influence in all areas of society, like wealth, economy, travel, lifestyle habits, and, amongst many others, financial and energy markets. The influence in standard energies, like crude oil, and renewable energies markets has been twofold: from one side, the predictability of volatility has strongly decreased; secondly, the linkages of the price time series have been modified. In this paper, by using DCC-GARCH and Price Leadership Share methodology, we can investigate the changes in the influences between standard energies and renewable energies markets by analyzing one-minute time series of West Texas Intermediate crude oil futures contract (WTI), the Brent crude oil futures contract (BRENT), the STOXX Europe 600 oil & gas index (SXEV), and the European renewable energy index (ERIX). Our results confirm volatility spillover between the time series. However, when assessing the accuracy of the predictability of the DCC-GARCH model, the results show that the model fails its prediction in the period of higher instability. Besides, we found that price leadership has been strongly influenced by the virus spreading stages. These results have been obtained by dividing the period between September 2019 and January 2021 into 6 subperiods according to the pandemic stages.
Riccardo De Blasis; Filippo Petroni. Price Leadership and Volatility Linkages between Oil and Renewable Energy Firms during the COVID-19 Pandemic. Energies 2021, 14, 2608 .
AMA StyleRiccardo De Blasis, Filippo Petroni. Price Leadership and Volatility Linkages between Oil and Renewable Energy Firms during the COVID-19 Pandemic. Energies. 2021; 14 (9):2608.
Chicago/Turabian StyleRiccardo De Blasis; Filippo Petroni. 2021. "Price Leadership and Volatility Linkages between Oil and Renewable Energy Firms during the COVID-19 Pandemic." Energies 14, no. 9: 2608.
In this paper, we computed general interval indicators of availability and reliability for systems modelled by time non-homogeneous semi-Markov chains. First, we considered duration-dependent extensions of the Interval Reliability and then, we determined an explicit formula for the availability with a given window and containing a given point. To make the computation of the window availability, an explicit formula was derived involving duration-dependent transition probabilities and the interval reliability function. Both interval reliability and availability functions were evaluated considering the local behavior of the system through the recurrence time processes. The results are illustrated through a numerical example. They show that the considered indicators can describe the duration effects and the age of the multi-state system and be useful in real-life problems.
Guglielmo D’Amico; Raimondo Manca; Filippo Petroni; Dharmaraja Selvamuthu. On the Computation of Some Interval Reliability Indicators for Semi-Markov Systems. Mathematics 2021, 9, 575 .
AMA StyleGuglielmo D’Amico, Raimondo Manca, Filippo Petroni, Dharmaraja Selvamuthu. On the Computation of Some Interval Reliability Indicators for Semi-Markov Systems. Mathematics. 2021; 9 (5):575.
Chicago/Turabian StyleGuglielmo D’Amico; Raimondo Manca; Filippo Petroni; Dharmaraja Selvamuthu. 2021. "On the Computation of Some Interval Reliability Indicators for Semi-Markov Systems." Mathematics 9, no. 5: 575.
In this paper we advance a nonlinear optimization problem for hedging wind power variability by using a dispatchable energy source (DES) like gas. The model considers several important aspects such as modeling of wind power production, electricity price, nonlinear penalization scheme for energy underproduction and interrelations among the considered variables. Results are given in terms of optimal co-generation policy with DES. The optimal policy is interpreted and analyzed in different penalization scenarios and related to a 48 MW hypothetical wind park. The model is suitable for integration of wind energy especially for isolated grids. Some probabilistic results for special moments of a Log-Normal distribution are obtained; they are necessary for the evolution of the optimal policy.
Guglielmo D’Amico; Bice Di Basilio; Filippo Petroni. Hedging the Risk of Wind Power Production Using Dispatchable Energy Source. Stochastics and Quality Control 2021, 1 .
AMA StyleGuglielmo D’Amico, Bice Di Basilio, Filippo Petroni. Hedging the Risk of Wind Power Production Using Dispatchable Energy Source. Stochastics and Quality Control. 2021; ():1.
Chicago/Turabian StyleGuglielmo D’Amico; Bice Di Basilio; Filippo Petroni. 2021. "Hedging the Risk of Wind Power Production Using Dispatchable Energy Source." Stochastics and Quality Control , no. : 1.
