NS-CUK Joint Journal Club: Minwoo Choi, Review on "Short-term wind speed forecasting based on spatial-temporal graph transformer networks", Energy 2021
NS-CUK Joint Journal Club: Minwoo Choi, Review on "Short-term wind speed forecasting based on spatial-temporal graph transformer networks", Energy 2021
Optimal combinaison of CFD modeling and statistical learning for short-term w...Jean-Claude Meteodyn
Β
After almost three decades of active research, short-term wind power forecasting is now considered as a mature field. It has been widely and successfully put into operation within the past ten years. Meteodyn with over a decade of experience in wind engineering has contributed to this spread with tens of wind farm equipped with forecast solutions around the world. Our next-generation short-term forecasting solution has been designed to makes the most of both a tailored micro-scale CFD modeling and advanced statistical learning. In the frame of our model design, various options have been considered and evaluated taking into account both model performance and operational constraints. Two main approaches for wind power forecasting are usually considered in the literature (and sometimes opposed): βphysicalβ and βstatisticalβ. It is widely admitted that an optimal combination of both is necessary to build a high performance forecasting system. However, behind "optimal combination" resides a wide variety of design options. We propose here to shed some light on what performances one should expect from several modeling options for combining physics (mesoscale/CFD modeling) and statistics (grey/black box statistical learning, phase/magnitude correction, data filtering). Case studies are taken from real wind farms in various climate and terrain conditions.
At present, with the development of wind power project in China, there are more and more projects located at the complex terrain and complex environment. At the same time, since the large planned area of project, the complex mountain area, and limited number of met mast, even without met mast, in order to the reliable development of the wind power project, it is important that how to do the wind resource assessment without actual measurement wind data and other conditions such as less reliable wind data, and the met mast was not considered representative. This paper will use the atmospheric model to do mesoscale simulation calculation of wind resources, and then combine with CFD technology to downscaling computation to get high resolution wind power assessment result. Finally, in order to confirm the validity of this application in the actual project, the comparison between calculation values and measurement values is carried out. The verification result through the actual data of different met mast shows that the wind resource assessment method which combines the CFD and mesoscale technologies is reliable. The main contribution of the article is to provide the reference model and approach for regional planning and large scale wind resource assessment when there isnβt enough adequate and effective wind data.
A novel wind power prediction model using graph attention networks and bi-dir...IJECEIAES
Β
Today, integrating wind energy forecasting is an important area of research due to the erratic nature of wind. To achieve this goal, we propose a new model of wind speed prediction based on graph attention networks (GAT), we added a new attention mechanism and a learnable adjacency matrix to the GAT structure to obtain attention scores for each weather variable. The results of the GAT-based model are merged with the bi-directional deep learning long and short-term memory (BiLSTM) layer to take advantage of the geographic and temporal properties of historical weather data. The experiments and analyzes are carried out using precise meteorological data collected from wind farms in the Moroccan city of Tetouan. We show that the proposed model can learn complex input-output correlations of meteorological data more efficiently than previous wind speed prediction algorithms. Due to the resulting attention weights, the model also provides more information about the main weather factors for the evaluated forecast work.
The document discusses using optimized neural networks for short-term wind speed forecasting. It proposes using parametric recurrent neural networks (PRNNs) with an improved activation function that includes a logarithmic parameter "p" to optimize the network size. The PRNNs are trained to predict wind speed using historical wind farm data. Simulation results show the PRNNs more accurately predict wind speed up to 180 minutes in the future compared to numerical methods using polynomials. The value of the "p" parameter can identify linearly dependent neurons that can be combined to reduce the optimized network size.
Validation of wind resource assessment process based on CFD Jean-Claude Meteodyn
Β
Wind resource assessment requires nowadays more efficient tools to provide an accurate evaluation of production in order to reduce costs.As onshore wind farms are built in more complex terrains, it is necessary to find a new method to provide a fine evaluation of energy which reduces the error during the data extrapolation process. This explains why CFD models have become a standard for WRA in specific conditions.This presentation is focused on the wind speed and energy yield prediction carried out for a 29MW wind farm project. The accuracy of the wind modeling is investigated by the cross validation between the different met masts around the site. The net energy prediction P50 is compared against real wind farm performance data during a blind test organized by EWEA in 2013. More than 50 companies have been involved in order to compare methods results.
