On the Impact of Temporal Data Resolution on the Accuracy of Non-Intrusive Load Monitoring

The paper researches the impact of the input data resolution on the solution of optimal allocation and power management of controllable and non-controllable renewable energy sources distributed generation in the distribution power system. Computational intelligence techniques and co-simulation approach are used, aiming at more realistic system modeling and solving the complex optimization problem. The optimization problem considers the optimal allocation of all distributed generations and the optimal power control of controllable distributed generations. The co-simulation setup employs a tool for power system analysis and a metaheuristic optimizer to solve the optimization problem. Three different resolutions of input data (generation and load profiles) are used: hourly, daily, and monthly averages over one year. An artificial neural network is used to estimate the optimal output of controllable distributed generations and thus significantly decrease the dimensionality of the optimization problem. The proposed procedure is applied on a 13 node test feeder proposed by the Institute of Electrical and Electronics Engineers. The obtained results show a huge impact of the input data resolution on the optimal allocation of distributed generations. Applying the proposed approach, the energy losses are decreased by over 50–70% by the optimal allocation and control of distributed generations depending on the tested network.

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

Energies ◽

10.3390/en14051432 ◽

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.

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External Training Load Monitoring and the Impact on Training Load Management in Collegiate Male Soccer Players

The Journal of Strength and Conditioning Research ◽

10.1519/jsc.0000000000004080 ◽

2021 ◽

Vol Publish Ahead of Print ◽

Author(s):

Jacob R. Gdovin ◽

Riley Galloway ◽

Lorenzo S. Tomasiello ◽

Michael Seabolt ◽

Robert Booker

Keyword(s):

Training Load ◽

Soccer Players ◽

Load Management ◽

Load Monitoring ◽

External Training ◽

The Impact

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Identifying Urban Traveling Hotspots Using an Interaction-Based Spatio-Temporal Data Field and Trajectory Data: A Case Study within the Sixth Ring Road of Beijing

Sustainability ◽

10.3390/su12229662 ◽

2020 ◽

Vol 12 (22) ◽

pp. 9662 ◽

Cited By ~ 1

Author(s):

Disheng Yi ◽

Yusi Liu ◽

Jiahui Qin ◽

Jing Zhang

Keyword(s):

Traffic Management ◽

Spatial Clustering ◽

Spatial Interaction ◽

The Other ◽

Temporal Data ◽

Trajectory Data ◽

Data Field ◽

Spatio Temporal ◽

The Impact

Exploring urban travelling hotspots has become a popular trend in geographic research in recent years. Their identification involved the idea of spatial autocorrelation and spatial clustering based on density in the previous research. However, there are some limitations to them, including the unremarkable results and the determination of various parameters. At the same time, none of them reflect the influences of their neighbors. Therefore, we used the concept of the data field and improved it with the impact of spatial interaction to solve those problems in this study. First of all, an interaction-based spatio-temporal data field identification for urban hotspots has been built. Then, the urban travelling hotspots of Beijing on weekdays and weekends are identified in six different periods. The detected hotspots are passed through qualitative and quantitative evaluations and compared with the other two methods. The results show that our method could discover more accurate hotspots than the other two methods. The spatio-temporal distributions of hotspots fit commuting activities, business activities, and nightlife activities on weekdays, and the hotspots discovered at weekends depict the entertainment activities of residents. Finally, we further discuss the spatial structures of urban hotspots in a particular period (09:00–12:00) as an example. It reflects the strong regularity of human travelling on weekdays, while human activities are more varied on weekends. Overall, this work has a certain theoretical and practical value for urban planning and traffic management.

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Data Sharing in Southeast Asia During the First Wave of the COVID-19 Pandemic

Frontiers in Public Health ◽

10.3389/fpubh.2021.662842 ◽

2021 ◽

Vol 9 ◽

Author(s):

Arianna Maever L. Amit ◽

Veincent Christian F. Pepito ◽

Bernardo Gutierrez ◽

Thomas Rawson

Keyword(s):

