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.

scholarly journals Comparison between VAR, GSTAR, FFNN-VAR and FFNN-GSTAR Models for Forecasting Oil Production

MATEMATIKA ◽  
2018 ◽  
Vol 34 (1) ◽  
pp. 103-111 ◽  
Author(s):  
Suhartono Suhartono ◽  
Dedy Dwi Prastyo ◽  
Heri Kuswanto ◽  
Muhammad Hisyam Lee
Keyword(s):  
Forecast Accuracy ◽  
Oil Production ◽  
Training Data ◽  
Temporal Data ◽  
Monthly Data ◽  
Vector Autoregressive ◽  
Temporal Models ◽  
Accurate Forecast ◽  
Temporal Model ◽  
Spatio Temporal

Monthly data about oil production at several drilling wells is an example of spatio-temporal data. The aim of this research is to propose nonlinear spatio-temporal model, i.e. Feedforward Neural Network - Vector Autoregressive (FFNN-VAR) and FFNN - Generalized Space-Time Autoregressive (FFNN-GSTAR), and compare their forecast accuracy to linear spatio-temporal model, i.e. VAR and GSTAR. These spatio-temporal models are proposed and applied for forecasting monthly oil production data at three drilling wells in East Java, Indonesia. There are 60 observations that be divided to two parts, i.e. the first 50 observations for training data and the last 10 observations for testing data. The results show that FFNN-GSTAR(11) and FFNN-VAR(1) as nonlinear spatio-temporal models tend to give more accurate forecast than VAR(1) and GSTAR(11) as linear spatio-temporal models. Moreover, further research about nonlinear spatio-temporal models based on neural networks and GSTAR is needed for developing new hybrid models that could improve the forecast accuracy.

scholarly journals 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

2020 ◽  
Vol 12 (22) ◽  
pp. 9662 ◽  
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.

scholarly journals Spatio-Temporal Data Fusion for Satellite Images Using Hopfield Neural Network

2019 ◽  
Vol 11 (18) ◽  
pp. 2077 ◽  
Author(s):  
Fung ◽  
Wong ◽  
Chan
Keyword(s):  
Neural Network ◽  
Data Fusion ◽  
Satellite Images ◽  
Hopfield Neural Network ◽  
Temporal Data ◽  
Fusion Model ◽  
Resolution Image ◽  
Coarse Resolution ◽  
Spatio Temporal ◽  
Fine Resolution

Spatio-temporal data fusion refers to the technique of combining high temporal resolution from coarse satellite images and high spatial resolution from fine satellite images. However, data availability remains a major limitation in algorithm development. Existing spatio-temporal data fusion algorithms require at least one known image pair between the fine and coarse resolution image. However, data which come from two different satellite platforms do not necessarily have an overlap in their overpass times, hence restricting the application of spatio-temporal data fusion. In this paper, a new algorithm named Hopfield Neural Network SPatio-tempOral daTa fusion model (HNN-SPOT) is developed by utilizing the optimization concept in the Hopfield neural network (HNN) for spatio-temporal image fusion. The algorithm derives a synthesized fine resolution image from a coarse spatial resolution satellite image (similar to downscaling), with the use of one fine resolution image taken on an arbitrary date and one coarse image taken on a predicted date. The HNN-SPOT particularly addresses the problem when the fine resolution and coarse resolution images are acquired from different satellite overpass times over the same geographic extent. Both simulated datasets and real datasets over Hong Kong and Australia have been used in the evaluation of HNN-SPOT. Results showed that HNN-SPOT was comparable with an existing fusion algorithm, the spatial and temporal adaptive reflectance fusion model (STARFM). HNN-SPOT assumes consistent spatial structure for the target area between the date of data acquisition and the prediction date. Therefore, it is more applicable to geographical areas with little or no land cover change. It is shown that HNN-SPOT can produce accurate fusion results with >90% of correlation coefficient over consistent land covers. For areas that have undergone land cover changes, HNN-SPOT can still produce a prediction about the outlines and the tone of the features, if they are large enough to be recorded in the coarse resolution image at the prediction date. HNN-SPOT provides a relatively new approach in spatio-temporal data fusion, and further improvements can be made by modifying or adding new goals and constraints in its HNN architecture. Owing to its lower demand for data prerequisites, HNN-SPOT is expected to increase the applicability of fine-scale applications in remote sensing, such as environmental modeling and monitoring.