The energy produced by a wind farm in a given location and its associated income depends both on the wind characteristics in that location—i.e., speed and direction—and the dynamics of the electricity spot price. Because of the evidence of cross-correlations between wind speed, direction and price series and their lagged series, we aim to assess the income of a hypothetical wind farm located in central Italy when all interactions are considered. To model these cross and auto-correlations efficiently, we apply a high-order multivariate Markov model which includes dependencies from each time series and from a certain level of past values. Besides this, we used the Raftery Mixture Transition Distribution model (MTD) to reduce the number of parameters to get a more parsimonious model. Using data from the MERRA-2 project and from the electricity market in Italy, we estimate the model parameters and validate them through a Monte Carlo simulation. The results show that the simulated income faithfully reproduces the empirical income and that the multivariate model also closely reproduces the cross-correlations between the variables. Therefore, the model can be used to predict the income generated by a wind farm.
Riccardo De Blasis; Giovanni Batista Masala; Filippo Petroni. A Multivariate High-Order Markov Model for the Income Estimation of a Wind Farm. Energies 2021, 14, 388 .
AMA StyleRiccardo De Blasis, Giovanni Batista Masala, Filippo Petroni. A Multivariate High-Order Markov Model for the Income Estimation of a Wind Farm. Energies. 2021; 14 (2):388.
Chicago/Turabian StyleRiccardo De Blasis; Giovanni Batista Masala; Filippo Petroni. 2021. "A Multivariate High-Order Markov Model for the Income Estimation of a Wind Farm." Energies 14, no. 2: 388.
This article deals with the production of energy through photovoltaic (PV) panels. The efficiency and quantity of energy produced by a PV panel depend on both deterministic factors, mainly related to the technical characteristics of the panels, and stochastic factors, essentially the amount of incident solar radiation and some climatic variables that modify the efficiency of solar panels such as temperature and wind speed. The main objective of this work is to estimate the energy production of a PV system with fixed technical characteristics through the modeling of the stochastic factors listed above. Besides, we estimate the economic profitability of the plant, net of taxation or subsidiary payment policies, considered taking into account the hourly spot price curve of electricity and its correlation with solar radiation, via vector autoregressive models. Our investigation ends with a Monte Carlo simulation of the models introduced. We also propose the pricing of some quanto options that allow hedging both the price risk and the volumetric risk.
Laura Casula; Guglielmo D’Amico; Giovanni Masala; Filippo Petroni. Performance estimation of photovoltaic energy production. Letters in Spatial and Resource Sciences 2020, 13, 267 -285.
AMA StyleLaura Casula, Guglielmo D’Amico, Giovanni Masala, Filippo Petroni. Performance estimation of photovoltaic energy production. Letters in Spatial and Resource Sciences. 2020; 13 (3):267-285.
Chicago/Turabian StyleLaura Casula; Guglielmo D’Amico; Giovanni Masala; Filippo Petroni. 2020. "Performance estimation of photovoltaic energy production." Letters in Spatial and Resource Sciences 13, no. 3: 267-285.
Because of the stochastic nature of wind turbines, the output power management of wind power generation (WPG) is a fundamental challenge for the integration of wind energy systems into either power systems or microgrids (i.e., isolated systems consisting of local wind energy systems only) in operation and planning studies. In general, a wind energy system can refer to both one wind farm consisting of a number of wind turbines and a given number of wind farms sited at the area in question. In power systems (microgrid) planning, a WPG should be quantified for the determination of the expected power flows and the analysis of the adequacy of power generation. Concerning this operation, the WPG should be incorporated into an optimal operation decision process, as well as unit commitment and economic dispatch studies. In both cases, the probabilistic investigation of WPG leads to a multivariate uncertainty analysis problem involving correlated random variables (the output power of either wind turbines that constitute wind farm or wind farms sited at the area in question) that follow different distributions. This paper advances a multivariate model of WPG for a wind farm that relies on indexed semi-Markov chains (ISMC) to represent the output power of each wind energy system in question and a copula function to reproduce the spatial dependencies of the energy systems’ output power. The ISMC model can reproduce long-term memory effects in the temporal dependence of turbine power and thus understand, as distinct cases, the plethora of Markovian models. Using copula theory, we incorporate non-linear spatial dependencies into the model that go beyond linear correlations. Some copula functions that are frequently used in applications are taken into consideration in the paper; i.e., Gumbel copula, Gaussian copula, and the t-Student copula with different degrees of freedom. As a case study, we analyze a real dataset of the output powers of six wind turbines that constitute a wind farm situated in Poland. This dataset is compared with the synthetic data generated by the model thorough the calculation of three adequacy indices commonly used at the first hierarchical level of power system reliability studies; i.e., loss of load probability (LOLP), loss of load hours (LOLH) and loss of load expectation (LOLE). The results will be compared with those obtained using other models that are well known in the econometric field; i.e., vector autoregressive models (VAR).