Southeast Asia ◽

Data Sharing ◽

Public Health Surveillance ◽

Demographic Data ◽

Epidemiological Data ◽

Temporal Data ◽

Individual Level ◽

Level Data ◽

Reporting Systems ◽

The Impact

Background: When a new pathogen emerges, consistent case reporting is critical for public health surveillance. Tracking cases geographically and over time is key for understanding the spread of an infectious disease and effectively designing interventions to contain and mitigate an epidemic. In this paper we describe the reporting systems on COVID-19 in Southeast Asia during the first wave in 2020, and highlight the impact of specific reporting methods.Methods: We reviewed key epidemiological variables from various sources including a regionally comprehensive dataset, national trackers, dashboards, and case bulletins for 11 countries during the first wave of the epidemic in Southeast Asia. We recorded timelines of shifts in epidemiological reporting systems and described the differences in how epidemiological data are reported across countries and timepoints.Results: Our findings suggest that countries in Southeast Asia generally reported precise and detailed epidemiological data during the first wave of the pandemic. Changes in reporting rarely occurred for demographic data, while reporting shifts for geographic and temporal data were frequent. Most countries provided COVID-19 individual-level data daily using HTML and PDF, necessitating scraping and extraction before data could be used in analyses.Conclusion: Our study highlights the importance of more nuanced analyses of COVID-19 epidemiological data within and across countries because of the frequent shifts in reporting. As governments continue to respond to impacts on health and the economy, data sharing also needs to be prioritised given its foundational role in policymaking, and in the implementation and evaluation of interventions.

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The Visualization Representation of Space-Time-Path in The Space-Time-Cube

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/906/1/012030 ◽

2021 ◽

Vol 906 (1) ◽

pp. 012030

Author(s):

Bingbing Song ◽

Yanlin Wang ◽

Fang Li

Keyword(s):

Three Dimensional ◽

Temporal Changes ◽

Space Time ◽

Temporal Data ◽

Processing Technologies ◽

Spatial Object ◽

Time Path ◽

Visual Clutter ◽

Spatio Temporal ◽

The Impact

Abstract Map is a traditional visualization tool to represent distribution and interaction of spatial objects or spatial phenomenon. However, with the continuous development of acquisition and processing technologies for spatio-temporal data, traditional map can hardly meet the visualization requirement for this type of data. In other words, the dynamic information about spatial object or phenomenon cannot be expressed fully by traditional map. The Space-Time-Cube (STC), as a three-dimensional visualization environment, whose base represents the two-dimensional geographical space and whose height represents the temporal dimension, can simultaneously represent the spatial distribution as well as the temporal changes of spatio-temporal data. For some spatial object or phenomenon, its moving trajectory can be visualized in STC as a Space-Time-Path (STP), by which the speed and state of motion can be clearly reflected. Noticeably, the problem of visual clutter about STP is inevitably due to the complexity of three-dimensional visualization. In order to reduce the impact of visual clutter, this paper discusses different aspects about visualization representation of STP in the STC. The multiple scales representation and the multiple views display can promote interactive experience of users, and the application of different visual variables can help to represent different kinds of attribute information of STP. With the visualization of STP, spatio-temporal changes and attributive characters of spatial object or phenomenon can be represented and analysed.

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Application of Operational Load Monitoring System for Fatigue Estimation of Main Landing Gear Attachment Frame of an Aircraft

Materials ◽

10.3390/ma14216564 ◽

2021 ◽

Vol 14 (21) ◽

pp. 6564

Author(s):

Michal Dziendzikowski ◽

Artur Kurnyta ◽

Piotr Reymer ◽

Marcin Kurdelski ◽

Sylwester Klysz ◽

...

Keyword(s):

Landing Gear ◽

Fatigue Wear ◽

Operational Load ◽

Load Monitoring ◽

Management Paradigms ◽

Optimal Maintenance ◽

Fatigue Estimation ◽

The Individual ◽

Vertical Landing ◽

The Impact

In this paper, we present an approach to fatigue estimation of a Main Landing Gear (MLG) attachment frame due to vertical landing forces based on Operational Loads Monitoring (OLM) system records. In particular, the impact of different phases of landing and on ground operations and fatigue wear of the MLG frame is analyzed. The main functionality of the developed OLM system is the individual assessment of fatigue of the main landing gear node structure for Su-22UM3K aircraft due to standard and Touch-And-Go (T&G) landings. Furthermore, the system allows for assessment of stress cumulation in the main landing gear node structure during touchdown and allows for detection of hard landings. Determination of selected stages of flight, classification of different types of load cycles of the structure recorded by strain gauge sensors during standard full stop landings and taxiing are also implemented in the developed system. Based on those capabilities, it is possible to monitor and compare equivalents of landing fatigue wear between airplanes and landing fatigue wear across all flights of a given airplane, which can be incorporated into fleet management paradigms for the purpose of optimal maintenance of aircraft. In this article, a detailed description of the system and algorithms used for landing gear node fatigue assessment is provided, and the results obtained during the 3-year period of system operation for the fleet of six aircraft are delivered and discussed.