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

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.

scholarly journals AtmoVis: Visualization of Air Quality Data

2021 ◽  
Author(s):  
◽  
Benjamin Powley
Keyword(s):  
Air Quality ◽  
User Study ◽  
Poor Quality ◽  
Quality Data ◽  
Temporal Data ◽  
Multiple Sources ◽  
Web Based ◽  
Visualization System ◽  
Spatio Temporal ◽  
Temporal Data Analysis

<p>Air quality has an adverse impact on the health of people living in areas with poor quality air. Monitoring is needed to understand the effects of poor air quality. It is difficult to compare measurements to find trends and patterns between different monitoring sites when data is contained in separate data stores. Data visualization can make analyzing air quality more effective by making the data more understandable. The purpose of this research is to design and build a prototype for visualizing spatio-temporal data from multiple sources related to air quality and to evaluate the effectiveness of the prototype against criteria by conducting a user study. The prototype web based visualization system, AtmoVis, has a windowed layout with 6 different visualizations: Heat calendar, line plot, monthly rose, site view, monthly averages and data comparison. A pilot study was performed with 11 participants and used to inform the study protocol before the main user study was performed on 20 participants who were air quality experts or experienced with Geographic Information Systems (GIS). The results of the study demonstrated that the heat calendar, line plot, site view, monthly averages and monthly rose visualizations were effective for analyzing the air quality through AtmoVis. The line plot and the heat calendar were the most effective for temporal data analysis. The interactive web based interface for data exploration with a window layout, provided by AtmoVis, was an effective method for accessing air quality visualizations and inferring relationships among air quality variables at different monitoring sites. AtmoVis could potentially be extended to include other datasets in the future.</p>

Human Movement Analysis Using Heterogeneous Data Sources

2013 ◽  
Vol 4 (3) ◽  
pp. 98-117 ◽  
Author(s):  
João Peixoto ◽  
Adriano Moreira
Keyword(s):  
Intelligent Transportation Systems ◽  
Real Data ◽  
Human Movement ◽  
Heterogeneous Data ◽  
Transportation Systems ◽  
Temporal Data ◽  
Urban Mobility ◽  
Multiple Sources ◽  
Urban Dynamics ◽  
Spatio Temporal

The analysis of urban mobility has been attracting the interest of the research community recently. The research challenges in this domain are diverse and include data acquisition and representation, human movement modeling and the visualization of dynamic geo-referenced data. Some of the direct applications for these studies are urban planning, security, intelligent transportation systems and wireless networks optimization. One of the drivers for recent work in this area is the availability of large datasets representing many aspects of the urban dynamics. Quite often, the proposed approaches are highly dependent on the data type. However, the analysis of urban dynamics could benefit from the combined and simultaneous use of multiple sources of spatio-temporal data. This paper describes the definition of a set of basic concepts for the representation and processing of spatio-temporal data, sufficiently flexible to deal with various types of mobility data and to support multiple forms of processing and visualization of the urban mobility. For this purpose the authors define a set of concepts and describe how real data from heterogeneous sources is mapped into the proposed framework. Available results obtained by the integration of geometric and symbolic data reveal the adequacy of the proposed concepts, and uncover new possibilities for the fusion of heterogeneous datasets.

scholarly journals 4D spatio-temporal convolutional networks for object position estimation in OCT volumes

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Marcel Bengs ◽  
Nils Gessert ◽  
Alexander Schlaefer
Keyword(s):  
Spatial Information ◽  
Tracking System ◽  
Computer Assisted Surgery ◽  
Absolute Error ◽  
Position Estimation ◽  
Optical Tracking ◽  
Computer Assisted ◽  
Optical Tracking System ◽  
Spatio Temporal ◽  
The Impact

AbstractTracking and localizing objects is a central problem in computer-assisted surgery. Optical coherence tomography (OCT) can be employed as an optical tracking system, due to its high spatial and temporal resolution. Recently, 3D convolutional neural networks (CNNs) have shown promising performance for pose estimation of a marker object using single volumetric OCT images. While this approach relied on spatial information only, OCT allows for a temporal stream of OCT image volumes capturing the motion of an object at high volumes rates. In this work, we systematically extend 3D CNNs to 4D spatio-temporal CNNs to evaluate the impact of additional temporal information for marker object tracking. Across various architectures, our results demonstrate that using a stream of OCT volumes and employing 4D spatio-temporal convolutions leads to a 30% lower mean absolute error compared to single volume processing with 3D CNNs.

scholarly journals Generalization of spatio-temporal deep learning for vision-based force estimation

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Finn Behrendt ◽  
Nils Gessert ◽  
Alexander Schlaefer
Keyword(s):  
Deep Learning ◽  
Force Feedback ◽  
Haptic Feedback ◽  
Absolute Error ◽  
Estimation Methods ◽  
Temporal Data ◽  
Learning Models ◽  
Force Estimation ◽  
Tissue Samples ◽  
Spatio Temporal

AbstractRobot-assisted minimally-invasive surgery is increasingly used in clinical practice. Force feedback offers potential to develop haptic feedback for surgery systems. Forces can be estimated in a vision-based way by capturing deformation observed in 2D-image sequences with deep learning models. Variations in tissue appearance and mechanical properties likely influence force estimation methods’ generalization. In this work, we study the generalization capabilities of different spatial and spatio-temporal deep learning methods across different tissue samples. We acquire several data-sets using a clinical laparoscope and use both purely spatial and also spatio-temporal deep learning models. The results of this work show that generalization across different tissues is challenging. Nevertheless, we demonstrate that using spatio-temporal data instead of individual frames is valuable for force estimation. In particular, processing spatial and temporal data separately by a combination of a ResNet and GRU architecture shows promising results with a mean absolute error of 15.450 compared to 19.744 mN of a purely spatial CNN.

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