Ultra-short Term Research Articles

A novel short-term electrical load forecasting framework with intelligent feature engineering

The emergence of many new types of electrical loads, such as security systems and charging piles, has resulted in a diversified trend of building load-end components. This trend is coupled with chaotic influencing factors with time-scale differences such as multiple periods and special events, thereby causing difficulties in extracting multiple time-scale features (including regular electricity behavior features in a long term and random electricity behavior features in an ultrashort term) and reducing the quality of short-term forecasting. To this end, a two-level intelligent feature engineering (IFE) and serial multi-timescale forecasting framework is designed in this work. First, the first-level IFE is proposed to deal with weather and calendar effects and to extract regular electricity behavior features in a long term, thus helping the 24-h ahead forecasting model to obtain a stable forecast value of the load trend. Second, based on the day-ahead trend component forecasting (DATCF) value obtained in the previous step, the second-level IFE is proposed to extract the random electricity behavior features in an ultrashort term. The aim is to improve the forecasting capability of the 15-min ahead forecasting model for load fluctuation detail. This work eliminates redundant and irrelevant features through a two-level IFE and balances the stability for load trend forecasting and the accuracy for load fluctuation detail forecasting through a serial multi-timescale forecasting framework. Finally, the simulation results indicate that the root mean square error (RMSE) of the proposed method is less than that of the control group by 8.98–31.56 kW, and the RMSE of the proposed method decreased by 1.27–66.13 kW during peak hours with strong volatility.

Ultra-short term trading using a neural-network based ensemble of financial technical indicators in a closed world market

The proposed paper presents the analysis, design, implementation and evaluation of an ultra-short-term frequency trading system for the foreign exchange market (FOREX), which features all stages of the trading process (Pre-Trade Analysis, Trend Forecasting, Trade Execution). The system uses artificial intelligence techniques in an environment that is constantly changing according to the decisions of the participating trading simulators. Our goal is to simulate the judgment and decision-making of the human expert (technical analyst or broker) in a closed world trading system that constantly adjusts exchange rates. We examine the system in terms of its contribution to how exchange rates are affected in an environment which solely consists of traders, without any exogenous factors, and show that our system can outperform all conventional technical indicators, thereby indicating that it could also play the role of facilitating the stabilization of a market by squeezing out non-intelligent traders. We designed and implemented a self-adjusting trading environment whose exchange rate price is only affected by trading within the environment (closed world assumption). We based our work on a modified series of technical indicator simulators, which are fed to an artificial neural network architecture, to eventually generate the trend forecasting signal and a series of customizable ultra-short term automated trading machines, which perform real-time virtual transactions based on the generated forecasting signals. A comparative analysis of the results is carried out to confirm that the proposed architecture outperforms traders based only on the conventional technical indicators while we also document the behavior of the system towards facilitating the attainment of an equilibrium.

Diagnostic Efficacy of Ultra-Short Term HRV Analysis in Obstructive Sleep Apnea.

Heart rate variability (HRV) is the standard method for assessing autonomic nervous system (ANS) activity and is considered a surrogate marker for sympathetic overactivity in obstructive sleep apnea (OSA). Although HRV features are usually obtained from the short-term segment method, it is impossible to evaluate rapid dynamic changes in ANS activity. Herein, we propose the ultra-short-term analysis to detect the balance of ANS activity in patients with OSA. In 1021 OSA patients, 10 min HRV target datasets were extracted from polysomnographic data and analyzed by shifting the 2 min (ultra-short-term) and 5 min (short-term) segments. We detected frequency-domain parameters, including total power (Ln TP), very low frequency (Ln VLF), low frequency (Ln LF), and high frequency (Ln HF). We found that overall HRV feature alterations indicated sympathetic overactivity dependent on OSA severity, and that this was more pronounced in the ultra-short-term methodology. The apnea-hypopnea index, oxygen desaturation index, and Epworth sleepiness scale correlated with increased sympathetic activity and decreased parasympathetic activity, regardless of the methodology. The Bland-Altman plot analyses also showed a higher agreement of HRV features between the two methodologies. This study suggests that ultra-short-term HRV analysis may be a useful method for detecting alterations in ANS function in OSA patients.

Serial-parallel dynamic echo state network: A hybrid dynamic model based on a chaotic coyote optimization algorithm for wind speed prediction

Multiple reservoirs have been widely used in nonlinear time series forecasting. The hybrid approach is recognized as the mainstream forecasting method in complex wind speed prediction systems. However, application research and error correction of the multiple reservoirs hybrid model in the wind energy domain are still insufficient. Therefore, a serial-parallel dynamic echo state network (SP-ESN) with dual dynamic characteristics is proposed firstly. Its serial structure can capture the short-term memory information of the sequence and dynamically select the length of the training set, while the parallel structure has the ability to automatically correct the prediction error of the serial structure. Then, a dynamic prediction model based on phase space reconstruction that includes the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method, the SP-ESN and the chaotic coyote optimization algorithm (CCOA) is designed in this paper. After data preprocessing of the original sequence by ICEEMDAN, the generated decomposed subsequence is input into the SP-ESN model for prediction and the final forecasting value is obtained accordingly. The CCOA is used to optimize the developed SP-ESN model parameters. Five wind speed datasets from Ningxia Hui Autonomous Region and Qinghai Province of China are selected for experiments. Six point prediction evaluation metric, forecasting efficiency and Diebold-Mariano test are used to evaluate the performance of the proposed model in ultra-short term wind speed forecasting. The case study results show that the proposed hybrid model achieves satisfactory performance compared with 5 datasets and 14 models, with mean absolute percentage error (MAPE) of 0.8405%, 1.2207%, 0.5163% 0.9791% and 0.3826%, respectively.