Guglielmo D’Amico; Giovanni Masala; Filippo Petroni; Robert Adam Sobolewski. Managing Wind Power Generation via Indexed Semi-Markov Model and Copula. Energies 2020, 13, 4246 .
AMA StyleGuglielmo D’Amico, Giovanni Masala, Filippo Petroni, Robert Adam Sobolewski. Managing Wind Power Generation via Indexed Semi-Markov Model and Copula. Energies. 2020; 13 (16):4246.
Chicago/Turabian StyleGuglielmo D’Amico; Giovanni Masala; Filippo Petroni; Robert Adam Sobolewski. 2020. "Managing Wind Power Generation via Indexed Semi-Markov Model and Copula." Energies 13, no. 16: 4246.
This paper presents an insurance contract that the supplier of wind power may subscribe to with an insurance company in order to immunize his/her revenue against the volatility of wind power and prices. Based on empirical evidence, we found that wind power and electricity prices are correlated. Then, we adopted a joint stochastic process to model both time series, which is based on indexed semi-Markov chains for the wind power generation process and on a general Markovian process for the electricity price process. Using a joint stochastic model allows the insurance company to compute the fair premium that the wind power producer has to pay in order to hedge the risk against inadequate revenues. Recursive type equations are obtained for the prospective mathematical reserves of the insurance contract. The model and the validity of the results are illustrated through a real data application.
Guglielmo D’Amico; Fulvio Gismondi; Filippo Petroni. Insurance Contracts for Hedging Wind Power Uncertainty. Mathematics 2020, 8, 1376 .
AMA StyleGuglielmo D’Amico, Fulvio Gismondi, Filippo Petroni. Insurance Contracts for Hedging Wind Power Uncertainty. Mathematics. 2020; 8 (8):1376.
Chicago/Turabian StyleGuglielmo D’Amico; Fulvio Gismondi; Filippo Petroni. 2020. "Insurance Contracts for Hedging Wind Power Uncertainty." Mathematics 8, no. 8: 1376.
In this paper, we propose a semi-Markov chain to model the salary levels of participants in a pension scheme. The aim of the models is to understand the evolution in time of the salary of active workers in order to implement it in the construction of the actuarial technical balance sheet. It is worth mentioning that the level of the contributions in a pension scheme is directly proportional to the incomes of the active workers; in almost all cases, it is a percentage of the worker’s incomes. As a consequence, an adequate modeling of the salary evolution is essential for the determination of the contributions paid to the fund and thus for the determination of the fund’s sustainability, especially currently, when all jobs and salaries are subject to changes due to digitalization, ICT, innovation, etc. The model is applied to a large dataset of a real compulsory Italian pension scheme of the first pillar. The semi-Markovian hypothesis is tested, and the advantages with respect to Markov chain models are assessed.
Guglielmo D'amico; Ada Lika; Filippo Petroni. Risk Management of Pension Fund: A Model for Salary Evolution. International Journal of Financial Studies 2019, 7, 44 .
AMA StyleGuglielmo D'amico, Ada Lika, Filippo Petroni. Risk Management of Pension Fund: A Model for Salary Evolution. International Journal of Financial Studies. 2019; 7 (3):44.
Chicago/Turabian StyleGuglielmo D'amico; Ada Lika; Filippo Petroni. 2019. "Risk Management of Pension Fund: A Model for Salary Evolution." International Journal of Financial Studies 7, no. 3: 44.