This document describes a study that uses an LSTM-CNN algorithm to estimate the output of solar PV plants. It presents a proposed LSTM-CNN model that uses a CNN as a feature extractor and LSTM to model temporal relationships. The methodology section describes training the model on weather and PV plant data. Results show the LSTM-CNN model achieved 96.7% accuracy, outperforming ANN and LSTM models. In conclusion, the model effectively captures both spatial and temporal features to accurately forecast solar PV plant output.
Predicting the Wind: Wind farm prospecting using GISKenex Ltd
Β
A presentation given to the ESRI NZ User Conference in 2012 about the wind prospecting system developed by Kenex using ArcGIS and custom modelling tools.
Optimal combinaison of CFD modeling and statistical learning for short-term w...Jean-Claude Meteodyn
Β
After almost three decades of active research, short-term wind power forecasting is now considered as a mature field. It has been widely and successfully put into operation within the past ten years. Meteodyn with over a decade of experience in wind engineering has contributed to this spread with tens of wind farm equipped with forecast solutions around the world. Our next-generation short-term forecasting solution has been designed to makes the most of both a tailored micro-scale CFD modeling and advanced statistical learning. In the frame of our model design, various options have been considered and evaluated taking into account both model performance and operational constraints. Two main approaches for wind power forecasting are usually considered in the literature (and sometimes opposed): βphysicalβ and βstatisticalβ. It is widely admitted that an optimal combination of both is necessary to build a high performance forecasting system. However, behind "optimal combination" resides a wide variety of design options. We propose here to shed some light on what performances one should expect from several modeling options for combining physics (mesoscale/CFD modeling) and statistics (grey/black box statistical learning, phase/magnitude correction, data filtering). Case studies are taken from real wind farms in various climate and terrain conditions.
At present, with the development of wind power project in China, there are more and more projects located at the complex terrain and complex environment. At the same time, since the large planned area of project, the complex mountain area, and limited number of met mast, even without met mast, in order to the reliable development of the wind power project, it is important that how to do the wind resource assessment without actual measurement wind data and other conditions such as less reliable wind data, and the met mast was not considered representative. This paper will use the atmospheric model to do mesoscale simulation calculation of wind resources, and then combine with CFD technology to downscaling computation to get high resolution wind power assessment result. Finally, in order to confirm the validity of this application in the actual project, the comparison between calculation values and measurement values is carried out. The verification result through the actual data of different met mast shows that the wind resource assessment method which combines the CFD and mesoscale technologies is reliable. The main contribution of the article is to provide the reference model and approach for regional planning and large scale wind resource assessment when there isnβt enough adequate and effective wind data.
A novel wind power prediction model using graph attention networks and bi-dir...IJECEIAES
Β
Today, integrating wind energy forecasting is an important area of research due to the erratic nature of wind. To achieve this goal, we propose a new model of wind speed prediction based on graph attention networks (GAT), we added a new attention mechanism and a learnable adjacency matrix to the GAT structure to obtain attention scores for each weather variable. The results of the GAT-based model are merged with the bi-directional deep learning long and short-term memory (BiLSTM) layer to take advantage of the geographic and temporal properties of historical weather data. The experiments and analyzes are carried out using precise meteorological data collected from wind farms in the Moroccan city of Tetouan. We show that the proposed model can learn complex input-output correlations of meteorological data more efficiently than previous wind speed prediction algorithms. Due to the resulting attention weights, the model also provides more information about the main weather factors for the evaluated forecast work.
The document discusses using optimized neural networks for short-term wind speed forecasting. It proposes using parametric recurrent neural networks (PRNNs) with an improved activation function that includes a logarithmic parameter "p" to optimize the network size. The PRNNs are trained to predict wind speed using historical wind farm data. Simulation results show the PRNNs more accurately predict wind speed up to 180 minutes in the future compared to numerical methods using polynomials. The value of the "p" parameter can identify linearly dependent neurons that can be combined to reduce the optimized network size.
