Probabilistic Multi-Regional Solar Power Forecasting with Any-Quantile Recurrent Neural Networks
Faculty of Electrical Engineering, Czestochowa University of Technology（捷斯托霍瓦技术大学电气工程学院）；Faculty of Mathematics and Computer Science, University of Łódź（卢布林大学数学与计算机科学学院）；CAMINO - Centre for Data Analysis, Modelling and Computational Sciences, University of Łódź（卢布林大学CAMINO数据与建模计算科学中心）
The increasing penetration of photovoltaic (PV) generation introduces significant uncertainty into power system operation, necessitating forecasting approaches that extend beyond deterministic point predictions. This paper proposes an any-quantile probabilistic forecasting framework for multi-regional PV power generation based on the Any-Quantile Recurrent Neural Network (AQ-RNN). The model integrates an any-quantile forecasting paradigm with a dual-track recurrent architecture that jointly processes series-specific and cross-regional contextual information, supported by dilated recurrent cells, patch-based temporal modeling, and a dynamic ensemble mechanism.
The proposed framework enables the estimation of calibrated conditional quantiles at arbitrary probability levels within a single trained model and effectively exploits spatial dependencies to enhance robustness at the system level. The approach is evaluated using 30 years of hourly PV generation data from 259 European regions and compared against established statistical and neural probabilistic baselines. The results demonstrate consistent improvements in forecast accuracy, calibration, and prediction interval quality, underscoring the suitability of the proposed method for uncertainty-aware energy management and operational decision-making in renewable-dominated power systems.
Here, we propose a general method for probabilistic time series forecasting. We combine an autoregressive recurrent neural network to model temporal dynamics with Implicit Quantile Networks to learn a large class of distributions over a time-series target. When compared to other probabilistic neural forecasting models on real- and simulated data, our approach is favorable in terms of point-wise prediction accuracy as well as on estimating the underlying temporal distribution.
In this work we propose the use of quantile regression and dilated recurrent neural networks with temporal scaling (MQ-DRNN-s) and apply it to the inventory management task. This model showed a better performance of up to 3.2\% over a statistical benchmark (the quantile autoregressive model with exogenous variables, QAR-X), being better than the MQ-DRNN without temporal scaling by 6\%. The above on a set of 10,000 time series of sales of El Globo over a 53-week horizon using rolling windows of 7-day ahead each week.
This paper develops probabilistic PV forecasters by taking advantage of recent breakthroughs in deep learning. It tailored forecasting tool, named encoder-decoder, is implemented to compute intraday multi-output PV quantiles forecasts to efficiently capture the time correlation. The models are trained using quantile regression, a non-parametric approach that assumes no prior knowledge of the probabilistic forecasting distribution. The case study is composed of PV production monitored on-site at the University of Liège (ULiège), Belgium. The weather forecasts from the regional climate model pro…
Institute of Statistics and Data Science, Nanjing Audit University（南京审计大学统计与数据科学学院）
China has made great achievements in electric power industry during the long-term deepening of reform and opening up. However, the complex regional economic, social and natural conditions, electricity resources are not evenly distributed, which accounts for the electricity deficiency in some regions of China. It is desirable to develop a robust electricity forecasting model. Motivated by which, we propose a Panel Semiparametric Quantile Regression Neural Network (PSQRNN) by utilizing the artificial neural network and semiparametric quantile regression. The PSQRNN can explore a potential linear…
Recurrent neural network-based robust control systems with regional properties and application to MPC design
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano（电子、信息与生物工程系，米兰理工大学）；Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato, Consiglio Nazionale delle Ricerche（先进智能制造工业系统与技术研究所，国家研究院）
African Institute for Mathematical Sciences（非洲数学科学研究所）；University of Montreal（蒙特利尔大学）；Mila（米拉研究所）；Microsoft IDC（微软印度开发中心）；McGill University（麦吉尔大学）；École de Technologie Supérieure（高等技术学院）；CIFAR（加拿大高级研究院）
Locust infestation of some regions in the world, including Africa, Asia and Middle East has become a concerning issue that can affect the health and the lives of millions of people. In this respect, there have been attempts to resolve or reduce the severity of this problem via detection and monitoring of locust breeding areas using satellites and sensors, or the use of chemicals to prevent the formation of swarms. However, such methods have not been able to suppress the emergence and the collective behaviour of locusts. The ability to predict the location of the locust swarms prior to their fo…
To accurately predict the regional spread of Covid-19 infection, this study proposes a novel hybrid model which combines a Long short-term memory (LSTM) artificial recurrent neural network with dynamic behavioral models. Several factors and control strategies affect the virus spread, and the uncertainty arisen from confounding variables underlying the spread of the Covid-19 infection is substantial. The proposed model considers the effect of multiple factors to enhance the accuracy in predicting the number of cases and deaths across the top ten most-affected countries and Australia. The result…
