Analysis of Moving Averages of BWEs Actual Capacity

2018 ◽  
pp. 622-632
Author(s):  
Leszek Jurdziak
Keyword(s):  
Moving Averages

scholarly journals On Adaptive Moving Average Algorithms for the Application of the Conservative Power Theory in Systems with Variable Frequency

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1201
Author(s):  
Daniel dos Santos Mota ◽  
Elisabetta Tedeschi
Keyword(s):  
Time Delay ◽  
Moving Average ◽  
Voltage Source ◽  
Moving Averages ◽  
Nonlinear Load ◽  
Power Theory ◽  
Variable Time Delay ◽  
Variable Time ◽  
Conservative Power Theory ◽  
Conservative Power

The Conservative Power Theory (CPT) emerged in recent decades as a theoretical framework for coping with harmonically distorted and unbalanced electric networks of ac power systems with a high participation of converter interfaced loads and generation. The CPT measurements are intrinsically linked to moving averages (MA) over one period of the grid. If the CPT is to be used in a low-inertia isolated-grid scenario, which is subjected to frequency variations, adaptive moving averages (AMA) are necessary. This paper reviews an efficient way of computing MAs and turns it into an adaptive one. It shows that an easily available variable time delay block, from MATLAB, causes steady-state errors in the measurements when the grid frequency varies. A new variable time delay block is, thus, proposed. Nonetheless, natural pulsations in the instantaneous power slip through MAs when the discrete moving average window does not fit perfectly the continuously varying period of the grid. A method consisting of weighing two MAs is reviewed and a new and effective hybrid AMA is proposed. The CPT transducers with the different choices of AMAs are compared via computer simulations of a single-phase voltage source feeding either a linear or a nonlinear load.

Simple Multivariate Conditional Covariance Dynamics Using Hyperbolically Weighted Moving Averages

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Hiroyuki Kawakatsu
Keyword(s):  
Moving Average ◽  
Curse Of Dimensionality ◽  
Moving Averages ◽  
Conditional Covariance ◽  
Arch Models ◽  
Empirical Application ◽  
Proposed Model ◽  
Out Of Sample ◽  
Weight Model ◽  
Weighted Moving Average

AbstractThis paper considers a class of multivariate ARCH models with scalar weights. A new specification with hyperbolic weighted moving average (HWMA) is proposed as an analogue of the EWMA model. Despite the restrictive dynamics of a scalar weight model, the proposed model has a number of advantages that can deal with the curse of dimensionality. The empirical application illustrates that the (pseudo) out-of-sample multistep forecasts can be surprisingly more accurate than those from the DCC model.

scholarly journals Statistical Modeling and Prediction for Tourism Economy Using Dendritic Neural Network

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Ying Yu ◽  
Yirui Wang ◽  
Shangce Gao ◽  
Zheng Tang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Rapid Development ◽  
Seasonal Trend ◽  
Moving Averages ◽  
Forecasting Methods ◽  
Tourism Economy ◽  
The Impact ◽  
D Model

With the impact of global internationalization, tourism economy has also been a rapid development. The increasing interest aroused by more advanced forecasting methods leads us to innovate forecasting methods. In this paper, the seasonal trend autoregressive integrated moving averages with dendritic neural network model (SA-D model) is proposed to perform the tourism demand forecasting. First, we use the seasonal trend autoregressive integrated moving averages model (SARIMA model) to exclude the long-term linear trend and then train the residual data by the dendritic neural network model and make a short-term prediction. As the result showed in this paper, the SA-D model can achieve considerably better predictive performances. In order to demonstrate the effectiveness of the SA-D model, we also use the data that other authors used in the other models and compare the results. It also proved that the SA-D model achieved good predictive performances in terms of the normalized mean square error, absolute percentage of error, and correlation coefficient.

scholarly journals Analyse spectrale des filtres de la méthode de désaisonnalisation X-11-ARMMI

2009 ◽  
Vol 56 (3) ◽  
pp. 465-484 ◽  
Author(s):  
Pierre A. Cholette
Keyword(s):  
The Other ◽  
Moving Averages ◽  
Analyse Spectrale ◽  
Short Series ◽  
Other Hand ◽  
Desirable Effect

Abstract This paper examines the changes produced on the seasonal estimates of the X-11-ARIMA method (Dagum, 1980), when (1) the two by twelve preliminary trend-cycle estimator is replaced by that of Cholette (1979a) (now optional in the program) and when (2) the "end" weights of the seasonal three by three and three by five moving averages are modified. The results show that the first substitution has a small but desirable effect which becomes more substantial for short series and for those with fast cyclical movements. On the other hand, the suggested replacement of the seasonal end weights would entail considerable improvements for series with stable seasonality or with much irregularity. Both modifications do not affect the properties of X-11-ARIMA in the "centre" of long series.

scholarly journals Training Load and Baseline Characteristics Associated With New Injury/Pain Within an Endurance Sporting Population: A Prospective Study

2019 ◽  
Vol 14 (5) ◽  
pp. 590-597 ◽  
Author(s):  
Richard Johnston ◽  
Roisin Cahalan ◽  
Laura Bonnett ◽  
Matthew Maguire ◽  
Alan Nevill ◽  
...  
Keyword(s):  
Confidence Interval ◽  
Frailty Model ◽  
Training Load ◽  
Moving Averages ◽  
Shared Frailty Model ◽  
Shared Frailty ◽  
History Of ◽  
Baseline Characteristics ◽  
A Prospective Study ◽  
Exponentially Weighted

Purpose: To determine the association between training-load (TL) factors, baseline characteristics, and new injury and/or pain (IP) risk in an endurance sporting population (ESP). Methods: Ninety-five ESP participants from running, triathlon, swimming, cycling, and rowing disciplines initially completed a questionnaire capturing baseline characteristics. TL and IP data were submitted weekly over a 52-wk study period. Cumulative TL factors, acute:chronic workload ratios, and exponentially weighted moving averages were calculated. A shared frailty model was used to explore time to new IP and association to TL factors and baseline characteristics. Results: 92.6% of the ESP completed all 52 wk of TL and IP data. The following factors were associated with the lowest risk of a new IP episode: (a) a low to moderate 7-d lag exponentially weighted moving averages (0.8–1.3: hazard ratio [HR] = 1.21; 95% confidence interval [CI], 1.01–1.44; P = .04); (b) a low to moderate 7-d lag weekly TL (1200–1700 AU: HR = 1.38; 95% CI, 1.15–1.65; P < .001); (c) a moderate to high 14-d lag 4-weekly cumulative TL (5200–8000 AU: HR = 0.33; 95% CI, 0.21–0.50; P < .001); and (d) a low number of previous IP episodes in the preceding 12 mo (1 previous IP episode: HR = 1.11; 95% CI, 1.04–1.17; P = .04). Conclusions: To minimize new IP risk, an ESP should avoid high spikes in acute TL while maintaining moderate to high chronic TLs. A history of previous IP should be considered when prescribing TLs. The demonstration of a lag between a TL factor and its impact on new IP risk may have important implications for future ESP TL analysis.

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