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In recent decades, the traditional monopolistic energy exchange market has been replaced by deregulated, competitive marketplaces in which electricity may be purchased and sold at market prices like any other commodity. As a result, the deregulation of the electricity industry has produced a demand for wholesale organized marketplaces. Price predictions, which are primarily meant to establish the market clearing price, have become a significant factor to an energy company’s decision making and strategic development. Recently, the fast development of deep learning algorithms, as well as the deployment of front-end metaheuristic optimization approaches, have resulted in the efficient development of enhanced prediction models that are used for electricity price forecasting. In this paper, the development of six highly accurate, robust and optimized data-driven forecasting models in conjunction with an optimized Variational Mode Decomposition method and the K-Means clustering algorithm for short-term electricity price forecasting is proposed. In this work, we also establish an Inverted and Discrete Particle Swarm Optimization approach that is implemented for the optimization of the Variational Mode Decomposition method. The prediction of the day-ahead electricity prices is based on historical weather and price data of the deregulated Greek electricity market. The resulting forecasting outcomes are thoroughly compared in order to address which of the two proposed divide-and-conquer preprocessing approaches results in more accuracy concerning the issue of short-term electricity price forecasting. Finally, the proposed technique that produces the smallest error in the electricity price forecasting is based on Variational Mode Decomposition, which is optimized through the proposed variation of Particle Swarm Optimization, with a mean absolute percentage error value of 6.15%.

Background Post bariatric hypoglycaemic (PBH) is a late complication of weight loss surgery, characterised by critically low blood glucose levels following meal-induced glycaemic excursions. The disabling consequences of PBH underline the need for the development of a decision support system (DSS) that can warn individuals about upcoming PBH events, thus enabling preventive actions to avoid impending episodes. In view of this, we developed various algorithms based on linear and deep learning models to forecast PBH episodes in the short-term. Methods We leveraged a dataset obtained from 50 patients with PBH after Roux-en-Y gastric bypass, monitored for up to 50 days under unrestricted real-life conditions. Algorithms’ performance was assessed by measuring Precision, Recall, F1-score, False-alarms-per-day and Time Gain (TG). Results The run-to-run forecasting algorithm based on recursive autoregressive model (rAR) outperformed the other techniques, achieving Precision of 64.38%, Recall of 84.43%, F1-score of 73.06%, a median TG of 10 min and 1 false alarm every 6 days. More complex deep learning models demonstrated similar median TG but inferior forecasting capabilities with F1-score ranging from 54.88% to 64.10%. Conclusions Real-time forecasting of PBH events using CGM data as a single input imposes high demands on various types of prediction algorithms, with CGM data noise and rapid postprandial glucose dynamics representing the key challenges. In this study, the run-to-run rAR yielded most satisfactory results with accurate PBH event predictive capacity and few false alarms, thereby indicating potential for the development of DSS for people with PBH.

Academic buildings in a typical university campus occupy 42% of the total space and are responsible for nearly 50 percent of the total energy use and carbon emissions of the campus. Forecasting of energy consumption in this energy intensive building category could help higher education institutions in taking energy saving initiatives and in revising their building operating strategies. Reliable predictive techniques does not only help in forecasting a building’ energy consumption, but also help in identifying a variety of factors affecting the energy consumption of that building. This study attempts to forecast and benchmark the daily electricity consumption of an academic building situated in London, United Kingdom using two different data-driven modeling techniques, i.e., Multiple Regression and Artificial Neural Network. Hourly dataset for the electricity consumption was available for the period 2007 to 2011 from the smart meter whereas hourly data of different factors such as ambient temperature, relative humidity, wind speed and solar radiation were downloaded from the website of environmental research group of Kings College London. The performances of the two predictive models have been critically analyzed by comparing their predicted consumption with a real dataset of the same building for the year 2012. A comparison shows that both Multiple Regression (MR) and Artificial Neural Network (ANN) perform reasonably well with a Mean Absolute Percentage Error (MAPE) of 3.34% and 2.44% for working days and 5.12% and 4.59% for non-working days respectively. ANN performs slightly better than MR. This energy consumption forecasting approach can easily be adapted for predicting energy use of similar buildings.

