The forecast of electrical loads is essential for the planning and operation of the power system. Recently, advances in deep learning have enabled more accurate forecasts. However, deep neural networks are prone to adversarial attacks. Although most of the literature focuses on integrity-based attacks, this paper proposes availability-based adversarial attacks, which can be more easily implemented by attackers. For each forecast instance, the availability attack position is optimally solved by mixed-integer reformulation of the artificial neural network. To tackle this attack, an adversarial training algorithm is proposed. In simulation, a realistic load forecasting dataset is considered and the attack performance is compared to the integrity-based attack. Meanwhile, the adversarial training algorithm is shown to significantly improve robustness against availability attacks. All codes are available at https://github.com/xuwkk/AAA_Load_Forecast.
Nanyang Technological University（南洋理工大学）；Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (ASTAR)（新加坡科技研究局资讯通信研究院（I2R））
Electric Vehicle (EV) charging demand and charging station availability forecasting is one of the challenges in the intelligent transportation system. With the accurate EV station situation prediction, suitable charging behaviors could be scheduled in advance to relieve range anxiety. Many existing deep learning methods are proposed to address this issue, however, due to the complex road network structure and comprehensive external factors, such as point of interests (POIs) and weather effects, many commonly used algorithms could just extract the historical usage information without considerin…
TECNALIA（泰克纳利亚）；Basque Research and Technology Alliance (BRTA)（巴斯克研究与技术联盟）；University of the Basque Country (UPV/EHU)（巴斯克大学）
This work aims at unveiling the potential of Transfer Learning (TL) for developing a traffic flow forecasting model in scenarios of absent data. Knowledge transfer from high-quality predictive models becomes feasible under the TL paradigm, enabling the generation of new proper models with few data. In order to explore this capability, we identify three different levels of data absent scenarios, where TL techniques are applied among Deep Learning (DL) methods for traffic forecasting. Then, traditional batch learning is compared against TL based models using real traffic flow data, collected by …
Enabling CMF Estimation in Data-Constrained Scenarios: A Semantic-Encoding Knowledge Mining Model
Institute of Transportation Studies, University of California, Davis（加州大学戴维斯分校交通研究所）；Department of Civil and Environmental Engineering, Washington State University（华盛顿州立大学土木与环境工程系）
National Chin-Yi University of Technology（国立勤益科技大学）；University of Tartu（塔尔图大学）
Renewable Energies (RE) have gained more attention in recent years since they offer clean and sustainable energy. One of the major sustainable development goals (SDG-7) set by the United Nations (UN) is to achieve affordable and clean energy for everyone. Among the world's all renewable resources, solar energy is considered as the most abundant and can certainly fulfill the target of SDGs. Solar energy is converted into electrical energy through Photovoltaic (PV) panels with no greenhouse gas emissions. However, power generated by PV panels is highly dependent on solar radiation received at a …
Forecasting Energy Availability in Local Energy Communities via LSTM Federated Learning
DISMI, University of Modena and Reggio Emilia, Italy（DISMI，Modena和Reggio Emilia大学）；En\&Tech, University of Modena and Reggio Emilia, Italy（En\&Tech，Modena和Reggio Emilia大学）
National University of Sciences and Technology（国立科技大学）；Information Technology University (ITU)（信息技术大学（ITU））
Wheat is the main agricultural crop of Pakistan and is a staple food requirement of almost every Pakistani household making it the main strategic commodity of the country whose availability and affordability is the government's main priority. Wheat food availability can be vastly affected by multiple factors included but not limited to the production, consumption, financial crisis, inflation, or volatile market. The government ensures food security by particular policy and monitory arrangements, which keeps up purchase parity for the poor. Such arrangements can be made more effective if a dyna…
Due to the increasing availability of large-scale observation and simulation datasets, data-driven representations arise as efficient and relevant computation representations of dynamical systems for a wide range of applications, where model-driven models based on ordinary differential equation remain the state-of-the-art approaches. In this work, we investigate neural networks (NN) as physically-sound data-driven representations of such systems. Reinterpreting Runge-Kutta methods as graphical models, we consider a residual NN architecture and introduce bilinear layers to embed non-linearities…
Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Center for Forecasting Science, Chinese Academy of Sciences（中国科学院预测科学研究中心）
The availability of tourism-related big data increases the potential to improve the accuracy of tourism demand forecasting, but presents significant challenges for forecasting, including curse of dimensionality and high model complexity. A novel bagging-based multivariate ensemble deep learning approach integrating stacked autoencoders and kernel-based extreme learning machines (B-SAKE) is proposed to address these challenges in this study. By using historical tourist arrival data, economic variable data and search intensity index (SII) data, we forecast tourist arrivals in Beijing from four c…
Qatar University（卡塔尔大学）；University of Sydney（悉尼大学）
This papers presents a deep learning-based framework to predict crowdsourced service availability spatially and temporally. A novel two-stage prediction model is introduced based on historical spatio-temporal traces of mobile crowdsourced services. The prediction model first clusters mobile crowdsourced services into regions. The availability prediction of a mobile crowdsourced service at a certain location and time is then formulated as a classification problem. To determine the availability duration of predicted mobile crowdsourced services, we formulate a forecasting task of time series usi…
Due to the limitation of data availability, traditional power load forecasting methods focus more on studying the load variation pattern and the influence of only a few factors such as temperature and holidays, which fail to reveal the inner mechanism of load variation. This paper breaks the limitation and collects 80 potential features from astronomy, geography, and society to study the complex nexus between power load variation and influence factors, based on which a short-term power load forecasting method is proposed. Case studies show that, compared with the state-of-the-art methods, the …
Accurate air turbulence forecasting can help airlines avoid hazardous turbulence, guide the routes that keep passengers safe, maximize efficiency, and reduce costs. Traditional turbulence forecasting approaches heavily rely on painstakingly customized turbulence indexes, which are less effective in dynamic and complex weather conditions. The recent availability of high-resolution weather data and turbulence records allows more accurate forecasting of the turbulence in a data-driven way. However, it is a non-trivial task for developing a machine learning based turbulence forecasting system due …
A Market-Rule-Informed Neural Network for Efficient Imbalance Electricity Price Forecasting
Delft University of Technology（代尔夫特理工大学）；Austrian Institute of Technology（奥地利技术研究院）；London Business School（伦敦商学院）；University of Technology Sydney（悉尼大学）；The Chinese University of Hongkong (Shenzhen)（香港中文大学（深圳））
Google Research（谷歌研究院）；Massachusetts Institute of Technology（麻省理工学院）
The availability of large amounts of time series data, paired with the performance of deep-learning algorithms on a broad class of problems, has recently led to significant interest in the use of sequence-to-sequence models for time series forecasting. We provide the first theoretical analysis of this time series forecasting framework. We include a comparison of sequence-to-sequence modeling to classical time series models, and as such our theory can serve as a quantitative guide for practitioners choosing between different modeling methodologies.
Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting Epidemics
International Institute of Information Technology Bangalore（班加罗尔国际信息技术学院）；Sorbonne University Abu Dhabi（索邦大学阿布扎比分校）；Sorbonne Center for Artificial Intelligence（索邦人工智能中心）；National University of Singapore（新加坡国立大学）
The transport literature is dense regarding short-term traffic predictions, up to the scale of 1 hour, yet less dense for long-term traffic predictions. The transport literature is also sparse when it comes to city-scale traffic predictions, mainly because of low data availability. In this work, we report an effort to investigate whether deep learning models can be useful for the long-term large-scale traffic prediction task, while focusing on the scalability of the models. We investigate a city-scale traffic dataset with 14 weeks of speed observations collected every 15 minutes over 1098 segm…
AIRLab, University of Amsterdam（阿姆斯特丹大学 AIRLab）；University of Amsterdam（阿姆斯特丹大学）
Probabilistic time series forecasting is crucial in many application domains such as retail, ecommerce, finance, or biology. With the increasing availability of large volumes of data, a number of neural architectures have been proposed for this problem. In particular, Transformer-based methods achieve state-of-the-art performance on real-world benchmarks. However, these methods require a large number of parameters to be learned, which imposes high memory requirements on the computational resources for training such models. To address this problem, we introduce a novel Bidirectional Temporal Co…
A Climate-Aware Deep Learning Framework for Generalizable Epidemic Forecasting
School of Engineering（工程学院）；Brown University（布朗大学）；Department of Epidemiology（流行病学系）；School of Public Health（公共卫生学院）；Institute at Brown for Environment and Society（布朗大学环境与社会研究所）
Short-term traffic flow prediction is one of the crucial issues in intelligent transportation system, which is an important part of smart cities. Accurate predictions can enable both the drivers and the passengers to make better decisions about their travel route, departure time and travel origin selection, which can be helpful in traffic management. Multiple models and algorithms based on time series prediction and machine learning were applied to this issue and achieved acceptable results. Recently, the availability of sufficient data and computational power, motivates us to improve the pred…
Applied AI Center（应用人工智能中心）；Skolkovo Institute of Science and Technology（斯科尔科沃科学技术学院）；Shirshov Institute of Oceanology（希尔绍夫海洋研究所）；Moscow Institute of Physics and Technology（莫斯科物理技术学院）；Gazprom Neft（俄罗斯天然气工业石油公司）
Global warming made the Arctic available for marine operations and created demand for reliable operational sea ice forecasts to make them safe. While ocean-ice numerical models are highly computationally intensive, relatively lightweight ML-based methods may be more efficient in this task. Many works have exploited different deep learning models alongside classical approaches for predicting sea ice concentration in the Arctic. However, only a few focus on daily operational forecasts and consider the real-time availability of data they need for operation. In this work, we aim to close this gap …
BuildSTG: A Multi-building Energy Load Forecasting Method using Spatio-Temporal Graph Neural Network
The Hong Kong University of Science（香港理工大学）；Ningbo Institute of Digital Twin, Eastern Institute of Technology（宁波数字孪生研究所）；Ginlong Technologies Co., Ltd.（金龙科技有限公司）；Wuhan University（武汉大学）；Zhejiang Key Laboratory of Industrial Intelligence（浙江工业智能关键实验室）
针对多建筑能源负荷预测中的空间依赖建模问题，提出 BuildSTG 时空图神经网络方法，结合图构建、注意力图卷积与图结构解释，并在 Building Data Genome Project 2 上验证其优于多种基线。
Load Forecasting for Households and Energy Communities: Are Deep Learning Models Worth the Effort?
illwerke vkw Endowed Professorship for Energy Efficiency, Energy Research Centre, Vorarlberg University of Applied Sciences（能源效率 endowed 教授职位，能源研究中心， Vorarlberg 应用科学大学）；Digital Factory Vorarlberg GmbH（Vorarlberg 数字工厂有限公司）；Department of Computer Science, University of Innsbruck（计算机科学系，因斯布鲁克大学）
Machine Learning (ML) and Deep Learning (DL) methods are increasingly replacing traditional methods in many domains involved with important decision making activities. DL techniques tailor-made for specific tasks such as image recognition, signal processing, or speech analysis are being introduced at a fast pace with many improvements. However, for the domain of forecasting, the current state in the ML community is perhaps where other domains such as Natural Language Processing and Computer Vision were at several years ago. The field of forecasting has mainly been fostered by statisticians/eco…
