The El Niño Southern Oscillation (ENSO) is one of the most prominent interannual climate phenomena. Early and reliable ENSO forecasting remains a crucial goal, due to its serious implications for ...economy, society, and ecosystem. Despite the development of various dynamical and statistical prediction models in the recent decades, the “spring predictability barrier” remains a great challenge for long-lead-time (over 6 mo) forecasting. To overcome this barrier, here we develop an analysis tool, System Sample Entropy (SysSampEn), to measure the complexity (disorder) of the system composed of temperature anomaly time series in the Niño 3.4 region. When applying this tool to several near-surface air temperature and sea surface temperature datasets, we find that in all datasets a strong positive correlation exists between the magnitude of El Niño and the previous calendar year’s SysSampEn (complexity). We show that this correlation allows us to forecast the magnitude of an El Niño with a prediction horizon of 1 y and high accuracy (i.e., root-mean-square error = 0.23° C for the average of the individual datasets forecasts). For the 2018 El Niño event, our method forecasted a weak El Niño with a magnitude of 1.11 ± 0.23° C. Our framework presented here not only facilitates long-term forecasting of the El Niño magnitude but can potentially also be used as a measure for the complexity of other natural or engineering complex systems.
•A fused speed prediction method is devised based on Markov chain and neural network.•The inputs of neural network are simplified by principal component analysis.•The weights and threshold of neural ...network is optimized by genetic algorithm.•The predicted speed is validated effective in improving operating economy of vehicle.
Prediction of short-term future driving conditions can contribute to energy management of plug-in hybrid electric vehicles and subsequent improvement of their fuel economy. In this study, a fused short-term forecasting model for driving conditions is established by incorporating the stochastic forecasting and machine learning. The Markov chain is applied to calculate the transition probability of historical driving data, by which the stochastic prediction is conducted based on the Monte Carlo algorithm. Then, a neural network is employed to learn the current driving information and main knowledge after the simplified correlation of characteristic parameters, and meanwhile the genetic algorithm is adopted to optimize the initial weight and thresholds of networks. Finally, the short-term velocity prediction is achieved by combining them, and the overall performance is evaluated by four typical criteria. Simulation results indicate that the proposed fusion algorithm outperforms the single Markov model, the radial basis function neural network and the back propagation neural network with respect to the prediction precision and the difference distribution between expectation and prediction values. In addition, a case study is conducted by applying the built prediction algorithm in energy management of a plug-in hybrid electric vehicle, and simulation results highlight that the proposed algorithm can supply preferable velocity prediction, thereby facilitating improvement of the operating economy of the vehicle.
Achieving accurate, real-time estimates of disease activity is challenged by delays in case reporting. "Nowcast" approaches attempt to estimate the complete case counts for a given reporting date, ...using a time series of case reports that is known to be incomplete due to reporting delays. Modeling the reporting delay distribution is a common feature of nowcast approaches. However, many nowcast approaches ignore a crucial feature of infectious disease transmission-that future cases are intrinsically linked to past reported cases-and are optimized to one or two applications, which may limit generalizability. Here, we present a Bayesian approach, NobBS (Nowcasting by Bayesian Smoothing) capable of producing smooth and accurate nowcasts in multiple disease settings. We test NobBS on dengue in Puerto Rico and influenza-like illness (ILI) in the United States to examine performance and robustness across settings exhibiting a range of common reporting delay characteristics (from stable to time-varying), and compare this approach with a published nowcasting software package while investigating the features of each approach that contribute to good or poor performance. We show that introducing a temporal relationship between cases considerably improves performance when the reporting delay distribution is time-varying, and we identify trade-offs in the role of moving windows to accurately capture changes in the delay. We present software implementing this new approach (R package "NobBS") for widespread application and provide practical guidance on implementation.
Unit Commitment With Gas Network Awareness Byeon, Geunyeong; Van Hentenryck, Pascal
IEEE transactions on power systems,
2020-March, 2020-3-00, 20200301, Letnik:
35, Številka:
2
Journal Article
Recenzirano
Odprti dostop
Recent changes in the fuel mix for electricity generation and, in particular, the increase in Gas-Fueled Power Plants (GFPP), have created significant interdependencies between the electrical power ...and natural gas transmission systems. However, despite their physical and economic couplings, these networks are still operated independently, with asynchronous market mechanisms. This mode of operation may lead to significant economic and reliability risks in congested environments as revealed by the 2014 polar vortex event experienced by the northeastern United States. To mitigate these risks, while preserving the current structure of the markets, this paper explores the idea of introducing gas network awareness into the standard unit commitment model. Under the assumption that the power system operator has some (or full) knowledge of gas demand forecast and the gas network, the paper proposes a tri-level mathematical program where natural gas zonal prices are given by the dual solutions of natural-gas flux conservation constraints and commitment decisions are subject to bid-validity constraints that ensure the economic viability of the committed GFPPs. This tri-level program can be reformulated as a single-level Mixed-Integer Second-Order Cone program which can then be solved using a dedicated Benders decomposition. The approach is validated on a case study for the Northeastern United States 1 that can reproduce the gas and electricity price spikes experienced during the early winter of 2014. The results on the case study demonstrate that gas awareness in unit commitment is instrumental in avoiding the peaks in electricity prices while keeping the gas prices to reasonable levels.
