Sampling Strategies for Representative Time Series in Load Flow Calculations

2018 ◽  
pp. 27-48
Author(s):  
Janosch Henze ◽  
Stephan Kutzner ◽  
Bernhard Sick
Keyword(s):  
Time Series ◽  
Load Flow ◽  
Sampling Strategies

scholarly journals Characterization of Export Regimes in Concentration–Discharge Plots via an Advanced Time-Series Model and Event-Based Sampling Strategies

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1723
Author(s):  
Ana Gonzalez-Nicolas ◽  
Marc Schwientek ◽  
Michael Sinsbeck ◽  
Wolfgang Nowak
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Time Series Model ◽  
Data Series ◽  
Additional Parameter ◽  
Sampling Strategies ◽  
Discharge Rates ◽  
Regression Approach ◽  
Event Based ◽  
Stochastic Time Series

Currently, the export regime of a catchment is often characterized by the relationship between compound concentration and discharge in the catchment outlet or, more specifically, by the regression slope in log-concentrations versus log-discharge plots. However, the scattered points in these plots usually do not follow a plain linear regression representation because of different processes (e.g., hysteresis effects). This work proposes a simple stochastic time-series model for simulating compound concentrations in a river based on river discharge. Our model has an explicit transition parameter that can morph the model between chemostatic behavior and chemodynamic behavior. As opposed to the typically used linear regression approach, our model has an additional parameter to account for hysteresis by including correlation over time. We demonstrate the advantages of our model using a high-frequency data series of nitrate concentrations collected with in situ analyzers in a catchment in Germany. Furthermore, we identify event-based optimal scheduling rules for sampling strategies. Overall, our results show that (i) our model is much more robust for estimating the export regime than the usually used regression approach, and (ii) sampling strategies based on extreme events (including both high and low discharge rates) are key to reducing the prediction uncertainty of the catchment behavior. Thus, the results of this study can help characterize the export regime of a catchment and manage water pollution in rivers at lower monitoring costs.

Intervention time series analysis — sampling strategies

1990 ◽  
Vol 9 (1) ◽  
pp. 83-94 ◽  
Author(s):  
H.M.J. Goldschmidt ◽  
L.J.F.Ten Voorde ◽  
J.F. Leijten ◽  
R.W. Lent
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Sampling Strategies ◽  
Series Analysis

scholarly journals Performance and uncertainties of TSS stormwater sampling strategies from online time series

2018 ◽  
Vol 78 (6) ◽  
pp. 1407-1416
Author(s):  
Santiago Sandoval ◽  
Jean-Luc Bertrand-Krajewski ◽  
Nicolas Caradot ◽  
Thomas Hofer ◽  
Günter Gruber
Keyword(s):  
Time Series ◽  
Suspended Solids ◽  
Sampling Strategy ◽  
Sampling Strategies ◽  
Sampling Errors ◽  
Time Step ◽  
Sewer System ◽  
Time Intervals ◽  
Combined Sewer ◽  
One Year

Abstract The event mean concentrations (EMCs) that would have been obtained by four different stormwater sampling strategies are simulated by using total suspended solids (TSS) and flowrate time series (about one minute time-step and one year of data). These EMCs are compared to the reference EMCs calculated by considering the complete time series. The sampling strategies are assessed with datasets from four catchments: (i) Berlin, Germany, combined sewer overflow (CSO); (ii) Graz, Austria, CSO; (iii) Chassieu, France, separate sewer system; and (iv) Ecully, France, CSO. A sampling strategy in which samples are collected at constant time intervals over the rainfall event and sampling volumes are pre-set as proportional to the runoff volume discharged between two consecutive sample leads to the most representative results. Recommended sampling time intervals are of 5 min for Berlin and Chassieu (resp. 100 and 185 ha area) and 10 min for Graz and Ecully (resp. 335 and 245 ha area), with relative sampling errors between 7% and 20% and uncertainties in sampling errors of about 5%. Uncertainties related to sampling volumes, TSS laboratory analyses and beginning/ending of rainstorm events are reported as the most influent sources in the uncertainties of sampling errors and EMCs.

scholarly journals Sampling strategies and post-processing methods for increasing the time resolution of organic aerosol measurements requiring long sample collection times

2016 ◽  
Author(s):  
R. L. Modini ◽  
S. Takahama
Keyword(s):  
Time Series ◽  
Measurement Error ◽  
Time Resolution ◽  
Urban Site ◽  
Sampling Strategies ◽  
Post Processing ◽  
Sample Collection ◽  
Random Measurement Error ◽  
Processing Methods ◽  
Random Measurement

Abstract. The composition and properties of atmospheric Organic Aerosols (OAs) change on timescales of minutes to hours. However, some important OA characterization techniques typically require greater than a few hours of sample collection time (e.g. Fourier Transform Infrared (FTIR) spectroscopy). In this study we have performed numerical modeling to investigate and compare sample collection strategies and post-processing methods for increasing the time resolution of OA measurements requiring long sample collection times. Specifically, we modeled the measurement of Hydrocarbon-like OA (HOA) and Oxygenated OA (OOA) concentrations at a polluted urban site in Mexico City, and investigated how to construct hourly-resolved time series from samples collected for 4, 6, and 8 h. We modeled two sampling strategies – sequential and staggered sampling – and a range of post-processing methods including interpolation and deconvolution. The results indicated that relative to the more sophisticated and costly staggered sampling methods, linear interpolation between sequential measurements is a surprisingly effective method for increasing time resolution. Additional error can be added to a time series constructed in this manner if a suboptimal sequential sampling schedule is chosen. Staggering measurements is one way to avoid this effect. There is little to be gained from deconvolving staggered measurements, except at very low values of random measurement error (< 5 %). Assuming 20 % random measurement error, one can expect average recovery errors of 1.33–2.81 μg m−3 when using 4–8 h long sequential and staggered samples to measure time series of concentration values ranging from 0.13–29.16 μg m−3. For 4 h samples, 19–47 % of this total error can be attributed to the process of increasing time resolution alone, depending on the method used, meaning that measurement precision would only be improved by 0.30–0.75 μg m−3 if samples could be collected over 1 h instead of 4 h. Devising a suitable sampling strategy and post-processing method is a good approach for increasing the time resolution of measurements requiring long sample collection times.