Validation of wind resource assessment process based on CFD Jean-Claude Meteodyn
Β
Wind resource assessment requires nowadays more efficient tools to provide an accurate evaluation of production in order to reduce costs.As onshore wind farms are built in more complex terrains, it is necessary to find a new method to provide a fine evaluation of energy which reduces the error during the data extrapolation process. This explains why CFD models have become a standard for WRA in specific conditions.This presentation is focused on the wind speed and energy yield prediction carried out for a 29MW wind farm project. The accuracy of the wind modeling is investigated by the cross validation between the different met masts around the site. The net energy prediction P50 is compared against real wind farm performance data during a blind test organized by EWEA in 2013. More than 50 companies have been involved in order to compare methods results.
This document describes a study that uses an LSTM-CNN algorithm to estimate the output of solar PV plants. It presents a proposed LSTM-CNN model that uses a CNN as a feature extractor and LSTM to model temporal relationships. The methodology section describes training the model on weather and PV plant data. Results show the LSTM-CNN model achieved 96.7% accuracy, outperforming ANN and LSTM models. In conclusion, the model effectively captures both spatial and temporal features to accurately forecast solar PV plant output.
Predicting the Wind: Wind farm prospecting using GISKenex Ltd
Β
A presentation given to the ESRI NZ User Conference in 2012 about the wind prospecting system developed by Kenex using ArcGIS and custom modelling tools.
Predicting the Wind - wind farm prospecting with GISKenex Ltd
Β
A new and original approach to wind farm development using advanced GIS modelling techniques, that allows developers to cut time and costs at the beginning of a project.
Presentation: Wind Speed Prediction using Radial Basis Function Neural NetworkArzam Muzaffar Kotriwala
Β
This document describes a project using a radial basis function neural network to predict wind speed. It discusses motivations for wind speed prediction and for using neural networks. It outlines objectives to design and test an RBF network model using historical wind data. The methodology describes collecting wind data, selecting input variables, training the RBF network with different configurations, and comparing its performance to other techniques. Results show the RBF network outperformed persistence and backpropagation models with some configurations achieving more accurate 1-hour ahead predictions. The conclusion discusses findings and recommendations for further improving wind speed prediction.
Analysis of Time Diversity Gain for Satellite Communication Link based on Ku-...IJECEIAES
Β
This paper reports a study on mitigation of propagation impairments on Earthβspace communication links. The study uses time diversity as a technique for mitigating rain propagation impairment in order to rectify rain fade. Rain attenuation time series along earth-to-satellite link were measured for two years period at 12.255 GHz in Malaysia. The time diversity technique was applied on measured rain fade to investigate the level of possible improvement in system. Time diversity gain from measured oneminute rain attenuation for two years period was estimated and significant improvement was observed with different delays of time. These findings will be utilized as a useful tool for link designers to apply time diversity as a rain fade mitigation technique in Earth-satellite communications systems.
Wind power forecasting: A Case Study in Terrain using Artificial IntelligenceIRJET Journal
Β
This document presents a study on using artificial neural networks to forecast wind power. Real-time data on wind speed, direction, temperature, humidity and pressure was collected from a measurement station. 100 artificial neural networks with different structures were trained and tested. The best performing network was a multilayer perceptron with 6 inputs, 24 hidden layers, exponential activation and identity output activation. This network achieved a 99% success rate in estimating wind power compared to real measured data. The study demonstrates that artificial neural networks can accurately estimate wind power for short-term forecasting.
Short-term wind speed forecasting system using deep learning for wind turbine...IJECEIAES
Β
It is very important to accurately detect wind direction and speed for wind energy that is one of the essential sustainable energy sources. Studies on the wind speed forecasting are generally carried out for long-term predictions. One of the main reasons for the long-term forecasts is the correct planning of the area where the wind turbine will be built due to the high investment costs and long-term returns. Besides that, short-term forecasting is another important point for the efficient use of wind turbines. In addition to estimating only average values, making instant and dynamic short-term forecasts are necessary to control wind turbines. In this study, short-term forecasting of the changes in wind speed between 1-20 minutes using deep learning was performed. Wind speed data was obtained instantaneously from the feedback of the emulated wind turbine's generator. These dynamically changing data was used as an input of the deep learning algorithm. Each new data from the generator was used as both test and training input in the proposed approach. In this way, the model accuracy and enhancement were provided simultaneously. The proposed approach was turned into a modular independent integrated system to work in various wind turbine applications. It was observed that the system can predict wind speed dynamically with around 3% error in the applications in the test setup applications.