Accurate forecast of mid-term power demand ensures the stable and efficient operation of power systems, and is essential for the construction of energy interconnections and renewable energy ...microgrids. However, the implementation of strategies aimed at reducing carbon emissions such as electric energy substitution increases the uncertainty of power demand. In order to effectively extract the changing characteristics of electricity demand, this paper firstly proposes a secondary decomposition model based on a seasonal-trend decomposition procedure based on Loess (STL) and variational mode decomposition (VMD) to reduce sequence complexity. Then, different models such as grey wolf optimized support vector regression (GWO-SVR) for different sequences were used to achieve the best prediction effect. In addition, this study used the Markov chain model to further improve the prediction accuracy based on interval optimization. To verify the effectiveness of the hybrid model, a case study was conducted on the monthly electricity consumption in Zhejiang Province, China. The results show that the proposed model effectively extracts the characteristics of changes in electricity demand and greatly improves the forecast accuracy.
•A hybrid model based on secondary decomposition and interval optimization is proposed.•Setups for determining the number of components of VMD are proposed.•The intelligent reconstruction method can be used in the VMD model.•The Markov chain can be used to further improve prediction accuracy.
Accurate and reliable multi-step wind speed forecasting is extremely crucial for the economic and safe operation of power systems. A novel dynamic hybrid model, which combines an adaptive secondary ...decomposition (ASD), a leave-one-out cross-validation-based regularized extreme learning machine (LRELM) and the backtracking search algorithm (BSA), is proposed to mitigate the practical difficulties of the traditional decomposition-ensemble forecasting models (DEFMs) through adaptive dynamic decomposing and modeling when new data is added. The new ASD method, which fuses ensemble empirical mode decomposition (EEMD), adaptive variational mode decomposition (AVMD) with sample entropy (SE), is developed for smoothing the raw series to reduce computational time as well as enhance generalization and stability of forecasting models. BSA is employed to optimize LRELM to overcome the drawback of instability. To validate its efficacy, the proposed model and thirteen benchmark models are compared by diverse lead-time forecasting of several real cases. Comprehensive comparisons with a coherent set of indices suggest that the proposed model is an effective and powerful tool for short-term wind speed forecasting not only from the perspective of reliability and sharpness but also from the view of overall skills.
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•Proposed an adaptive hybrid model to mitigate practical difficulties of traditional DEFMs.•Integrating EEMD, AVMD and sample entropy for data preprocessing.•The LRELM model with parameters optimized by BSA is built to do forecasting.•The hybrid decomposition method ASD made more contributions than the BSA.
Increasingly, humanitarian organizations have opened regional warehouses and pre-positioned resources locally. Choosing appropriate locations is not easy and frequently based on opportunities rather ...than rational decisions. Dedicated decision-support systems could help humanitarian practitioners design their supply networks. Academic literature suggests the use of commercial sector models but rarely considers the constraints and specific context of humanitarian operations, such as obtaining accurate data, high uncertainties, limited budgets and increasing pressure on cost efficiency. We propose a tooled methodology to properly support humanitarian decision makers in the design of their supply chains. Our contribution is based on the definition of aggregate scenarios to reliably forecast demand using past disaster data and future trends. Demand for relief items based on these scenarios is then fed to a mixed-integer linear programming model in order to improve current supply networks. The specifications of this model have been defined in close collaboration with humanitarian workers. The model allows analysis of the impact of alternative sourcing strategies and service level requirements on operational efficiency. It provides clear and actionable recommendations for a given context, bridging the gap between academics and humanitarian logisticians. The methodology was developed to be useful to a broad range of humanitarian organizations, and a specific application to the supply chain design of the International Federation of Red Cross and Red Crescent Societies is discussed in detail.
Do laboratory subjects correctly perceive the dynamics of a mean-reverting time series? In our experiment, subjects receive historical data and make forecasts at different horizons. The time series ...process that we use features short-run momentum and long-run partial mean reversion. Half of the subjects see a version of this process in which the momentum and partial mean reversion unfold over 10 periods ('fast'), while the other subjects see a version with dynamics that unfold over 50 periods ('slow'). Typical subjects recognize most of the mean reversion of the fast process and none of the mean reversion of the slow process.
•Advanced ML techniques are evaluated to find the accurate prediction models.•Different Error metrics are used to check the performance accuracy of the model.•Proposed PSO based SVM prediction ...technique outperforms all other model used.•Hardware experimental demonstration to exhibit ISEMS power negotiation feature.•Secure IoT environment is integrated for remote data monitoring and analysis.
The challenge in demand side energy management lays focus on the efficient utilization of renewable sources without limiting the power consumption. To deal with the above issue, it seeks for design and development of an intelligent system with day-ahead planning and accurate forecasting of energy availability. In this work, an Intelligent Smart Energy Management Systems (ISEMS) is proposed to handle energy demand in a smart grid environment with deep penetration of renewables. The proposed scheme compares several prediction models for accurate forecasting of energy with hourly and day ahead planning. PSO based SVM regression model outperforms over several other prediction models in terms of performance accuracy. Finally, based on the predicted information, the demonstration of ISEMS experimental set-up is carried out and evaluated with different configurations considering user comfort and priority features. Also, integration of the IoT environment is developed for monitoring at the user end.
In addition to main aggregates, this publication includes detailed national accounts for final consumption expenditure of households by purpose and simplified accounts for three main sectors: general ...government, corporations and households.
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