The major drawback of wind energy relies in its variability in time, which necessitates specific strategies to be settled. One such strategy can be the coordination of wind power production with a co-located power generation of dispatchable energy source (DES), e.g., thermal power station, combined heat and power plant, gas turbine or compressed air energy storage. In this paper, we consider an energy producer that generates power by means of a wind park and of a DES and sells the produced energy to an isolated grid. We determine the optimal quantity of energy produced by a DES, given the unit cost of this energy, that a power producer should buy and use to hedge against the risk inherent in the production of energy through wind turbines. We determine the optimal quantity by solving a static optimization problem taking into account the possible dependence between the amount of energy produced by wind turbines and electricity prices by using a copula function. Several particular cases are studied that allow the determination of the optimal solution in an analytical closed form. Finally, a numerical example concerning a real 48 MW wind farm located in Poland and Polish Power Exchange shows the possibility of implementing the model in real-life problems.
Guglielmo D’Amico; Filippo Petroni; Robert Adam Sobolewski. Optimal Control of a Dispatchable Energy Source for Wind Energy Management. Stochastics and Quality Control 2019, 34, 19 -34.
AMA StyleGuglielmo D’Amico, Filippo Petroni, Robert Adam Sobolewski. Optimal Control of a Dispatchable Energy Source for Wind Energy Management. Stochastics and Quality Control. 2019; 34 (1):19-34.
Chicago/Turabian StyleGuglielmo D’Amico; Filippo Petroni; Robert Adam Sobolewski. 2019. "Optimal Control of a Dispatchable Energy Source for Wind Energy Management." Stochastics and Quality Control 34, no. 1: 19-34.
In this paper we study the high frequency dynamic of financial volumes of traded stocks by using a semi-Markov approach. More precisely we assume that the intraday logarithmic change of volume is described by a weighted-indexed semi-Markov chain model. Based on this assumptions we show that this model is able to reproduce several empirical facts about volume evolution like time series dependence, intra-daily periodicity and volume asymmetry. Results have been obtained from a real data application to high frequency data from the Italian stock market from first of January 2007 until end of December 2010.
Guglielmo D’Amico; Fulvio Gismondi; Filippo Petroni. A New Approach to the Modeling of Financial Volumes. Stochastic Processes and Applications 2018, 363 -373.
AMA StyleGuglielmo D’Amico, Fulvio Gismondi, Filippo Petroni. A New Approach to the Modeling of Financial Volumes. Stochastic Processes and Applications. 2018; ():363-373.
Chicago/Turabian StyleGuglielmo D’Amico; Fulvio Gismondi; Filippo Petroni. 2018. "A New Approach to the Modeling of Financial Volumes." Stochastic Processes and Applications , no. : 363-373.
The main purpose of this work is to investigate the relation between some measures in information theory and the accuracy of volatility forecasting using a model of asset returns. First we highlight the dependence between volatility forecasting and entropy and then we determine the relation between predictability and volatility. The study is conducted using a database of 65 stocks of the Dow Jones Composite Average from 1973 to 2014 and by computing the daily volatility of the market index. To this end we use the standard GARCH approach to model and forecast the daily volatility. The main result of this paper is the establishment of a relationship between the accuracy of the volatility forecast and the entropy of the time series of price returns. Since the entropy changes in time, before computing a forecast of the volatility it is recommended to compute the entropy of the time series that furnishes an important indicator on the limit of successive volatility forecast.
Guglielmo D’Amico; Fulvio Gismondi; Filippo Petroni; Flavio Prattico. Stock market daily volatility and information measures of predictability. Physica A: Statistical Mechanics and its Applications 2018, 518, 22 -29.
AMA StyleGuglielmo D’Amico, Fulvio Gismondi, Filippo Petroni, Flavio Prattico. Stock market daily volatility and information measures of predictability. Physica A: Statistical Mechanics and its Applications. 2018; 518 ():22-29.
Chicago/Turabian StyleGuglielmo D’Amico; Fulvio Gismondi; Filippo Petroni; Flavio Prattico. 2018. "Stock market daily volatility and information measures of predictability." Physica A: Statistical Mechanics and its Applications 518, no. : 22-29.