IEEE International Conference PresentationAnmol Dwivedi
Β
IEEE INTERNATIONAL CONFERENCE -
Paper Title "Real-Time Implementation of Phasor Measurement Unit Using NI CompactRIO".
Code Available on: https://github.com/anmold-07/Synchrophasor-Estimation
The document discusses applying a BERT model for wind power forecasting using spatial and temporal data from turbines. It summarizes preprocessing steps including filling missing values and normalization. A single-layer BERT model is used to capture long-term dependencies without positional encodings. The model predicts the overall trend but daily fluctuations are added post-processing using historical patterns. The full code is released online and the approach is found to be simple and effective for this wind power forecasting task.
Wind Turbines: Will they ever become economically feasible? Jeffrey Funk
Β
The document discusses wind turbines and whether they will become economically feasible. It provides an overview of wind turbine costs, theoretical and empirical data on power output in relation to rotor diameter and wind speed, and how rotor diameter and other factors impact rated wind speed. It finds that while larger turbine sizes were once able to reduce costs, costs may now be rising due to the stronger materials needed for very large diameters. New materials development could potentially further reduce costs but significant improvements are uncertain. The future of wind power appearing unclear without new designs or materials breakthroughs.
This document discusses a study that uses a hybrid CNN-LSTM attention model with quantile regression to predict faults in electrical machines by analyzing time series sensor data. The model aims to better manage uncertainties in the data compared to traditional models. Researchers collected vibration data from sensors on a real electrical machine measuring variations in three axes. They preprocessed the data using empirical wavelet transform and Savitzky-Golay filtering to extract relevant features and reduce noise. The hybrid deep learning model was trained on this data and used with quantile regression and anomaly detection algorithms to predict faults and provide probability levels to machine operators. The study aims to help optimize maintenance scheduling and improve electrical machine performance.
This paper proposes a Wavelet based Adaptive Neuro-Fuzzy Inference System (WANFIS) applied to forecast the wind power and enhance the accuracy of one step ahead with a 10 minutes resolution of real time data collected from a wind farm in North India. The proposed method consists two cases. In the first case all the inputs of wind series and output of wind power decomposition coefficients are carried out to predict the wind power. In the second case all the inputs of wind series decomposition coefficients are carried out to get wind power prediction. The performance of proposed WANFIS is compared to Wavelet Neural Network (WNN) and the results of the proposed model are shown superior to compared methods.
Test different neural networks models for forecasting of wind,solar and energ...Tonmoy Ibne Arif
Β
In this project work, a multi-step deep neural network is used to forecast power generation and load demand for a short-term time frame. The data or feature vectors that have been used to predict the target, is a sequential time series sequence. In this project, a Recurrent Neural Network has been used in combination with a convolutional neural network to have a better forecasting model for the Windpark, Solar park and Loadpark datasets. Moreover, the forecasting performance of Feedforward neural network and Long Short Term Memory also has been compared. The whole project work has divided into two parts, in the first approach the raw dataset has been divided into a train, test split and no previous step data have been used. In the second step whole raw dataset has been divided into test, train and validation split. Additionally, current and seven previous time steps data has been fed into the model.
Wind power prediction using a nonlinear autoregressive exogenous model netwo...IJECEIAES
Β
The monitoring of wind installations is key for predicting their future behavior, due to the strong dependence on weather conditions and the stochastic nature of the wind. However, in some places, in situ measurements are not always available. In this paper, active power predictions for the city of Santa Marta-Colombia using a nonlinear autoregressive exogenous model (NARX) network were performed. The network was trained with a reliable dataset from a wind farm located in Turkey, because the meteorological data from the city of Santa Marta are unavailable or unreliable on certain dates. Three training and testing cases were designed, with different input variables and varying the network target between active power and wind speed. The dataset was obtained from the Kaggle platform, and is made up of five variables: date, active power, wind speed, theoretical power, and wind direction; each with 50,530 samples, which were preprocessed, and in some cases, normalized, to facilitate the neural network learning. For the training, testing and validation processes, a correlation coefficient of 0.9589 was obtained for the best scenario with the data from Turkey, while the best correlation coefficient for the data from Santa Marta was 0.8537.
