The Experts below are selected from a list of 297 Experts worldwide ranked by ideXlab platform
A. J. Pires - One of the best experts on this subject based on the ideXlab platform.
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on the use of reactive power as an Endogenous Variable in short term load forecasting
International Journal of Energy Research, 2003Co-Authors: Jorge P Santos, Gomes A Martins, A. J. PiresAbstract:In the last decades, short-term load forecasting(STLF) has been the object of particular attention in the power systems field. STLF has been applied almost exclusively to the generation sector, based on Variables, which are transversal to most models. Among the most significant Variables we can find load, expressed as active power (MW), as well as exogenous Variables, such as weather and economy-related ones; although the latter are applied in larger forecasting horizons than STLF. In this paper, the application of STLF to the distribution sector is suggested including inductive reactive power as a forecasting Endogenous Variable. The inclusion of this additional Variable is mainly due to the evidence that correlations between load and weather Variables are tenuous, due to the mild climate of the actual case-study system and the consequent feeble penetration of electrical heating ventilation and air conditioning loads. Artificial neural networks (ANN) have been chosen as the forecasting methodology, with standard feed forward back propagation algorithm, because it is a largely used method with generally considered satisfactory results. Usually the input vector to ANN applied to load forecasting is defined in a discretionary way, mainly based on experience, on engineering judgement criteria and on concern about the ANN dimension, always taking into consideration the apparent (or actually evaluated) correlations within the available data. The approach referred in the paper includes pre-processing the data in order to influence the composition of the input vector in such a way as to reduce the margin of discretion in its definition. A relative entropy analysis has been performed to the time series of each Variable. The paper also includes an illustrative case study. Copyright © 2003 John Wiley & Sons, Ltd.
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On the use of reactive power as an Endogenous Variable in short‐term load forecasting
International Journal of Energy Research, 2003Co-Authors: P. Jorge Santos, A. Gomes Martins, A. J. PiresAbstract:In the last decades, short-term load forecasting(STLF) has been the object of particular attention in the power systems field. STLF has been applied almost exclusively to the generation sector, based on Variables, which are transversal to most models. Among the most significant Variables we can find load, expressed as active power (MW), as well as exogenous Variables, such as weather and economy-related ones; although the latter are applied in larger forecasting horizons than STLF. In this paper, the application of STLF to the distribution sector is suggested including inductive reactive power as a forecasting Endogenous Variable. The inclusion of this additional Variable is mainly due to the evidence that correlations between load and weather Variables are tenuous, due to the mild climate of the actual case-study system and the consequent feeble penetration of electrical heating ventilation and air conditioning loads. Artificial neural networks (ANN) have been chosen as the forecasting methodology, with standard feed forward back propagation algorithm, because it is a largely used method with generally considered satisfactory results. Usually the input vector to ANN applied to load forecasting is defined in a discretionary way, mainly based on experience, on engineering judgement criteria and on concern about the ANN dimension, always taking into consideration the apparent (or actually evaluated) correlations within the available data. The approach referred in the paper includes pre-processing the data in order to influence the composition of the input vector in such a way as to reduce the margin of discretion in its definition. A relative entropy analysis has been performed to the time series of each Variable. The paper also includes an illustrative case study. Copyright © 2003 John Wiley & Sons, Ltd.
James H Stock - One of the best experts on this subject based on the ideXlab platform.
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optimal two sided invariant similar tests for instrumental Variables regression
Econometrica, 2006Co-Authors: Donald W K Andrews, Marcelo J Moreira, James H StockAbstract:This paper considers tests of the parameter on an Endogenous Variable in an instrumental Variables regression model. The focus is on determining tests that have some optimal power properties. We start by considering a model with normally distributed errors and known error covariance matrix. We consider tests that are similar and satisfy a natural rotational invariance condition. We determine a two-sided power envelope for invariant similar tests. This allows us to assess and compare the power properties of tests such as the conditional likelihood ratio (CLR), the Lagrange multiplier, and the Anderson-Rubin tests. We find that the CLR test is quite close to being uniformly most powerful invariant among a class of two-sided tests. Copyright The Econometric Society 2006.
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Optimal Two‐Sided Invariant Similar Tests for Instrumental Variables Regression
Econometrica, 2006Co-Authors: Donald W K Andrews, Marcelo J Moreira, James H StockAbstract:This paper considers tests of the parameter on an Endogenous Variable in an instrumental Variables regression model. The focus is on determining tests that have some optimal power properties. We start by considering a model with normally distributed errors and known error covariance matrix. We consider tests that are similar and satisfy a natural rotational invariance condition. We determine a two-sided power envelope for invariant similar tests. This allows us to assess and compare the power properties of tests such as the conditional likelihood ratio (CLR), the Lagrange multiplier, and the Anderson-Rubin tests. We find that the CLR test is quite close to being uniformly most powerful invariant among a class of two-sided tests. Copyright The Econometric Society 2006.
