Reliable and accurate wind speed prediction has significant impact in many industrial sectors such as economic, business and management among others. This paper presents a new model for wind speed prediction based on Graph Attention Networks (GAT). In particular, the proposed model extends GAT architecture by equipping it with a learnable adjacency matrix as well as incorporating a new attention mechanism with the aim of obtaining attention scores per weather variable. The output of the GAT based model is combined with the LSTM layer in order to exploit both the spatial and temporal characteristics of the multivariate multidimensional historical weather data. Real weather data collected from several cities in Denmark and Netherlands are used to conduct the experiments and evaluate the performance of the proposed model. We show that in comparison to previous architectures used for wind speed prediction, the proposed model is able to better learn the complex input-output relationships of the weather data. Furthermore, thanks to the learned attention weights, the model provides an additional insights on the most important weather variables and cities for the studied prediction task.
System Engineering Institute, Xi’an Jiaotong University（西安交通大学系统工程研究所）；Rutgers University（罗格斯大学）；Electric Power Research Institute, State Grid Liaoning Electric Power Co. Ltd.（国网辽宁省电力有限公司电力科学研究院）
Wind power is one of the most important renewable energy sources and accurate wind power forecasting is very significant for reliable and economic power system operation and control strategies. This paper proposes a novel framework with spatiotemporal attention networks (STAN) for wind power forecasting. This model captures spatial correlations among wind farms and temporal dependencies of wind power time series. First of all, we employ a multi-head self-attention mechanism to extract spatial correlations among wind farms. Then, temporal dependencies are captured by the Sequence-to-Sequence (S…
Deep learning applied to weather forecasting has started gaining popularity because of the progress achieved by data-driven models. The present paper compares two different deep learning architectures to perform weather prediction on daily data gathered from 18 cities across Europe and spanned over a period of 15 years. We propose the Deep Attention Unistream Multistream (DAUM) networks that investigate different types of input representations (i.e. tensorial unistream vs. multistream ) as well as the incorporation of the attention mechanism. In particular, we show that adding a self-attention…
University of Oslo（奥斯陆大学）；Institute for Energy Systems (IFE)（能源系统研究所）
This study focuses on multi-step spatio-temporal wind speed forecasting for the Norwegian continental shelf. The study aims to leverage spatial dependencies through the relative physical location of different measurement stations to improve local wind forecasts. Our multi-step forecasting models produce either 10-minute, 1- or 4-hour forecasts, with 10-minute resolution, meaning that the models produce more informative time series for predicted future trends. A graph neural network (GNN) architecture was used to extract spatial dependencies, with different update functions to learn temporal co…
A novel automatic wind power prediction framework based on multi-time scale and temporal attention mechanisms
Lanzhou University（兰州大学）；University of Wollongong（伍伦贡大学）；Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences（中国科学院西北生态环境资源研究院）
TCNFormer: Temporal Convolutional Network Former for Short-Term Wind Speed Forecasting
Aligarh Muslim University（阿里格尔穆斯林大学）；University of Massachusetts, Amherst（马萨诸塞大学阿默斯特分校）；Monash University Malaysia（莫纳什大学马来西亚分校）；Tibet University（西藏大学）；Shanghai University（上海大学）
2DXformer: Dual Transformers for Wind Power Forecasting with Dual Exogenous Variables
School of Artificial Intelligence, Hebei University of Technology, Tianjin, China（人工智能学院，河北工业大学，天津，中国）；State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China（电力设备可靠性与智能化国家重点实验室，河北工业大学，天津，中国）；Hebei Province Key Laboratory of Big Data Calculation, Hebei University of Technology, Tianjin, China（河北省大数据计算重点实验室，河北工业大学，天津，中国）
In this technical report, we present our solution for the Baidu KDD Cup 2022 Spatial Dynamic Wind Power Forecasting Challenge. Wind power is a rapidly growing source of clean energy. Accurate wind power forecasting is essential for grid stability and the security of supply. Therefore, organizers provide a wind power dataset containing historical data from 134 wind turbines and launch the Baidu KDD Cup 2022 to examine the limitations of current methods for wind power forecasting. The average of RMSE (Root Mean Square Error) and MAE (Mean Absolute Error) is used as the evaluation score. We adopt…
Wind power and other forms of renewable energy sources play an ever more important role in the energy supply of today's power grids. Forecasting renewable energy sources has therefore become essential in balancing the power grid. While a lot of focus is placed on new forecasting methods, little attention is given on how to compare, reproduce and transfer the methods to other use cases and data. One reason for this lack of attention is the limited availability of open-source datasets, as many currently used datasets are non-disclosed and make reproducibility of research impossible. This unavail…
WindFM: An Open-Source Foundation Model for Zero-Shot Wind Power Forecasting
School of Economics and Management, North China Electric Power University（经济管理学院，华北电力大学）；Tsinghua University（清华大学）；Institute for Interdisciplinary Information Sciences（交叉信息学院）；Department of Automation（自动化系）；Department of Electrical Engineering（电气工程系）