This document discusses using machine learning to help develop subgrid parameterizations for climate models based on high-resolution simulations. It notes that while high-resolution models have progressed faster than parameterizations, humans still develop parameterizations, which is slow. The document explores using machine learning on comprehensive training datasets from high-resolution models to relate coarse-grid variables to subgrid-scale quantities needed by climate models. Challenges include making schemes stochastic, handling data outside the training range, and instability in global models. Past work applying neural networks to a cloud-resolving model dataset showed promise but has not been used prognostically. Overall, machine learning may help break the parameterization bottleneck if technical challenges can be overcome.
Future guidelines the meteorological view - Isabel MartΓnez (AEMet)IrSOLaV Pomares
Β
This document discusses nowcasting and forecasting of solar irradiance using meteorological data. Nowcasting uses observations from the past 6 hours to predict clouds and irradiance up to 2 hours ahead for a specific site. Forecasting uses numerical weather prediction models to predict clouds and irradiance out to days or weeks ahead on regional to global scales. The document outlines various nowcasting techniques including the use of sky cameras, satellites, and neural networks. It also describes several forecast models run operationally at ECMWF and AEMET including HIRLAM, HARMONIE, and the ECMWF model. Prognostic aerosols are also modeled to improve irradiance forecasts.
IRJET- Sink Mobility based Energy Efficient Routing Protocol for Wireless Sen...IRJET Journal
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The document describes a proposed sink mobility based energy efficient routing protocol for wireless sensor networks. The protocol uses both a static centralized sink and a mobile sink that follows a predetermined path with 4 sojourn locations. This is aimed to improve network lifetime by balancing energy load across nodes. Simulation results show that the proposed approach with a mobile sink performs better than the Threshold sensitive Energy Efficient sensor Network (TEEN) protocol alone in terms of number of alive nodes, number of cluster heads, and number of packets sent to the base station over multiple rounds. Using a mobile sink helps scatter the energy load in the network and extends lifetime compared to only using a static sink.
Upcoming Datasets: Global wind map, Jake Badger ( Risoe DTU)IRENA Global Atlas
Β
Upcoming Datasets: Global wind map. A presentation by Jake Badger ( Risoe DTU) during the Global Atlas side event which held at the World Future Energy Summit in 2014
This document summarizes a study that evaluated the quantitative performance of a wind turbine generator cluster using statistical techniques. The study aimed to identify reasons for shortfalls between actual and predicted power generation and quantify losses due to lack of machine availability and grid availability. Several statistical methods were proposed and evaluated, including extraction factor method, monthly correlation method, turbine specific power curve method, and rated power curve method. The turbine specific power curve method was found to provide the best and most consistent results for calculating actual generation losses below 10% due to lack of machine availability.
NS-CUK Seminar: V.T.Hoang, Review on "GOAT: A Global Transformer on Large-sca...ssuser4b1f48
Β
This document presents GOAT, a scalable global transformer model for graph-structured data. GOAT uses a novel local attention module to absorb rich local information from node neighborhoods, in addition to a global attention mechanism that allows each node to attend to all other nodes. The document reports that GOAT achieves strong performance on large-scale homophilous and heterophilous node classification benchmarks, demonstrating its ability to leverage both local and global graph information for prediction tasks. Ablation studies on codebook size further indicate GOAT's effectiveness at modeling long-range interactions through its global attention.
NS-CUK Seminar: J.H.Lee, Review on "Graph Propagation Transformer for Graph R...ssuser4b1f48
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NS-CUK Seminar:
J.H.Lee, Review on "Graph Propagation Transformer for Graph Representation Learning", IJCAI 2023
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Presentation: Wind Speed Prediction using Radial Basis Function Neural NetworkArzam Muzaffar Kotriwala
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Analysis of Time Diversity Gain for Satellite Communication Link based on Ku-...IJECEIAES
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Short-term wind speed forecasting system using deep learning for wind turbine...IJECEIAES
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It is very important to accurately detect wind direction and speed for wind energy that is one of the essential sustainable energy sources. Studies on the wind speed forecasting are generally carried out for long-term predictions. One of the main reasons for the long-term forecasts is the correct planning of the area where the wind turbine will be built due to the high investment costs and long-term returns. Besides that, short-term forecasting is another important point for the efficient use of wind turbines. In addition to estimating only average values, making instant and dynamic short-term forecasts are necessary to control wind turbines. In this study, short-term forecasting of the changes in wind speed between 1-20 minutes using deep learning was performed. Wind speed data was obtained instantaneously from the feedback of the emulated wind turbine's generator. These dynamically changing data was used as an input of the deep learning algorithm. Each new data from the generator was used as both test and training input in the proposed approach. In this way, the model accuracy and enhancement were provided simultaneously. The proposed approach was turned into a modular independent integrated system to work in various wind turbine applications. It was observed that the system can predict wind speed dynamically with around 3% error in the applications in the test setup applications.
