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As El Niño's little brother in the equatorial Atlantic Ocean, Atlantic Niño affects the climate variability in the tropical Atlantic Ocean and the vicinity. In 2019–2021, two extremely strong Atlantic Niños occurred with peaks in January 2020 and July 2021, respectively. The coupling between the ocean and atmosphere associated with the Atlantic Niños is similar to that associated with El Niño‐Southern Oscillation. Both the Atlantic Niños were triggered and modulated by a Benguela Niño‐like warming, through inducing wind stress anomalies in the South and equatorial Atlantic Ocean. In addition to the atmosphere‐ocean coupling at intraseasonal‐interseasonal time scales, interdecadal and longer time scale variation amplified the Atlantic Niños. Model predictions only capture the evolution of the Atlantic Niños at a 1‐month lead, consistent with the low prediction skill for sea surface temperature anomalies in the tropical Atlantic Ocean. Plain Language Summary Two extremely strong Atlantic Niños, characterized by anomalous warming in the equatorial Atlantic Ocean surface, occurred in January 2020 and July 2021. The development of these two events resulted from the coupling between the atmosphere and ocean in a way that is analogous to the El Niño‐Southern Oscillation in the equatorial Pacific. A unique feature of these two Atlantic events is the prominent role of anomalous warming in the vicinity of the Benguela coast that preceded the equatorial Atlantic warming. Both intraseasonal‐interseasonal and interdecadal and longer time scale variations contribute to the strength of the Atlantic Niños. In prediction, the evolution of the Atlantic Niños is captured only in the near‐term and failed beyond 1 month. That may partially be due to the low predictability of the Benguela Niño‐like warming, an indication of prediction challenge. Key Points The two extremely strong Atlantic Niños in 2019–2021 are associated with a basin‐wide atmosphere‐ocean coupling The two Atlantic Niños are triggered by Benguela warming and predicted in the near term by models The events are amplified by the in‐phase variations of the intraseasonal‐interseasonal and interdecadal‐trend variations

Atlantic Niño is a leading mode of climate variability in the tropical Atlantic Ocean with important regional impacts. Tropical Atlantic sea surface temperatures (SSTs) connected with Atlantic Niño exhibit notable multi‐time scale variations, including a quasi‐linear warming trend and sub‐annual to interdecadal variability associated with different physical processes. Contrasting the tropical Pacific SST associated with the El Niño‐Southern Oscillation (ENSO), which has a weak trend and quasi‐periodic oscillatory variability with a period of 2–7 years, the ATL3 index, the primary index for monitoring the Atlantic Niño, has no dominant time scale, that is likely responsible for its low prediction skills. Following the same spirit as the relative Niño3.4 index, we demonstrate that a relative ATL3 index, which is defined as the difference between the raw ATL3 index and the global SST anomaly, provides distinct advantages for monitoring the sub‐annual‐to‐interdecadal variations for Atlantic Niño in real time. Plain Language Summary As El Niño's little brother, Atlantic Niño affects the climate variability in the tropical Atlantic Ocean and the South American and West African coastal countries. Different from the El Niño‐Southern Oscillation, a unique feature of tropical Atlantic sea surface temperatures (SSTs) connected with Atlantic Niño is the distinct variations at different time scales, ranging from monthly to multi‐decadal. Interestingly, the amplitudes of the variations for the primary SST index for monitoring Atlantic Niño (ATL3) are comparable at sub‐annual and interannual time scales, making Atlantic Niño's prediction more challenging and with much lower skill than ENSO. Despite the inherent prediction and monitoring challenges for Atlantic Niño, we proposed a relative Atlantic Niño index, which is defined as the difference between the raw ATL3 index and the global averaged SST anomaly, as an improved metric to monitor the sub‐annual‐to‐interdecadal variations superimposed on the warming trend in real time. Key Points The ATL3 index has no dominant time scales with an appreciable contribution from the linear trend Compared with El Niño‐Southern Oscillation, Atlantic Niño has lower prediction skills with different seasonal variation A relative ATL3 index can be used to monitor the sub‐annual‐to‐interdecadal variations in Atlantic Niño in real time

The distributed integrated multi‐energy system (DIMS) has many advantages in terms of the utilisation of renewable energy sources and clean energy. Operation strategies for the DIMS based on a real‐time profile have been extensively studied. In a DIMS online optimisation problem, besides fluctuations in the renewable energy output and load, inconsistent time scales of the transport dynamics of different energy flows and non‐ideal communication (involving communication uncertainty and latency) result in suboptimal operation in dispatch scheduling. An online multi‐time‐scale optimal operation strategy is proposed for the DIMS to respond to the above challenges, using a hybrid algorithm comprising a model predictive control method and distributed collaborative consensus algorithm (CCA). The approach is based on a hierarchy, comprising rolling optimisation and adjustment. A rolling optimisation is established to schedule operations according to the latest forecast and status information. The rolling dispatch is then adjusted according to the ultrashort‐term adjustment using the CCA. Meanwhile, the effect of the information transmission environment on real‐time scheduling is considered, and the robust CCA is improved for the implementation of strategies under non‐ideal communication conditions. Case studies and results are presented and discussed to show the effectiveness of the proposed approach with the better matching between demand and supply.