This paper uses an Indexed Markov Chain to model high frequency price returns of quoted rms. Introducing an Index process permits consideration of endogenous market volatility, and two important stylized facts of financial time series can be taken into account: long memory and volatility clustering. This paper rst proposes a method to optimally determine the state space of the Index process, which is based on a change-point approach for Markov chains. Furthermore, we provide an explicit formula for the probability distribution function of the rst change of state of the Index process. Results are illustrated with an application to intra-day firm prices.
Guglielmo D’Amico; Ada Lika; Filippo Petroni. Change point dynamics for financial data: an indexed Markov chain approach. Annals of Finance 2018, 15, 247 -266.
AMA StyleGuglielmo D’Amico, Ada Lika, Filippo Petroni. Change point dynamics for financial data: an indexed Markov chain approach. Annals of Finance. 2018; 15 (2):247-266.
Chicago/Turabian StyleGuglielmo D’Amico; Ada Lika; Filippo Petroni. 2018. "Change point dynamics for financial data: an indexed Markov chain approach." Annals of Finance 15, no. 2: 247-266.
Guglielmo D’Amico; Filippo Petroni. Copula based multivariate semi-Markov models with applications in high-frequency finance. European Journal of Operational Research 2018, 267, 765 -777.
AMA StyleGuglielmo D’Amico, Filippo Petroni. Copula based multivariate semi-Markov models with applications in high-frequency finance. European Journal of Operational Research. 2018; 267 (2):765-777.
Chicago/Turabian StyleGuglielmo D’Amico; Filippo Petroni. 2018. "Copula based multivariate semi-Markov models with applications in high-frequency finance." European Journal of Operational Research 267, no. 2: 765-777.
Performing a maintenance of wind energy system components under good wind conditions may lead to energy not served and finally – to financial losses. The best starting time of preventive maintenance will be, that reduces the energy not served in most. To find this time, a decision model is desired, where many circumstances should be taken into account, i.e. (i) the number and the order of components to be maintained, (ii) component maintenance duration, and (iii) wind turbine(s) output power prediction. Usually, preventive maintenance is planned a few days or weeks in advance. One of the decision problem representations can be influence diagram that enables choosing a decision alternative that has the lowest expected utility (energy not served). The paper presents an decision model that can support decisions-making on starting time of preventive maintenance and maintenance order of wind energy system components. The model relies on influence diagram. The conditional probability distribution of a chance nodes of the diagram are obtained relying on Bayesian networks (BN), whereas the utilities of value node in the diagram are calculated thanks to the second order semi-Markov chains (SMC). The example shows the application of the model in real case of two wind turbines located in Poland. Both the parameters of Bayesian network nodes and semi-Markov chain are derived from real data recorded by SCADA system of the both turbines and weather forecast.
Robert Adam Sobolewski; Guglielmo D’Amico; Filippo Petroni. Decision Model of Wind Turbines Maintenance Planning. Advances in Intelligent Systems and Computing 2018, 440 -450.
AMA StyleRobert Adam Sobolewski, Guglielmo D’Amico, Filippo Petroni. Decision Model of Wind Turbines Maintenance Planning. Advances in Intelligent Systems and Computing. 2018; ():440-450.
Chicago/Turabian StyleRobert Adam Sobolewski; Guglielmo D’Amico; Filippo Petroni. 2018. "Decision Model of Wind Turbines Maintenance Planning." Advances in Intelligent Systems and Computing , no. : 440-450.
A new branch based on Markov processes is developing in the recent literature of financial time series modeling. In this paper, an Indexed Markov Chain has been used to model high frequency price returns of quoted firms. The peculiarity of this type of model is that through the introduction of an Index process it is possible to consider the market volatility endogenously and two very important stylized facts of financial time series can be taken into account: long memory and volatility clustering. In this paper, first we propose a method for the optimal determination of the state space of the Index process which is based on a change-point approach for Markov chains. Furthermore we provide an explicit formula for the probability distribution function of the first change of state of the index process. Results are illustrated with an application to intra-day prices of a quoted Italian firm from January $1^{st}$, 2007 to December $31^{st}$ 2010.
Guglielmo D'amico; Ada Lika; Filippo Petroni. Indexed Markov Chains for financial data: testing for the number of states of the index process. 2018, 1 .
AMA StyleGuglielmo D'amico, Ada Lika, Filippo Petroni. Indexed Markov Chains for financial data: testing for the number of states of the index process. . 2018; ():1.