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Code Available on: https://github.com/anmold-07/Synchrophasor-Estimation
The document discusses applying a BERT model for wind power forecasting using spatial and temporal data from turbines. It summarizes preprocessing steps including filling missing values and normalization. A single-layer BERT model is used to capture long-term dependencies without positional encodings. The model predicts the overall trend but daily fluctuations are added post-processing using historical patterns. The full code is released online and the approach is found to be simple and effective for this wind power forecasting task.
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5th Power Grid Model Meet-up
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Power Grid Model
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Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
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NS-CUK Joint Journal Club: Minwoo Choi, Review on "Short-term wind speed forecasting based on spatial-temporal graph transformer networks", Energy 2021
1. Min-Woo Choi
Network Science Lab
Dept. of Artificial Intelligence
The Catholic University of Korea
E-mail: choimin1231@catholic.ac.kr
2023. 02. 14 Publish: 2022
Journal: Energy (IF: 8.85)
2. 1
ο Introduction
β’ Limitation
β’ Purpose
β’ Main contribution
ο Methodology
β’ Data description
β’ Experiment setup
ο Results
ο Conclusions
ο Limitation and Future work
3. 2
1. Introduction
Limitation
β’ CNN-based spatio-temporal prediction models require wind information that nodes at regular
intervals or nodes in a square array.
β’ But actual wind farms are not necessarily arranged regularly.
Related study
β’ Fu et al. (2019): Spatiotemporal attention network (STAN)
- Using Multi head self attention to extract the spatial correlation.
β’ Khodayar et al. (2019): Graph convolution deep learning architecture (GCDLA)
- To capture the deep spatiotemporal information of wind speed.
1. Develop a relatively simple multi-node forecasting model by taking advantage of the various
architectures of the above methods.
2. Proposed Spatial-Temporal Graph Transformer Network (STGTN) model to improve short-
term wind speed prediction performance.
Purpose of study
4. 3
1. Introduction
Main contribution
β’ External attention mechanism is incorporated into the forecasting model
οΌ It can capture dynamic spatial information and reduce the complexity of the network.
β’ A transformer model with graph convolution is proposed
οΌ To learn spatial correlation based on the Euclidean distance between wind farms.
5. 4
2. Methodology
Data description
β’ Location: Danish offshore wind farms
β’ Period : February 6, 2014 to June 6, 2014
β’ Number of data: 111 wind turbine node (14,400 points)
β’ Resolution: 10 min
β’ Range of data:
β’ Training/Validation/Test set: 3:1:1
β’ Using historical data for each 12 points, the wind speed
is predicted for 10 min to 1 h.
6. 5
2. Methodology
Problem formulation
β’ Position information between wind turbines is represented as a graph
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Spatialtemporal model of Short-term wind speed forecasting
Experiment setup
β’ Batch size: 64
β’ learning rate: 0.01
β’ Optimization: Adam
β’ Decay rate of 0.7 after every 10 epochs
π
πΈ π΄
1 2 3
2
3
π π
7. 6
2. Methodology
Graph transformer
Structure of the graph transformer
ο§ The fusion of spatial and
temporal information
ο§ it considered to improve the robustness of
extracting spatial features of wind speed
ο§ higher-level features can be extracted in the
spatial correlation based on Euclidean distance
External attention mechanism
8. 7
2. Methodology
Architecture of the proposed model
Aggregate
spatiotemporal
features
Extract
temporal
feature
Extract
spatial
feature
Form input
through
residual
connection
Fig. 1. Illustration of the proposed spatial-temporal graph transformer network (STGTN).