Toronto Metropolitan University（多伦多都会大学）；University of Toronto（多伦多大学）；Bogazici University（博阿齐奇大学）
Wind power forecasting helps with the planning for the power systems by contributing to having a higher level of certainty in decision-making. Due to the randomness inherent to meteorological events (e.g., wind speeds), making highly accurate long-term predictions for wind power can be extremely difficult. One approach to remedy this challenge is to utilize weather information from multiple points across a geographical grid to obtain a holistic view of the wind patterns, along with temporal information from the previous power outputs of the wind farms. Our proposed CNN-RNN architecture combine…
Short-term Wind Speed Forecasting for Power Integration in Smart Grids based on Hybrid LSSVM-SVMD Method
School of Electrical and Computer Engineering, Mekelle University（梅克莱尔大学电气与计算机工程学院）；Binghamton University（宾汉姆顿大学）；School of Computer Science Engineering, Galgotias University（加尔各答大学计算机科学与工程学院）；School of Electrical and Computer Engineering, Addis Ababa Institute of Technology（亚的斯亚贝巴理工学院电气与计算机工程学院）
University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Mojio Inc.（Mojio公司）
The paper presents a spatio-temporal wind speed forecasting algorithm using Deep Learning (DL)and in particular, Recurrent Neural Networks(RNNs). Motivated by recent advances in renewable energy integration and smart grids, we apply our proposed algorithm for wind speed forecasting. Renewable energy resources (wind and solar)are random in nature and, thus, their integration is facilitated with accurate short-term forecasts. In our proposed framework, we model the spatiotemporal information by a graph whose nodes are data generating entities and its edges basically model how these nodes are int…
Shandong University（山东大学）；Zhejiang University（浙江大学）；Argonne National Laboratory（阿贡国家实验室）；Tsinghua University（清华大学）
Short-term probabilistic wind power forecasting can provide critical quantified uncertainty information of wind generation for power system operation and control. As the complicated characteristics of wind power prediction error, it would be difficult to develop a universal forecasting model dominating over other alternative models. Therefore, a novel multi-model combination (MMC) approach for short-term probabilistic wind generation forecasting is proposed in this paper to exploit the advantages of different forecasting models. The proposed approach can combine different forecasting models th…
A Posterior-Predictive Variance Decomposition for Epistemic and Aleatoric Uncertainty in Wind Power Forecasting
School of Engineering, Deakin University（德肯大学工程学院）；ARC Training Centre in Energy Technologies for Future Grids, School of Engineering, University of Wollongong（未来电网能源技术培训中心，沃林戈大学工程学院）
Improving the forecast accuracy of wind power by leveraging multiple hierarchical structure
Lucas English School of Mathematics and Physics The University of Queensland St Lucia, QLD 4072, Australia（Lucas English 数学与物理学院 昆士兰大学 斯塔 Lucia 分校）；Mahdi Abolghasemi School of Mathematics and Physics The University of Queensland St Lucia, QLD 4072, Australia（Mahdi Abolghasemi 数学与物理学院 昆士兰大学 斯塔 Lucia 分校）
In machine learning, a nonparametric forecasting algorithm for time series data has been proposed, called the kernel spectral hidden Markov model (KSHMM). In this paper, we propose a technique for short-term wind-speed prediction based on KSHMM. We numerically compared the performance of our KSHMM-based forecasting technique to other techniques with machine learning, using wind-speed data offered by the National Renewable Energy Laboratory. Our results demonstrate that, compared to these methods, the proposed technique offers comparable or better performance.
Frequency-aware forecasting for short-term typhoon gust prediction
School of Artificial Intelligence, Hubei University（湖北大学人工智能学院）；School of Economics and Management, Wuhan University（武汉大学经济与管理学院）；School of Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电气工程学院）
Geometric deep learning has gained tremendous attention in both academia and industry due to its inherent capability of representing arbitrary structures. Due to exponential increase in interest towards renewable sources of energy, especially wind energy, accurate wind speed forecasting has become very important. . In this paper, we propose a novel deep learning architecture, Multi Scale Graph Wavenet for wind speed forecasting. It is based on a graph convolutional neural network and captures both spatial and temporal relationships in multivariate time series weather data for wind speed foreca…
Precisely forecasting wind speed is essential for wind power producers and grid operators. However, this task is challenging due to the stochasticity of wind speed. To accurately predict short-term wind speed under uncertainties, this paper proposed a multi-variable stacked LSTMs model (MSLSTM). The proposed method utilizes multiple historical meteorological variables, such as wind speed, temperature, humidity, pressure, dew point and solar radiation to accurately predict wind speeds. The prediction performance is extensively assessed using real data collected in West Texas, USA. The experimen…
Harbin Institute of Technology（哈尔滨工业大学）；Texas A&M University-Kingsville（德州农工大学金斯维尔分校）
Two-stage ensemble-based forecasting methods have been studied extensively in the wind power forecasting field. However, deep learning-based wind power forecasting studies have not investigated two aspects. In the first stage, different learning structures considering multiple inputs and multiple outputs have not been discussed. In the second stage, the model extrapolation issue has not been investigated. Therefore, we develop four deep neural networks for the first stage to learn data features considering the input-and-output structure. We then explore the model extrapolation issue in the sec…