Chicago/Turabian StyleGuglielmo D'amico; Ada Lika; Filippo Petroni. 2018. "Indexed Markov Chains for financial data: testing for the number of states of the index process." , no. : 1.
A new branch based on Markov processes is developing in the recent literature of financial time series modeling. In this paper, an Indexed Markov Chain has been used to model high frequency price returns of quoted firms. The peculiarity of this type of model is that through the introduction of an Index process it is possible to consider the market volatility endogenously and two very important stylized facts of financial time series can be taken into account: long memory and volatility clustering. In this paper, first we propose a method for the optimal determination of the state space of the Index process which is based on a change-point approach for Markov chains. Furthermore we provide an explicit formula for the probability distribution function of the first change of state of the index process. Results are illustrated with an application to intra-day prices of a quoted Italian firm from January $1^{st}$, 2007 to December $31^{st}$ 2010.
Guglielmo D'amico; Ada Lika; Filippo Petroni. Indexed Markov Chains for financial data: testing for the number of states of the index process. 2018, 1 .
AMA StyleGuglielmo D'amico, Ada Lika, Filippo Petroni. Indexed Markov Chains for financial data: testing for the number of states of the index process. . 2018; ():1.
Chicago/Turabian StyleGuglielmo D'amico; Ada Lika; Filippo Petroni. 2018. "Indexed Markov Chains for financial data: testing for the number of states of the index process." , no. : 1.
In this paper we study the high frequency dynamic of financial volumes of traded stocks by using a semi-Markov approach. More precisely we assume that the intraday logarithmic change of volume is described by a weighted-indexed semi-Markov chain model. Based on this assumptions we show that this model is able to reproduce several empirical facts about volume evolution like time series dependence, intra-daily periodicity and volume asymmetry. Results have been obtained from a real data application to high frequency data from the Italian stock market from first of January 2007 until end of December 2010.
Guglielmo D'amico; Filippo Petroni. A new approach to the modeling of financial volumes. 2017, 1 .
AMA StyleGuglielmo D'amico, Filippo Petroni. A new approach to the modeling of financial volumes. . 2017; ():1.
Chicago/Turabian StyleGuglielmo D'amico; Filippo Petroni. 2017. "A new approach to the modeling of financial volumes." , no. : 1.
Maintenance of a wind turbine is a combination of all technical, administrative and managerial actions intended to retain it in, or restore it to, a state in which the turbine is able to generate power. This paper presents an influence diagram to estimate the expected utility that represents wind turbine energy to be produced given period of time in the future. The conditional probability distribution of a chance node of the diagram is obtained relying on Bayesian networks, whereas the utilities of value node are calculated thanks to the second order semi-Markov chains. The example shows the application of the models in the real case of one wind turbine E48 by Enercon located in northern part of Poland. Both Bayesian network parameters and kernel of semi-Markov chain are derived from real data recorded by SCADA system of the turbine and weather forecast.
Guglielmo D’Amico; Filippo Petroni; Robert Adam Sobolewski; Wojciech Zamojski; Jacek Mazurkiewicz; Jarosław Sugier; Tomasz Walkowiak; Janusz Kacprzyk. Maintenance of Wind Turbine Scheduling Based on Output Power Data and Wind Forecast. Advances in Intelligent Systems and Computing 2017, 582, 106 -117.
AMA StyleGuglielmo D’Amico, Filippo Petroni, Robert Adam Sobolewski, Wojciech Zamojski, Jacek Mazurkiewicz, Jarosław Sugier, Tomasz Walkowiak, Janusz Kacprzyk. Maintenance of Wind Turbine Scheduling Based on Output Power Data and Wind Forecast. Advances in Intelligent Systems and Computing. 2017; 582 ():106-117.
Chicago/Turabian StyleGuglielmo D’Amico; Filippo Petroni; Robert Adam Sobolewski; Wojciech Zamojski; Jacek Mazurkiewicz; Jarosław Sugier; Tomasz Walkowiak; Janusz Kacprzyk. 2017. "Maintenance of Wind Turbine Scheduling Based on Output Power Data and Wind Forecast." Advances in Intelligent Systems and Computing 582, no. : 106-117.