scholarly journals Sampling strategies and post-processing methods for increasing the time resolution of organic aerosol measurements requiring long sample-collection times

2016 ◽  
Vol 9 (7) ◽  
pp. 3337-3354
Author(s):  
Rob L. Modini ◽  
Satoshi Takahama
Keyword(s):  
Time Series ◽  
Measurement Error ◽  
Time Resolution ◽  
Urban Site ◽  
Sampling Strategies ◽  
Post Processing ◽  
Sample Collection ◽  
Random Measurement Error ◽  
Processing Methods ◽  
Random Measurement

Abstract. The composition and properties of atmospheric organic aerosols (OAs) change on timescales of minutes to hours. However, some important OA characterization techniques typically require greater than a few hours of sample-collection time (e.g., Fourier transform infrared (FTIR) spectroscopy). In this study we have performed numerical modeling to investigate and compare sample-collection strategies and post-processing methods for increasing the time resolution of OA measurements requiring long sample-collection times. Specifically, we modeled the measurement of hydrocarbon-like OA (HOA) and oxygenated OA (OOA) concentrations at a polluted urban site in Mexico City, and investigated how to construct hourly resolved time series from samples collected for 4, 6, and 8 h. We modeled two sampling strategies – sequential and staggered sampling – and a range of post-processing methods including interpolation and deconvolution. The results indicated that relative to the more sophisticated and costly staggered sampling methods, linear interpolation between sequential measurements is a surprisingly effective method for increasing time resolution. Additional error can be added to a time series constructed in this manner if a suboptimal sequential sampling schedule is chosen. Staggering measurements is one way to avoid this effect. There is little to be gained from deconvolving staggered measurements, except at very low values of random measurement error (< 5 %). Assuming 20 % random measurement error, one can expect average recovery errors of 1.33–2.81 µg m−3 when using 4–8 h-long sequential and staggered samples to measure time series of concentration values ranging from 0.13–29.16 µg m−3. For 4 h samples, 19–47 % of this total error can be attributed to the process of increasing time resolution alone, depending on the method used, meaning that measurement precision would only be improved by 0.30–0.75 µg m−3 if samples could be collected over 1 h instead of 4 h. Devising a suitable sampling strategy and post-processing method is a good approach for increasing the time resolution of measurements requiring long sample-collection times.

Analytic time series load flow

2018 ◽  
Vol 82 ◽  
pp. 3886-3899 ◽  
Author(s):  
Haidar Samet ◽  
Morteza Khorshidsavar
Keyword(s):  
Time Series ◽  
Load Flow

Probabilistic load flow with load estimation using time series techniques and neural networks

2017 ◽  
Vol 10 ◽  
pp. 1153-1161
Author(s):  
Fernando Mesa ◽  
Pedro Pablo Cardenas Alzate ◽  
Carlos Alberto Rodriguez Varela
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Load Flow ◽  
Load Estimation ◽  
Probabilistic Load Flow ◽  
Probabilistic Load

scholarly journals Short Term Electric Load Forecasting of Kathmandu Valley of Nepal using Artificial Neural Network

2018 ◽  
Vol 1 (1) ◽  
pp. 43-48
Author(s):  
Binod Bhandari ◽  
Shree Raj Shakya ◽  
Ajay Kumar Jha
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Load Flow ◽  
Kathmandu Valley ◽  
Electric Load ◽  
Short Term ◽  
Load Demand ◽  
The Neural Network ◽  
Artificial Neural

Decision making in the energy sector has to be based on accurate forecasts of the load demand. Short-term forecasting, which forms the focus of this paper, gives a day ahead hourly forecast of electric load. This forecast can help to make important decisions in the field of scheduling, contingency analysis, load flow analysis, preventing imbalance in the power generation and load demand, load switching strategies, thus leading to greater network reliability and power quality. A method called Artificial Neural Network is used to anticipate the future load of Kathmandu Valley of Nepal. The Neural Network is build, trained with historical data along with seven different input variables and used for prediction of day ahead 24 hours load. The output is validated with the real Load collected from NEA. In addition, forecasting is performed by some other time series methods as well, and whose output are compared with that of neural network. The range of Mean Absolute Deviation for four different time series models lied between 1.50-2.59. When the errors were calculated in terms of MSE and MAPE the range of these values were found to be in between 2.59-7.78, and 1.61- 5.07 respectively. The Artificial Neural Network proved to be the more accurate forecast method when the results are compared in terms of error measurements with a MAD having 1.23, MSE having 1.79 and MAPE having 1.17. The Neural Network proved to be more accurate method comparatively with satisfactory minimum error.

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