Computation and uses of the energy flows for the distribution system analysis over time intervals

2002 ◽  
Author(s):  
E. Bompard ◽  
E. Carpaneto ◽  
G. Chicco ◽  
R. Napoli ◽  
F. Piglione
Keyword(s):  
Distribution System ◽  
System Analysis ◽  
Time Intervals ◽  
Energy Flows ◽  
Over Time

scholarly journals Temporal load resolution impact on PV/grid system energy flows

2018 ◽  
Vol 240 ◽  
pp. 04003 ◽  
Author(s):  
Marek Jaszczur ◽  
Qusay Hassan ◽  
Janusz Teneta
Keyword(s):  
Temporal Resolution ◽  
System Analysis ◽  
Estimation Error ◽  
Energy System ◽  
Photovoltaic System ◽  
Acquisition Time ◽  
Storage Unit ◽  
Time Step ◽  
Energy Flows ◽  
Pv Grid

In this paper, an investigation of the electrical load temporal resolution on the PV/Grid energy system flows, and self-consumption is done in order to determine the optimum parameters for modelling and simulation. The analysed PV/Grid power systems include a photovoltaic system with the nominal power of Pmax@STC=1.5, 2.5, 3.5 kW without storage unit connected to the grid. The results show that the temporal load resolution may have a high impact on energy flows as well as can be a critical issue for the system analysis accuracy even for the single household. It has been found that the load temporal resolution for energy consumption of 1-min yields reliable results, while data resolutions of 5 and 15 min are still sufficient, however, in that case, the daily electrical energy flows and in consequence energy self-consumption estimation error for selected days may exceed 15%. Acquisition time step longer than 15-minutes may increase error above 20% and from the designer’s point of view should not be used. The high and low temporal resolution experimental data of the electricity consumption (load) for a household are available in digital form on the author’s website http://home.agh.edu.pl/jaszczur.

Distribution System Analysis and the Future Smart Grid

2011 ◽  
Vol 47 (6) ◽  
pp. 2343-2350 ◽  
Author(s):  
Robert F. Arritt ◽  
Roger C. Dugan
Keyword(s):  
Smart Grid ◽  
Distribution System ◽  
System Analysis ◽  
The Future

scholarly journals Hubungan Perilaku Perdagangan Investor Dengan Volume Perdagangan Di Bursa Efek Indonesia

2009 ◽  
Vol 2 (1) ◽  
Author(s):  
Ghazali Syamni
Keyword(s):  
Trading Volume ◽  
Stock Exchange ◽  
Time Interval ◽  
Time Intervals ◽  
Using Data ◽  
Changes Over Time ◽  
Relationship Of ◽  
The Relationship ◽  
Order Pattern ◽  
Over Time

This paper examines the relationship of behavior trading investor using data detailed transaction history-corporate edition demand and order history in Indonesia Stock Exchange during period of March, April and May 2005. Peculiarly, behavior placing of investor order at trading volume. The result of this paper indicates that trading volume order pattern to have pattern U shape. The pattern happened that investors have strong desires to places order at the opening and close of compared to in trading periods. While the largest orders are of market at the opening indicates that investor is more conservatively when opening, where many orders when opening has not happened transaction to match. In placing order both of investor does similar strategy. By definition, informed investors’ orders more large than uninformed investors. If comparison of order examined hence both investors behavior relatively changes over time. But, statistically shows there is not ratio significant. This implies behavior trading of informed investors and uninformed investors stable relative over time. The result from regression analysis indicates that informed investors to correlate at trading volume in all time intervals, but not all uninformed investors correlates in every time interval. This imply investor order inform is more can explain trading volume pattern compared to uninformed investor order in Indonesia Stock Exchange. Finally, result of regression also finds that order status match has greater role determines trading volume pattern intraday especially informed buy match and informed sale match. While amend, open and withdraw unable to have role to determine intraday trading volume pattern.

scholarly journals Fill ventilation of heated corn grain

2020 ◽  
pp. 74-77
Author(s):  
Mikhail Gennadievich Zagoruyko ◽  
Sergey Anatolyevich Pavlov
Keyword(s):  
Experimental Data ◽  
Distribution System ◽  
Air Flow ◽  
Air Distribution ◽  
The Dead ◽  
Over Time ◽  
Corn Grain

The grain masses of the first and subsequent batches, the equivalent coefficient of the dead gap of the air distribution system are calculated, the expressions for calculating the air flow for these batches and the experimental data on the change in moisture and temperature of the grain over time are given.

