Extract Transfer Load Considerations of Temporal Data Sources to Data Warehouses

2019 ◽  
pp. 170-176
Author(s):  
Thanapol Phungtua-Eng ◽  
Suphamit Chittayasothorn
Keyword(s):  
Data Sources ◽  
Temporal Data ◽  
Data Warehouses

scholarly journals Photovoltaic Power Forecasting: Assessment of the Impact of Multiple Sources of Spatio-Temporal Data on Forecast Accuracy

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1432
Author(s):  
Xwégnon Ghislain Agoua ◽  
Robin Girard ◽  
Georges Kariniotakis
Keyword(s):  
Satellite Images ◽  
Forecast Accuracy ◽  
Absolute Error ◽  
Data Sources ◽  
Test Case ◽  
Temporal Data ◽  
Multiple Sources ◽  
Forecasting Error ◽  
Spatio Temporal ◽  
The Impact

The efficient integration of photovoltaic (PV) production in energy systems is conditioned by the capacity to anticipate its variability, that is, the capacity to provide accurate forecasts. From the classical forecasting methods in the state of the art dealing with a single power plant, the focus has moved in recent years to spatio-temporal approaches, where geographically dispersed data are used as input to improve forecasts of a site for the horizons up to 6 h ahead. These spatio-temporal approaches provide different performances according to the data sources available but the question of the impact of each source on the actual forecasting performance is still not evaluated. In this paper, we propose a flexible spatio-temporal model to generate PV production forecasts for horizons up to 6 h ahead and we use this model to evaluate the effect of different spatial and temporal data sources on the accuracy of the forecasts. The sources considered are measurements from neighboring PV plants, local meteorological stations, Numerical Weather Predictions, and satellite images. The evaluation of the performance is carried out using a real-world test case featuring a high number of 136 PV plants. The forecasting error has been evaluated for each data source using the Mean Absolute Error and Root Mean Square Error. The results show that neighboring PV plants help to achieve around 10% reduction in forecasting error for the first three hours, followed by satellite images which help to gain an additional 3% all over the horizons up to 6 h ahead. The NWP data show no improvement for horizons up to 6 h but is essential for greater horizons.

Temporal Data Warehouses

2008 ◽  
pp. 185-249
Author(s):  
Elzbieta Malinowski ◽  
Esteban Zimányi
Keyword(s):  
Temporal Data ◽  
Data Warehouses

scholarly journals Temporal ranges and ancestry in the hominin fossil record: The case of Australopithecus sediba

2018 ◽  
Vol 114 (3/4) ◽  
Author(s):  
Chris Robinson ◽  
Timothy L. Campbell ◽  
Susanne Cote ◽  
Darryl J. de Ruiter
Keyword(s):  
Phylogenetic Relationships ◽  
Fossil Record ◽  
Thought Experiment ◽  
Mammalian Species ◽  
Data Sources ◽  
Evolutionary Relationships ◽  
Temporal Data ◽  
Genus Homo ◽  
Using Data ◽  
Fossil Hominin

In attempting to resolve the phylogenetic relationships of fossil taxa, researchers can use evidence from two sources – morphology and known temporal ranges. For most taxa, the available evidence is stronger for one of these data sources. We examined the limitations of temporal data for reconstructing hominin evolutionary relationships, specifically focusing on the hypothesised ancestor–descendant relationship between Australopithecus sediba and the genus Homo. Some have implied that because the only known specimens of A. sediba are dated to later than the earliest fossils attributed to Homo, the former species is precluded from being ancestral to the latter. However, A. sediba is currently known from one site dated to 1.98 Ma and, thus, its actual temporal range is unknown. Using data from the currently known temporal ranges of fossil hominin species, and incorporating dating error in the analysis, we estimate that the average hominin species’ temporal range is ~0.97 Myr, which is lower than most figures suggested for mammalian species generally. Using this conservative figure in a thought experiment in which the Malapa specimens are hypothesised to represent the last appearance date, the middle of the temporal range, and first appearance date for the species, the first appearance date of A. sediba would be 2.95, 2.47 and 1.98 Ma, respectively. As these scenarios are all equally plausible, and 2.95 Ma predates the earliest specimens that some have attributed to Homo, we cannot refute the hypothesis that the species A. sediba is ancestral to our genus based solely on currently available temporal data.

Difference Operators in Simulation Data Warehouses

2017 ◽  
Vol 12 (2) ◽  
pp. 347-354 ◽  
Author(s):  
Jing Zhao ◽  
◽  
Yoshiharu Ishikawa ◽  
Yukiko Wakita ◽  
Kento Sugiura
Keyword(s):  
Difference Operator ◽  
Difference Operators ◽  
Observation Data ◽  
Temporal Data ◽  
Data Warehouses ◽  
Simulation Data ◽  
Tsunami Disaster ◽  
Spatio Temporal ◽  
The Difference ◽  
Mass Evacuation

In analyzing observation data and simulation results, there are frequent demands for comparing more than one data on the same subject to detect any differences between them. For example, comparison of observation data for an object in a certain spatial domain at different times or comparison of spatial simulation data with different parameters. Therefore, this paper proposes the difference operator in spatio-temporal data warehouses, which store temporal and spatial observation data and simulation data. The requirements for the difference operator are summarized, and the approaches to implement them are presented. In addition, the proposed approach is applied to the mass evacuation of simulation data in a tsunami disaster, and its effectiveness is verified. Extensions of the difference operator and their applications are also discussed.

Designing Spatial and Temporal Data Warehouses

2008 ◽  
pp. 315-352
Author(s):  
Elzbieta Malinowski ◽  
Esteban Zimányi
Keyword(s):  
Temporal Data ◽  
Data Warehouses

scholarly journals Indexing spatio-temporal data warehouses

2003 ◽  
Author(s):  
D. Papadias ◽  
Yufei Tao ◽  
P. Kanis ◽  
Jun Zhang
Keyword(s):  
Temporal Data ◽  
Data Warehouses ◽  
Spatio Temporal

Exploiting Temporal Correlation in Temporal Data Warehouses

2005 ◽  
pp. 662-674 ◽  
Author(s):  
Ying Feng ◽  
Hua-Gang Li ◽  
Divyakant Agrawal ◽  
Amr El Abbadi
Keyword(s):  
Temporal Correlation ◽  
Temporal Data ◽  
Data Warehouses
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