Donald W K Andrews - One of the best experts on this subject based on the ideXlab platform.
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optimal two sided invariant similar tests for instrumental Variables regression
Econometrica, 2006Co-Authors: Donald W K Andrews, Marcelo J Moreira, James H StockAbstract:This paper considers tests of the parameter on an Endogenous Variable in an instrumental Variables regression model. The focus is on determining tests that have some optimal power properties. We start by considering a model with normally distributed errors and known error covariance matrix. We consider tests that are similar and satisfy a natural rotational invariance condition. We determine a two-sided power envelope for invariant similar tests. This allows us to assess and compare the power properties of tests such as the conditional likelihood ratio (CLR), the Lagrange multiplier, and the Anderson-Rubin tests. We find that the CLR test is quite close to being uniformly most powerful invariant among a class of two-sided tests. Copyright The Econometric Society 2006.
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Optimal Two‐Sided Invariant Similar Tests for Instrumental Variables Regression
Econometrica, 2006Co-Authors: Donald W K Andrews, Marcelo J Moreira, James H StockAbstract:This paper considers tests of the parameter on an Endogenous Variable in an instrumental Variables regression model. The focus is on determining tests that have some optimal power properties. We start by considering a model with normally distributed errors and known error covariance matrix. We consider tests that are similar and satisfy a natural rotational invariance condition. We determine a two-sided power envelope for invariant similar tests. This allows us to assess and compare the power properties of tests such as the conditional likelihood ratio (CLR), the Lagrange multiplier, and the Anderson-Rubin tests. We find that the CLR test is quite close to being uniformly most powerful invariant among a class of two-sided tests. Copyright The Econometric Society 2006.
Marcelo J Moreira - One of the best experts on this subject based on the ideXlab platform.
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optimal two sided invariant similar tests for instrumental Variables regression
Econometrica, 2006Co-Authors: Donald W K Andrews, Marcelo J Moreira, James H StockAbstract:This paper considers tests of the parameter on an Endogenous Variable in an instrumental Variables regression model. The focus is on determining tests that have some optimal power properties. We start by considering a model with normally distributed errors and known error covariance matrix. We consider tests that are similar and satisfy a natural rotational invariance condition. We determine a two-sided power envelope for invariant similar tests. This allows us to assess and compare the power properties of tests such as the conditional likelihood ratio (CLR), the Lagrange multiplier, and the Anderson-Rubin tests. We find that the CLR test is quite close to being uniformly most powerful invariant among a class of two-sided tests. Copyright The Econometric Society 2006.
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Optimal Two‐Sided Invariant Similar Tests for Instrumental Variables Regression
Econometrica, 2006Co-Authors: Donald W K Andrews, Marcelo J Moreira, James H StockAbstract:This paper considers tests of the parameter on an Endogenous Variable in an instrumental Variables regression model. The focus is on determining tests that have some optimal power properties. We start by considering a model with normally distributed errors and known error covariance matrix. We consider tests that are similar and satisfy a natural rotational invariance condition. We determine a two-sided power envelope for invariant similar tests. This allows us to assess and compare the power properties of tests such as the conditional likelihood ratio (CLR), the Lagrange multiplier, and the Anderson-Rubin tests. We find that the CLR test is quite close to being uniformly most powerful invariant among a class of two-sided tests. Copyright The Econometric Society 2006.
Jorge P Santos - One of the best experts on this subject based on the ideXlab platform.
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on the use of reactive power as an Endogenous Variable in short term load forecasting
International Journal of Energy Research, 2003Co-Authors: Jorge P Santos, Gomes A Martins, A. J. PiresAbstract:In the last decades, short-term load forecasting(STLF) has been the object of particular attention in the power systems field. STLF has been applied almost exclusively to the generation sector, based on Variables, which are transversal to most models. Among the most significant Variables we can find load, expressed as active power (MW), as well as exogenous Variables, such as weather and economy-related ones; although the latter are applied in larger forecasting horizons than STLF. In this paper, the application of STLF to the distribution sector is suggested including inductive reactive power as a forecasting Endogenous Variable. The inclusion of this additional Variable is mainly due to the evidence that correlations between load and weather Variables are tenuous, due to the mild climate of the actual case-study system and the consequent feeble penetration of electrical heating ventilation and air conditioning loads. Artificial neural networks (ANN) have been chosen as the forecasting methodology, with standard feed forward back propagation algorithm, because it is a largely used method with generally considered satisfactory results. Usually the input vector to ANN applied to load forecasting is defined in a discretionary way, mainly based on experience, on engineering judgement criteria and on concern about the ANN dimension, always taking into consideration the apparent (or actually evaluated) correlations within the available data. The approach referred in the paper includes pre-processing the data in order to influence the composition of the input vector in such a way as to reduce the margin of discretion in its definition. A relative entropy analysis has been performed to the time series of each Variable. The paper also includes an illustrative case study. Copyright © 2003 John Wiley & Sons, Ltd.