Department of Statistics, Texas A\&M University, College Station, TX
Wind power forecasting has drawn increasing attention among researchers as the consumption of renewable energy grows. In this paper, we develop a deep learning approach based on encoder-decoder structure. Our model forecasts wind power generated by a wind turbine using its spatial location relative to other turbines and historical wind speed data. In this way, we effectively integrate spatial dependency and temporal trends to make turbine-specific predictions. The advantages of our method over existing work can be summarized as 1) it directly predicts wind power based on historical wind speed,…
Spatial Temporal Approach for High-Resolution Gridded Wind Forecasting across Southwest Western Australia
International Centre for Radio Astronomy Research, University of Western Australia（国际射电天文研究中心，西澳大利亚大学）；UWA Oceans Institute, University of Western Australia（西澳大利亚大学海洋研究所）
KDD CUP 2022 proposes a time-series forecasting task on spatial dynamic wind power dataset, in which the participants are required to predict the future generation given the historical context factors. The evaluation metrics contain RMSE and MAE. This paper describes the solution of Team 88VIP, which mainly comprises two types of models: a gradient boosting decision tree to memorize the basic data patterns and a recurrent neural network to capture the deep and latent probabilistic transitions. Ensembling these models contributes to tackle the fluctuation of wind power, and training submodels t…
Symbolic regression corresponds to an ensemble of techniques that allow to uncover an analytical equation from data. Through a closed form formula, these techniques provide great advantages such as potential scientific discovery of new laws, as well as explainability, feature engineering as well as fast inference. Similarly, deep learning based techniques has shown an extraordinary ability of modeling complex patterns. The present paper aims at applying a recent end-to-end symbolic regression technique, i.e. the equation learner (EQL), to get an analytical equation for wind speed forecasting. …
Northeast Electric Power University（东北电力大学）；State Grid Jilin Electric Power Co., Ltd. Jilin Power Supply Company（国网吉林省电力有限公司吉林供电公司）；Huazhong University of Science and Technology（华中科技大学）；Naval University of Engineering（海军工程大学）；Cranfield University（克兰菲尔德大学）
In a modern power system with an increasing proportion of renewable energy, wind power prediction is crucial to the arrangement of power grid dispatching plans due to the volatility of wind power. However, traditional centralized forecasting methods raise concerns regarding data privacy-preserving and data islands problem. To handle the data privacy and openness, we propose a forecasting scheme that combines federated learning and deep reinforcement learning (DRL) for ultra-short-term wind power forecasting, called federated deep reinforcement learning (FedDRL). Firstly, this paper uses the de…
Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting
Northwestern University（西北大学）；Lanzhou University（兰州大学）；South China University of Technology（华南理工大学）；Xi’an Jiaotong University（西安交通大学）；Cloud Gansu Technology Co., Ltd（甘肃云科技有限公司）；Deer AI (Gansu) Technology Co., Ltd（迪尔人工智能（甘肃）科技有限公司）
This paper introduces an R package ForecastTB that can be used to compare the accuracy of different forecasting methods as related to the characteristics of a time series dataset. The ForecastTB is a plug-and-play structured module, and several forecasting methods can be included with simple instructions. The proposed test-bench is not limited to the default forecasting and error metric functions, and users are able to append, remove, or choose the desired methods as per requirements. Besides, several plotting functions and statistical performance metrics are provided to visualize the comparat…
Improving the Accuracy and Interpretability of Neural Networks for Wind Power Forecasting
Ecole Polytechnique Federale de Lausanne (EPFL)（洛桑联邦理工学院）；Aalborg University（奥尔堡大学）；The Hong Kong Polytechnic University（香港理工大学）；The University of Hong Kong（香港大学）
Adani Institute of Infrastructure Engineering（阿达尼基础设施工程学院）
Wind turbine is a complex machine with its rotating and non-rotating equipment being sensitive to faults. Due to increased wear and tear, the maintenance aspect of a wind turbine is of critical importance. Unexpected failure of wind turbine components can lead to increased O\&M costs which ultimately reduces effective power capture of a wind farm. Fault detection in wind turbines is often supplemented with SCADA data available from wind farm operators in the form of time-series format with a 10-minute sample interval. Moreover, time-series analysis and data representation has become a powerful…
Long-term Wind Power Forecasting with Hierarchical Spatial-Temporal Transformer
Sun Yat-sen University（中山大学）；The Hong Kong Polytechnic University（香港理工大学）；Smart Cities Research Institute, The Hong Kong Polytechnic University（香港理工大学智慧城市研究院）；Research Institute, Sun Yat-sen University（中山大学研究院）
DeepMIDE: A Multi-Output Spatio-Temporal Method for Ultra-Scale Offshore Wind Energy Forecasting
Department of Industrial Engineering, Clemson University（克莱姆森大学工业工程系）；Department of Computer Science, Rutgers University（罗杰斯大学计算机科学系）；Department of Statistics, Rutgers University（罗杰斯大学统计学系）；Department of Industrial & Systems Engineering, Rutgers University（罗杰斯大学工业与系统工程系）；University College, Korea University（韩国大学大学学院）