Machine Learning (ML) is having a profound impact in the domain of Weather and Climate Prediction. A recent development in this area has been the emergence of fully data‐driven ML prediction models which routinely claim superior performance to that of traditional physics‐based models. We examine some aspects of the forecasts produced by three of the leading current ML models, Pangu‐Weather, FourCastNet and GraphCast, with a focus on their fidelity and physical consistency. The main conclusion is that these ML models are not able to properly reproduce sub‐synoptic and mesoscale weather phenomena and lack the fidelity and physical consistency of physics‐based models and this has impacts on the interpretation of their forecasts and their perceived skill. Balancing forecast skill and physical realism will be an important consideration for future ML models. Plain Language Summary The last few years have seen the emergence of a new type of weather forecasting models completely based on ML technologies. These models do not codify the physical laws governing atmospheric dynamics but learn to produce forecasts from historical reanalysis data sets of the Earth system like the ECMWF ERA5. In this work we show that the forecasts produced by some of the leading ML models are physically inconsistent and should be better considered as post‐processing algorithms rather than realistic simulators of the atmosphere. The challenge for next generation of ML models for weather forecasting will be to improve their fidelity while maintaining forecast skill. Key Points Forecasts from Machine Learning (ML) models have energy spectra notably different from those of their training reanalysis fields and Numerical Weather Prediction models This results in overly smooth predictions and weather phenomena at spatial scales shorter than 300–400 km are not properly represented Fundamental physical balances and derived quantities are not realistically represented in the forecasts of the ML models

The data‐driven approaches for medium‐range weather forecasting are recently shown to be extraordinarily promising for ensemble forecasting due to their fast inference speed compared to the traditional numerical weather prediction models. However, their forecast accuracy can hardly match the state‐of‐the‐art operational ECMWF Integrated Forecasting System (IFS) model. Previous data‐driven approaches perform ensemble forecasting using some simple perturbation methods, like the initial condition perturbation and the Monte Carlo dropout. However, their ensemble performance is often limited arguably by the sub‐optimal ways of applying perturbation. We propose a Swin Transformer‐based Variational Recurrent Neural Network (SwinVRNN), which is a stochastic weather forecasting model combining a SwinRNN predictor with a perturbation module. SwinRNN is designed as a Swin Transformer‐based recurrent neural network, which predicts the future states deterministically. Furthermore, to model the stochasticity in the prediction, we design a perturbation module following the Variational Auto‐Encoder paradigm to learn the multivariate Gaussian distributions of a time‐variant stochastic latent variable from the data. Ensemble forecasting can be easily performed by perturbing the model features leveraging the noise sampled from the learned distribution. We also compare four categories of perturbation methods for ensemble forecasting, that is, fixed distribution perturbation, learned distribution perturbation, MC dropout, and multi model ensemble. Comparisons on the WeatherBench data set show that the learned distribution perturbation method using our SwinVRNN model achieves remarkably improved forecasting accuracy and reasonable ensemble spread due to the joint optimization of the two targets. More notably, SwinVRNN surpasses operational IFS on the surface variables of the 2‐m temperature and the 6‐hourly total precipitation at all lead times up to 5 days (Code is available at https://github.com/tpys/wwprediction). Plain Language Summary Ensemble forecasting plays a crucial role in numerical weather prediction (NWP), since a single deterministic model is hard to forecast the chaotic atmosphere conditions. Recent works have begun to explore the data‐driven based ensemble methods due to their rapid prediction speed over traditional NWP. We develop an efficient and effective deep learning model capable of generating large ensemble forecasts with high prediction accuracy and low prediction time cost. The predicted ensemble members have much greater and more reasonable ensemble spread, and better coverage of the ground truth, compared to the prior data‐driven methods. Moreover, our model surpasses the state‐of‐the‐art operational NWP model on the surface atmospheric variables of the 2‐m temperature and the 6‐hourly total precipitation, offering an impressive probability weather prediction baseline. Key Points A transformer‐based variational model called SwinVRNN is developed for medium‐range weather prediction The proposed SwinVRNN can effectively generate large ensemble forecasts with great prediction accuracy and reasonable ensemble spread The model sets a new state‐of‐the‐art among data‐driven models and surpasses the Integrated Forecast System on key atmospheric variables