Solar irradiance forecasting is an inevitable and most significant process in grid-connected photovoltaic systems. Solar power is highly non-linear, and thus to manage the grid operation efficiently, with irradiance forecasting for various timescales, such as an hour ahead, a day ahead, and a week ahead, strategies are developed and analysed in this article. However, the single time scale model can perform better for that specific time scale but cannot be employed for other time scale forecasting. Moreover, the data consideration for single time scale forecasting is limited. In this work, a multi-time scale model for solar irradiance forecasting is proposed based on the multi-task learning algorithm. An effective resource sharing scheme between each task is presented. The proposed multi-task learning algorithm is implemented with a long short-term memory (LSTM) neural network model and the performance is investigated for various time scale forecasting. The hyperparameter estimation of the proposed LSTM model is made by a hybrid chicken swarm optimizer based on combining the best features of both the chicken swarm optimization algorithm (CSO) and grey wolf optimization (GWO) algorithm. The proposed model is validated, comparing existing methodologies for single timescale forecasting, and the proposed strategy demonstrated highly consistent performance for all time scale forecasting with improved metric results.

The large-scale integration of renewable energy has reduced power system flexibility and exacerbated supply–demand imbalances. In industrial parks, the combined variability of high energy-consuming industrial loads and photovoltaic (PV) generation further complicates the energy management challenge. Aiming to enhance the operational flexibility of industrial parks and mitigate supply–demand imbalances, this paper proposes a multi-time-scale stochastic energy management strategy that accounts for the uncertainty associated with PV generation. First, a conditional generative adversarial network (CGAN) is employed to generate the representative PV generation scenarios, thereby enabling the modeling of PV generation uncertainty within the optimal dispatch model. Considering the coupling mechanisms and control characteristics of various regulation resources within the industrial park, a multi-time-scale dispatch model is developed. In the day-ahead dispatch phase, the operational costs are minimized by optimizing the production plans of industrial loads. In contrast, in the intraday phase, the more flexible measures, such as adjusting the tap positions of arc furnaces and controlling the charge/discharge of energy storage systems, are employed to smooth power fluctuations within the park. A case study validated the effectiveness of the proposed approach, demonstrating a 7.56% reduction in power fluctuations and a 4.34% decrease in daily operating costs. These results highlight the significance of leveraging industrial loads in park-level systems to enhance cost efficiency and renewable energy integration.

As an important factor affecting the mangrove wetland ecosystem, water quality has become the focus of attention in recent years. Therefore, many studies have focused on the prediction of water quality to help establish a regulatory framework for the assessment and management of water pollution and ecosystem health. To make a more accurate and comprehensive forecast analysis of water quality, we propose a method for water quality prediction based on the multi-time scale bidirectional LSTM network. In the method, we improve data integrity and data volume through data preprocessing. And the network processes input data forward and backward and considers the dependencies at multiple time scales. Besides, we use the Box–Behnken experimental design method to adjust hyper-parameters in the process of modeling. In this study, we apply this method to the water quality prediction research of Beilun Estuary, and the performance of our proposed model is evaluated and compared with other models. The experiment results show that this model has better performance in water quality prediction than that of using LSTM or bidirectional LSTM alone. Graphical Abstract Schematic of research work

In many sequential decision-making problems we may want to manage risk by minimizing some measure of variability in rewards in addition to maximizing a standard criterion. Variance related risk measures are among the most common risk-sensitive criteria in finance and operations research. However, optimizing many such criteria is known to be a hard problem. In this paper, we consider both discounted and average reward Markov decision processes. For each formulation, we first define a measure of variability for a policy, which in turn gives us a set of risk-sensitive criteria to optimize. For each of these criteria, we derive a formula for computing its gradient. We then devise actor-critic algorithms that operate on three timescales—a TD critic on the fastest timescale, a policy gradient (actor) on the intermediate timescale, and a dual ascent for Lagrange multipliers on the slowest timescale. In the discounted setting, we point out the difficulty in estimating the gradient of the variance of the return and incorporate simultaneous perturbation approaches to alleviate this. The average setting, on the other hand, allows for an actor update using compatible features to estimate the gradient of the variance. We establish the convergence of our algorithms to locally risk-sensitive optimal policies. Finally, we demonstrate the usefulness of our algorithms in a traffic signal control application.