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Monitoring Coronavirus Disease 2019 (COVID-19) Through Trends in Influenza-like Illness, Laboratory-confirmed Influenza, and COVID-19—New York State, Excluding New York City, 1 January 2020–12 April 2020

Clinical Infectious Diseases ◽

10.1093/cid/ciaa684 ◽

2020 ◽

Cited By ~ 2

Author(s):

Eli S Rosenberg ◽

Eric W Hall ◽

Elizabeth M Rosenthal ◽

Angela M Maxted ◽

Donna L Gowie ◽

...

Keyword(s):

New York ◽

New York City ◽

York City ◽

New York State ◽

Temporal Data ◽

York State ◽

Influenza Like Illness ◽

The Impact

Abstract Innovative monitoring approaches are needed to track the coronavirus disease 2019 (COVID-19) epidemic and potentially assess the impact of community mitigation interventions. We present temporal data on influenza-like illness, influenza diagnosis, and COVID-19 cases for all 4 regions of New York State through the first 6 weeks of the outbreak.

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Mapping Chestnut Stands Using Bi-Temporal VHR Data

Remote Sensing ◽

10.3390/rs11212560 ◽

2019 ◽

Vol 11 (21) ◽

pp. 2560 ◽

Cited By ~ 2

Author(s):

Francesca Marchetti ◽

Björn Waske ◽

Manuel Arbelo ◽

Jose Moreno-Ruíz ◽

Alfonso Alonso-Benito

Keyword(s):

Spatial Information ◽

Accuracy Assessment ◽

Temporal Data ◽

Mapping Accuracy ◽

The Public ◽

Tenerife Island ◽

Complex Landscape ◽

The Impact ◽

Very High ◽

Phenological Phases

This study analyzes the potential of very high resolution (VHR) remote sensing images and extended morphological profiles for mapping Chestnut stands on Tenerife Island (Canary Islands, Spain). Regarding their relevance for ecosystem services in the region (cultural and provisioning services) the public sector demand up-to-date information on chestnut and a simple straight-forward approach is presented in this study. We used two VHR WorldView images (March and May 2015) to cover different phenological phases. Moreover, we included spatial information in the classification process by extended morphological profiles (EMPs). Random forest is used for the classification process and we analyzed the impact of the bi-temporal information as well as of the spatial information on the classification accuracies. The detailed accuracy assessment clearly reveals the benefit of bi-temporal VHR WorldView images and spatial information, derived by EMPs, in terms of the mapping accuracy. The bi-temporal classification outperforms or at least performs equally well when compared to the classification accuracies achieved by the mono-temporal data. The inclusion of spatial information by EMPs further increases the classification accuracy by 5% and reduces the quantity and allocation disagreements on the final map. Overall the new proposed classification strategy proves useful for mapping chestnut stands in a heterogeneous and complex landscape, such as the municipality of La Orotava, Tenerife.

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Event Driven Networked Control System for Impact Load Monitoring

Solid State Phenomena ◽

10.4028/www.scientific.net/ssp.237.169 ◽

2015 ◽

Vol 237 ◽

pp. 169-174

Author(s):

Andrzej Majcher

Keyword(s):

Control System ◽

Impact Load ◽

Networked Control System ◽

Networked Control ◽

Impact Monitoring ◽

Event Driven ◽

Load Monitoring ◽

Properties Of Materials ◽

Stochastic Characteristics ◽

The Impact

The article describes the application of the event-driven networked control system (NCS) for impact load monitoring. The main problem in this type of application is measurement synchronization, which should provide full information about the impact in terms of stochastic delays introduced by the network and stochastic characteristics of the actuator. Using the simulation model of the NCS system, the method of solving this particular problem was developed. The results of verification tests for the real object and a synchronized dynamic measurement of signals from resistance strain gauges are presented. The described system can find use in monitoring and fault detection, in tasks connected with dynamics of liquids and gases, acoustics, and the analysis of resistance properties of materials and technical objects. Key words: Networked control system (NCS), event maintenance, event synchronization, impact monitoring, measurement trigger, strain gauge.

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