An interpretable deep inference forecasting model is designed to improve the forecasting capability of sea surface variables. By incorporating the air‐sea coupling mechanism as a dynamic constraint, the interpretability and forecasting performance of the model are improved. More specifically, our findings underscore the critical role of air‐sea interactions in forecasting sea surface variables, especially sea surface temperature (SST) variations induced by tropical cyclones (TCs). Additionally, Liang‐Kleeman information flow (IF), a causal inference method, is introduced to optimize the selection of predictors. Using satellite remote sensing data, our study demonstrates the model's capability in realizing sea surface multivariate forecasts in the South China Sea (SCS) within 10 days. More importantly, the experimental results prove the applicability of the model in both normal and extreme weather conditions, highlighting its effectiveness in enhancing sea surface variables forecasting. Plain Language Summary There are exchanges of momentum, heat, and mass between the ocean and the atmosphere. The sea surface, as a crucial interface for these exchanges, plays a pivotal role in the earth's climate system. Consequently, accurate prediction of sea surface variables is vital for understanding climate dynamics. Despite the considerable forecasting capabilities demonstrated by intelligent forecasting techniques, they still face issues of poor interpretability and low forecasting skills under extreme conditions compared to numerical models. The main reason is that previous intelligent forecasting models often focus on the evolution of a single variable, or only consider interactions within the ocean, and do not forecast under air‐sea coupling conditions. Such practice leads to incomplete systems and incoordination between air‐sea variables, thus failing to describe the interfacial behaviors of the ocean and atmosphere under extreme conditions such as typhoons. This study constructs an interpretable deep inference forecasting model for sea surface variables within the air‐sea coupling framework, illustrating the importance of considering air‐sea interactions to improve the forecasting performance of sea surface variables. Additionally, the model improves the prediction accuracy of sea surface variables (especially sea surface temperature) under extreme weather conditions. Key Points An interpretable forecasting model is proposed for the evolution of sea surface variables in the South China Sea Air‐sea interactions are incorporated as dynamic constraints to improve the forecasting skill and interpretability of the model The model has good prediction performance for sea surface temperature under extreme conditions

Numerical weather prediction models require ever‐growing computing time and resources but, still, have sometimes difficulties with predicting weather extremes. We introduce a data‐driven framework that is based on analog forecasting (prediction using past similar patterns) and employs a novel deep learning pattern‐recognition technique (capsule neural networks, CapsNets) and an impact‐based autolabeling strategy. Using data from a large‐ensemble fully coupled Earth system model, CapsNets are trained on midtropospheric large‐scale circulation patterns (Z500) labeled 0–4 depending on the existence and geographical region of surface temperature extremes over North America several days ahead. The trained networks predict the occurrence/region of cold or heat waves, only using Z500, with accuracies (recalls) of 69–45% (77–48%) or 62–41% (73–47%) 1–5 days ahead. Using both surface temperature and Z500, accuracies (recalls) with CapsNets increase to ∼80% (88%). In both cases, CapsNets outperform simpler techniques such as convolutional neural networks and logistic regression, and their accuracy is least affected as the size of the training set is reduced. The results show the promises of multivariate data‐driven frameworks for accurate and fast extreme weather predictions, which can potentially augment numerical weather prediction efforts in providing early warnings. Key Points A data‐driven extreme weather prediction framework based on analog forecasting and deep learning pattern‐recognition methods is proposed Extreme surface temperature events over North America are skillfully predicted using only midtropospheric large‐scale circulation patterns More advanced deep learning methods are found to yield better forecasts, encouraging novel methods tailored for climate/weather data