Accurate trend prediction of valve internal leakage is crucial for the safe and economical operation of thermal power units. To address the issues of prediction lag and insufficient accuracy in existing methods when dealing with the dynamic changes in internal leakage, this paper proposed an Improved Autoregressive Integrated Moving Average–Generalized Autoregressive Conditional Heteroskedasticity (IARIMA-GARCH) method that integrated Multi-Time-Scale Decomposition, an Improved ARIMA (IARIMA) model, and an Improved GARCH (IGARCH) model for accurate prediction of drain valve internal leakage. First, using a Multi-Time-Scale Decomposition method based on sampling at different time intervals, the original valve internal leakage time series were reconstructed into three characteristic subsequences—short-term, medium-term, and long-term—to capture the evolutionary features at various time scales. Then, an IARIMA model, employing the Huber loss function for robust parameter estimation, was constructed as the leakage prediction model to effectively suppress the interference of outliers. Simultaneously, an IGARCH model was built as the leakage volatility prediction model by introducing the previous moment’s volatility to correct the current residual, establishing a feedback mechanism between the mean and volatility equations, thereby enhancing the characterization of volatility clustering. Finally, using a weight coefficient dynamic calculation method based on RMSE, the Multi-Time-Scale prediction results of each subsequence were fused to obtain the final predicted valve internal leakage. Taking the main steam drain valve of a thermal power plant as the research object, and using Mean Absolute Error (MAE), Root-Mean-Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and symmetric Mean Absolute Percentage Error (sMAPE) as evaluation metrics, a case study on trend prediction of drain valve internal leakage was conducted, comparing the proposed method with ARIMA, Long Short-Term Memory networks (LSTM) and eXtreme Gradient Boosting (XGBoost) methods. The results showed that compared to ARIMA, LSTM and XGBoost, the proposed IARIMA-GARCH method achieved the lowest values on error metrics such as Mean Absolute Error (MAE), Root-Mean-Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and symmetric Mean Absolute Percentage Error (sMAPE), and its Coefficient of Determination (R2) is closest to 1. The standardized residual sequence most closely resembled a white noise sequence with zero mean and unit variance, and its distribution was the closest to a normal distribution. This proved that the IARIMA-GARCH method possessed higher prediction accuracy, stronger dynamic adaptability, and superior statistical robustness, providing an effective solution for valve condition prediction and predictive maintenance.

This paper studies the collaborative inertia-frequency regulation strategies for the high renewable energy penetrated low inertia power system. Firstly, a systematic investigation is conducted to reveal the dominant dynamic characteristics and the possible challenges for such systems, and then proved the effectiveness of virtual inertia. Subsequently, a novel Laguerre-based model predictive control strategy is accordingly pro-posed, which ensures a better system states convergence ability and a reduced computational burden. The controller takes into account the system’s dual-mode feature to ensure timely response for both the inertia and the frequency support. Then, the regulation quality, operational burden and the cost are mathematically defined. The control trajectory is determined by the rolling optimization. The Gravity Searching Algorithm is utilized to determine the optimal control parameters. Finally, the proposed control strategy is validated through five case studies, demonstrating enhanced robustness, superior dynamic performance and cost-effective operation. This study provides new insights for the analysis and control strategies of the high RE penetrated low inertia systems.

Drought simulation and prediction are of great significance to drought early warning. However, it is difficult to predict hydrological drought in data-scarce areas. To address this problem, Tarim River Basin was selected as a typical representative of the data-scarce inland river basin in China, we constructed a hybrid model by combining the complete ensemble empirical mode decomposition with adaptive noise and the long short-term memory method to predict hydrological drought from 2022 to 2100 based on CMIP6. The results show that meteorological drought has quasi-3-month, quasi-5-month, quasi-7-month, quasi-1-year, quasi-2-year, quasi-4-year, quasi-9-year, quasi-17-year and quasi-54-year cycles. Hydrological drought has quasi-3-month, quasi-5-month, quasi-6-month, quasi-1-year, quasi-2-year, quasi-4-year, quasi-9-year, quasi-29-year and quasi-32-year cycles. The components of meteorological drought and hydrological drought have significant correlations on monthly, interannual, and interdecadal scales, with correlation coefficients of 0.282, 0.573, and 0.340, respectively, and p values of 0.000. The hybrid model had a better prediction accuracy (R2 = 0.951, MAE = 0.131, NSE = 0.951, d index = 0.987) than previous studies. The trend of the hydrological drought index in the sustainable development model (SSP1-2.6) shows a trend of increasing severity with a rate of − 0.004/10 years from 2022 to 2100. And from the sustainable development model (SSP1-2.6) to the unbalanced development model (SSP5-8.5), the hydrological drought gradually becomes more serious. This study provides a new mechanism for predicting hydrological drought in data-scarce areas and is of great significance for the early warning of hydrological drought in this area.