Active Optimum Control of Orthotropic Plates Using Piezoelectric Stiffeners

1993 ◽  
Author(s):  
Victor Birman ◽  
Sarp Adali
Keyword(s):  
Time Interval ◽  
Short Time Interval ◽  
Bending Moments ◽  
Optimum Control ◽  
Orthotropic Plates ◽  
Time Intervals ◽  
Control Forces ◽  
Short Time ◽  
Alternating Voltage ◽  
Over Time

Abstract Active control of orthotropic plates subjected to an impulse loading is considered. The dynamic response is minimized using in-plane forces or bending moments induced by piezoelectric stiffeners bonded to the opposite surfaces of the plate and placed symmetrically with respect to the middle plane. The control forces and moments are activated by a piece-wise constant alternating voltage with varying switch-over time intervals. The magnitude of voltage is bounded while the switch-over time intervals are constantly adjusted to achieve an optimum control. Numerical examples presented in the paper demonstrate the effectiveness of the method and the possibility of reducing the vibrations to very small amplitudes within a short time interval which is in the order of a second.

Finding Minimal Infrequent Elements in Multi-Dimensional Data Defined over Partially Ordered Sets and its Applications

2010 ◽  
pp. 98-116
Author(s):  
Khaled M. Elbassioni
Keyword(s):  
Association Rules ◽  
Partially Ordered Set ◽  
Quantitative Data ◽  
Partially Ordered Sets ◽  
Threshold Value ◽  
Ordered Sets ◽  
Time Intervals ◽  
Partially Ordered ◽  
The Given ◽  
Over Time

The authors consider databases in which each attribute takes values from a partially ordered set (poset). This allows one to model a number of interesting scenarios arising in different applications, including quantitative databases, taxonomies, and databases in which each attribute is an interval representing the duration of a certain event occurring over time. A natural problem that arises in such circumstances is the following: given a database D and a threshold value t, find all collections of “generalizations” of attributes which are “supported” by less than t transactions from D. They call such collections infrequent elements. Due to monotonicity, they can reduce the output size by considering only minimal infrequent elements. We study the complexity of finding all minimal infrequent elements for some interesting classes of posets. The authors show how this problem can be applied to mining association rules in different types of databases, and to finding “sparse regions” or “holes” in quantitative data or in databases recording the time intervals during which a re-occurring event appears over time. Their main focus will be on these applications rather than on the correctness or analysis of the given algorithms.

scholarly journals Usage of the Term Big Data in Biomedical Publications: A Text Mining Approach

2019 ◽  
Vol 3 (1) ◽  
pp. 13
Author(s):  
Allard van Altena ◽  
Perry Moerland ◽  
Aeilko Zwinderman ◽  
Sílvia Olabarriaga
Keyword(s):  
Big Data ◽  
Operating Characteristic ◽  
Roc Curves ◽  
Distinct Type ◽  
Research Field ◽  
Time Intervals ◽  
Learning Classifier ◽  
Biomedical Field ◽  
Biomedical Publications ◽  
Over Time

In this study, we attempt to assess the value of the term Big Data when used by researchers in their publications. For this purpose, we systematically collected a corpus of biomedical publications that use and do not use the term Big Data. These documents were used as input to a machine learning classifier to determine how well they can be separated into two groups and to determine the most distinguishing classification features. We generated 100 classifiers that could correctly distinguish between Big Data and non-Big Data documents with an area under the Receiver Operating Characteristic (ROC) curve of 0.96. The differences between the two groups were characterized by terms specific to Big Data themes—such as `computational’, `mining’, and `challenges’—and also by terms that indicate the research field, such as `genomics’. The ROC curves when plotted for various time intervals showed no difference over time. We conclude that there is a detectable and stable difference between publications that use the term Big Data and those that do not. Furthermore, the use of the term Big Data within a publication seems to indicate a distinct type of research in the biomedical field. Therefore, we conclude that value can be attributed to the term Big Data when used in a publication and this value has not changed over time.

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