Main contribution
9. 8
2. Methodology
Benchmark models
β’ SVR
β’ DL-STF
β’ STAN
β’ STGTN-T (Transformer)
β’ STGTN (proposed)
Not include spatial information
Based on spatial-temporal information (previous study)
Transformer and MLP
10. 9
3. Results
10 min to 1 h prediction
β’ The results show that STGTN dominates all
considered methods in terms of the lowest RMSE
values.
β’ STGTN with MLP outperforms STGTN-T with
transformer.
β’ Forecasting models based on spatiotemporal
information can track changes of wind speed faster.
β’ The performance of the STGTN model is more stable
compared with the DL-STF and STAN models.
0 min to 2000 min prediction
Consider the all wind turbine node
11. 10
3. Results
May 20, performance
β’ These results show that spatial information may degrades the forecasting performance when the
standard deviation of wind speed is small.
May 22, performance Wind speed Condition: fluctuation is small
β’ SVR is better than DL-STF
β’ SVR is better than STGTN-T
12. 11
3. Results
June 4, performance
May 21, performance
June 4, performance Wind speed Condition: fluctuation is large
β’ The MLP and Transformer performs similarly as the feature extractor when large-scale wind speed
fluctuations exist.
ο¨ On the whole, STGTN provides more stable and accurate wind speed forecasts compared with
other methods under different wind speed conditions.
13. 12
4. Conclusions
β’ The results indicate that the proposed model can effectively utilize the spatiotemporal
information to generate more accurate wind speed forecasts.
β’ Wind speed data of adjacent nodes is sufficiently used to correct the wrong predictions
caused by outliers in the historical data of individual nodes.
β’ The forecasting results confirms that the proposed model can yield stable wind speed
forecasts regardless of different scales of wind speed fluctuations.
14. 13
5. Limitation and Future work
β’ Seasonal factors are not considered. (just consider May & June)
β’ The utilization of wind direction information is not discussed.
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The topic of my presentation is Short-term wind speed forecasting based on spatial-temporal graph transformer networks.
The order of contents is as follows:
First, the limitations of previous studies in wind speed prediction are as follows.
When we use a spatiotemporal prediction model to predict wind speed based on CNN, a square array or nodes with regular intervals are required. Like that grid.
However, in actually it is not composed of regular data.
Therefore, as a related study, Fu extracted spatial correlation using the STAN model.
and Kyodayar extracted spatiotemporal information of wind speed using the GCDLA model.
There are few related studies, but taking advantage of related studies, this study proposed STGTN to predict wind speed.
The main contribution of this paper,
First~
Second~
The data used were wind farms located in Danish offshore, and data were collected at 10-minute intervals with 111 wind turbine nodes.
The data set was divided into 3:1:1, and the wind speed was predicted using the historical data of each 12 points.
The experiment setup is as written, and the information of wind turbines is constructed as follows.
V is ~
E is ~
A is ~
First, this is a structure of the graph transformer model.
First Attention mechanism cannot capture time series and spatial information compared with the recurrent and convolutional structure.
Therefore, the spatial-temporal position in embedding layer, temporal and spatial information is injected into the input sequence before performing subsequent operations.
In External attention mechanism ~
And Graph convolution extract the higher level features in spatial correlation
It is a configuration of the proposed model as STGTN.
first, spatial features are extracted from the Graph transformer, and second, inputs are formed through residual connections, moved to MLP, TEMPORAL features are extracted.
In the last convolutional layer, the wind speed is predicted by aggregate.
Benchmark models were constructed to validate the proposed model.
First, SVR model not include the spatial information, DL-STF and STAN models based on spatial-temporal information
and the STGTN-T model using Transformer was used instead of MLP model.
Next is the results part. Looking at the table, the proposed model performed better than the benchmarking models.
What is noteworthy here is that STGTN showed better performance than STGTN-T, which means that the MLP used instead of the transformer improved the prediction accuracy, It has the advantage of reducing the overhead of model training and hyperparameter tuning with a simple structure.
Next, when the days of wind speed fluctuation is small, look at the upper table SVR is better than DL-STF and under table SVR is better than STGTN-T.
These results show that spatial information may degrades the forecasting performance when the Flutuation is small.
Conversely, STGTN-T and STGTN were found to be effective when the fluctuations is large.
The proposed models all showed good predictive performance regardless of wind speed conditions.
The proposed model is stable and has excellent spatio-temporal predictability.