Diffusion Precipitation Generation with Medium-range Weather Forecasting
Tsinghua University（清华大学）；The Chinese University of Hong Kong（香港中文大学）；The Hong Kong University of S & T（香港科技大学）；Sun Yat-Sen University（中山大学）；NSCC-SZ（国家超级计算深圳中心）；China Meteorological Administration（中国气象局）；GBA Weather Research Center（粤港澳大湾区天气研究中心）；Huawei Technologies Co., Ltd（华为技术有限公司）
We present a novel sub-8-bit quantization-aware training (S8BQAT) scheme for 8-bit neural network accelerators. Our method is inspired from Lloyd-Max compression theory with practical adaptations for a feasible computational overhead during training. With the quantization centroids derived from a 32-bit baseline, we augment training loss with a Multi-Regional Absolute Cosine (MRACos) regularizer that aggregates weights towards their nearest centroid, effectively acting as a pseudo compressor. Additionally, a periodically invoked hard compressor is introduced to improve the convergence rate by …
Faculty of Integrated Technologies\ Brunei Darussalam\ Darussalam；Institute of Applied Data Analytics\ Brunei Darussalam\ Darussalam
Machine translation has many applications such as news translation, email translation, official letter translation etc. Commercial translators, e.g. Google Translation lags in regional vocabulary and are unable to learn the bilingual text in the source and target languages within the input. In this paper, a regional vocabulary-based application-oriented Neural Machine Translation (NMT) model is proposed over the data set of emails used at the University for communication over a period of three years. A state-of-the-art Sequence-to-Sequence Neural Network for ML -> EN and EN -> ML translations …
Fraunhofer Heinrich Hertz Institute（弗劳恩霍夫海因里希·赫兹研究所）；University Medical Center Schleswig-Holstein（石勒苏益格-荷尔斯泰因大学医学中心）
Electrical impedance tomography (EIT) is a noninvasive imaging modality that allows a continuous assessment of changes in regional bioimpedance of different organs. One of its most common biomedical applications is monitoring regional ventilation distribution in critically ill patients treated in intensive care units. In this work, we put forward a proof-of-principle study that demonstrates how one can reconstruct synchronously measured respiratory or circulatory parameters from the EIT image sequence using a deep learning model trained in an end-to-end fashion. We demonstrate that one can acc…
University of Hyogo（兵库县立大学）；Vanderbilt University（范德堡大学）；BRAC University（BRAC大学）；North South University（北南大学）
Temperature and rainfall have a significant impact on economic growth as well as the outbreak of seasonal diseases in a region. In spite of that inadequate studies have been carried out for analyzing the weather pattern of Bangladesh implementing the artificial neural network. Therefore, in this study, we are implementing a Long Short-term Memory (LSTM) model to forecast the month-wise temperature and rainfall by analyzing 115 years (1901-2015) of weather data of Bangladesh. The LSTM model has shown a mean error of -0.38oC in case of predicting the month-wise temperature for 2 years and -17.64…
Movia Public Transport（Movia公共交通公司）；Technical University of Denmark（丹麦技术大学）
This paper presents two novel approaches for uncertainty estimation adapted and extended for the multi-link bus travel time problem. The uncertainty is modeled directly as part of recurrent artificial neural networks, but using two fundamentally different approaches: one based on Deep Quantile Regression (DQR) and the other on Bayesian Recurrent Neural Networks (BRNN). Both models predict multiple time steps into the future, but handle the time-dependent uncertainty estimation differently. We present a sampling technique in order to aggregate quantile estimates for link level travel time to yi…
MERCURY: A fast and versatile multi-resolution based global emulator of compound climate hazards
University of Oxford（牛津大学）；Montreal Polytechnique（蒙特利尔理工学院）；Columbia Climate School（哥伦比亚气候学院）；International Institute for Applied Systems Analysis（国际应用系统分析研究所）；Universität Leipzig（莱比锡大学）；IRI THESys；Humboldt-Universität zu Berlin（柏林洪堡大学）；Institute Pierre-Simone-Laplace（皮埃尔-西蒙·拉普拉斯研究所）
Deep Learning and Elicitability for McKean-Vlasov FBSDEs With Common Noise
School of Applied Mathematics, Getulio Vargas Foundation（应用数学学院，古特雷斯基金会）；Department of Statistical Sciences, University of Toronto（统计科学系，多伦多大学）；Oxford-Man Institute for Quantitative Finance, University of Oxford（牛津-曼定量金融研究所，牛津大学）
Beihang University（北京航空航天大学）；University of Central Florida（中佛罗里达大学）；Rutgers University（罗格斯大学）
Graph Convolutional Network (GCN) has been widely applied in transportation demand prediction due to its excellent ability to capture non-Euclidean spatial dependence among station-level or regional transportation demands. However, in most of the existing research, the graph convolution was implemented on a heuristically generated adjacency matrix, which could neither reflect the real spatial relationships of stations accurately, nor capture the multi-level spatial dependence of demands adaptively. To cope with the above problems, this paper provides a novel graph convolutional network for tra…
TSCoNet: A Two-Stage Copula CNN-LSTM for Uncertainty-Aware Spatio-Temporal Forecasting
Department of Mathematical Sciences, Ball State University（数学科学系，巴尔的摩州立大学）；Statistics Discipline, Division of Science and Mathematics, University of Minnesota-Morris（统计学学科，明尼苏达-莫里斯大学）
Efficiently Transforming Neural Networks into Decision Trees: A Path to Ground Truth Explanations with RENTT
Fraunhofer Institute for Manufacturing Engineering and Automation IPA（弗劳恩霍夫制造工程与自动化研究所）；Institute of Industrial Manufacturing and Management IFF, University of Stuttgart（工业制造与管理研究所，斯图加特大学）；Technical University Munich（慕尼黑技术大学）