We introduce a data-driven forecasting method for high-dimensional chaotic systems using long short-term memory (LSTM) recurrent neural networks. The proposed LSTM neural networks perform inference of high-dimensional dynamical systems in their reduced order space and are shown to be an effective set of nonlinear approximators of their attractor. We demonstrate the forecasting performance of the LSTM and compare it with Gaussian processes (GPs) in time series obtained from the Lorenz 96 system, the Kuramoto–Sivashinsky equation and a prototype climate model. The LSTM networks outperform the GPs in short-term forecasting accuracy in all applications considered. A hybrid architecture, extending the LSTM with a mean stochastic model (MSM–LSTM), is proposed to ensure convergence to the invariant measure. This novel hybrid method is fully data-driven and extends the forecasting capabilities of LSTM networks.

Upon the introduction of machine learning (ML) and its variants, in the form that we know today, to the landslide community, many studies have been carried out to explore the usefulness of ML in landslide research and to look at some classic landslide problems from an ML point of view. ML techniques, including deep learning methods, are becoming popular to model complex landslide problems and are starting to demonstrate promising predictive performance compared to conventional methods. Almost all the studies published in the literature in recent years belong to one of the following three broad categories: landslide detection and mapping, landslide spatial forecasting in the form of susceptibility mapping, and landslide temporal forecasting. In this paper, we present a brief overview of ML techniques, provide a general summary of the landslide studies conducted, in recent years, in the three above-mentioned categories, and make an attempt to critically evaluate the use of ML methods to model landslide processes. The paper also provides suggestions for future use of these powerful data-driven techniques in landslide studies.

Data‐driven weather models have shown the potential to match the accuracy of state‐of‐the‐art numerical weather predictions (NWPs). However, existing data‐driven forecasting models still have limitations in operational applications. For example, most of them are predominantly trained via fifth‐generation climate reanalysis data (ERA5). However, in actual forecasting operations, the models are usually initiated by analysis fields instead of reanalysis data; this leads to a mismatch between the training data used by machine learning (ML) forecasting models and the actual operational data. To address this issue, we attempt to fine‐tune the data‐driven model with the initiation fields in operation. This study first develops a fine‐tuned Pangu Weather Model (PGW) by integrating forecasting system (IFS) analysis data from 2021 to 2022 and conducts a comprehensive evaluation of its performance. By comparing the fine‐tuned version (PGW_O) with the public version (PGW_P) against IFS models with different resolutions (IFS_L at 0.25° and IFS_H at 0.1°), this research highlights advancements in data‐driven forecasting methodologies. The models are tested on data from South China, a region with dense meteorological observation networks, over a three‐month period, encompassing a detailed case study of Tropical Cyclone Haikui (2023). The findings show that with the forecast activity (FA) level comparable to PGW_P, PGW_O significantly reduces the root mean square error (RMSE) and mean error (ME) across upper atmospheric variables and demonstrates superior accuracy in predicting surface elements. The operational relevance of these models is evaluated through both ERA5 reanalysis and surface observations, revealing that fine‐tuning with IFS data enhances PGW compatibility and forecasting precision, particularly for severe weather events. To improve the forecasting ability within the operational framework, the Pangu Weather Model (PGW) is fine‐tuned for the first time via integrated forecasting system (IFS) analysis data from 2021 to 2022. Compared with the public version of the PGW (PGW_P) and IFS models, the fine‐tuned Pangu Weather Model (PGW_O) has the best forecasting ability, particularly for the upper variables, where the fine‐tuning effects are especially pronounced.