Univariate and Multivariate LSTM Models for One Step and Multistep PV Power Forecasting

The energy demand is increasing due to population growth and economic development. To satisfy this energy demand, the use of renewable energy is essential to face global warming and the depletion of fossil fuels. Photovoltaic energy is one of the renewable energy sources, widely used by several countries over the world. The integration of PV energy into the grid brings significant benefits to the economy and environment, however, high penetration of this energy also brings some challenges to the stability of the electrical grid, due to the intermittency of solar energy. To overcome this issue, the use of a forecasting system is one of the solutions to guarantee an effective integration of PV plants in the electrical grid. In this paper, a PV power ultra short term forecasting has been done by using univariate and multivariate LSTM models. Different combinations of input variables of the models and different timesteps forecasting were tested and compared. The main aim of this work is to study the influence of the different combinations of variables on the accuracy of the LSTM models for one-step forecasting and multistep forecasting and comparing the univariate and multivariate LSTM models with MLP and CNN models . The results show that for one step forecasting, the use of a univariate model based on historical data of PV output power is sufficient to get accurate forecasting with 28.98W in MAE compared to multivariate models that can reach 35.39W. Meanwhile, for multistep forecasting, it is mandatory to use a multivariate model that has historical data of meteorological variables and PV output power in the input of LSTM model. Moreover, The LSTM model shows great accuracy compared to MLP and CNN especially in multistep PV power forecasting.

Mental Stress Assessment Using Ultra Short Term HRV Analysis Based on Non-Linear Method.

Mental stress is on the rise as one of the major health problems in modern society. It is important to detect and manage mental stress to prevent various diseases caused by stress and to maintain a healthy life. The purpose of this paper is to present new heart rate variability (HRV) features based on empirical mode decomposition and to detect acute mental stress through short-term HRV (5 min) and ultra-short-term HRV (under 5 min) analysis. HRV signals were acquired from 74 young police officers using acute stressors, including the Trier Social Stress Test and horror movie viewing, and a total of 26 features, including the proposed IMF energy features and general HRV features, were extracted. A support vector machine (SVM) classification model is used to classify the stress and non-stress states through leave-one-subject-out cross-validation. The classification accuracies of short-term HRV and ultra-short-term HRV analysis are 86.5% and 90.5%, respectively. In the results of ultra-short-term HRV analysis using various time lengths, we suggest the optimal duration to detect mental stress, which can be applied to wearable devices or healthcare systems.

The (ultra) short term prediction of length-of-day using kriging

The contribution presents results of ultra-short-term (15 days into the future) and short-term (30 days into the future) LOD prediction using kriging. IERS EOP 14 C04 (IAU2000A) and IERS EOP 05 C04 (IAU2000A) series are used as a reference. IERS EOP 05 C04 (IAU2000A), which is no longer in force, was used for comparison with previously published results. Two strategies of semivariogram selection for prediction purposes are employed. The first one uses a single semivariogram model in the entire prediction procedure whilst the second one selects a semivariogram model, from a group of models, with the least residual sum of squares. This work’s implementation of kriging offers mean absolute prediction errors in ultra-short-term forecast for days 1–15 with respect to IERS EOP 14 C04 on the level of 0.035–0.149 ms whilst for IERS EOP 05 C04 the values are 0.030–0.139 ms. For short term prediction (days 1–30) the values are 0.038–0.180 ms and 0.034–0.175 ms; respectively. The results show that LOD kriging-based prediction may compete with existing methods.

Ultra-Short-Term Wind Power Interval Prediction Based on Fluctuating Process Partitioning and Quantile Regression Forest

As errors in point forecasts of wind power are unavoidable, interval forecasts can adequately describe the uncertainty in wind power and thus provide further guidance to dispatchers in their decision making. Current interval prediction methods are still incomplete in terms of tapping into the physical variability of wind power, especially for the specific time scale of the ultra-short term. This paper therefore proposes a new framework for interval forecasting of ultra-short-term wind power that incorporates the power fluctuation process. Firstly, a fluctuating process of wind power series is defined and a Kalman-SOM method for clustering the fluctuating processes of wind power is constructed. Secondly, a quantile regression forest interval prediction model is constructed for multiple fluctuation processes for ultra-short-term time scales. Finally, the effectiveness of the overall framework is validated at a wind farm in Jilin Province, China. Compared with the traditional interval prediction method. The interval bandwidth is reduced by 0.86% on average, and the interval coverage is increased by 1.4% on average. The results demonstrate the effectiveness and feasibility of the method in this paper.

A deep learning-based method for ultra-short-term PV power prediction

Abstract With the rapid development of photovoltaic industry, the application accuracy of photovoltaic power prediction technology in the field of power system control, dispatching and operation is more precise. Due to the volatility and randomness of photovoltaic power in the ultra short term, high-precision prediction has more theoretical and practical significance. This paper presents an ultra short-term photovoltaic power prediction model based on dual attention mechanism and GRU, which realizes the high-precision prediction of photovoltaic power in ultra short term. Firstly, attention mechanism is introduced to realize the dual extraction of temporal and spatial features; Then, the power is predicted by combining the extracted features with the characteristics of GRU long-term memory ability and fast calculation. The time series attention mechanism is introduced to independently extract the information of historical key moments to improve the stability of long-time series prediction effect; The feature attention mechanism effectively calculates the correlation of each meteorological feature quantity, and alters the feature weight. Through the comparison experiment with the baseline model, it is verified that the proposed model has higher prediction accuracy, better generalization ability and robustness.

Research on ultra-short term forecasting technology of wind power output based on various meteorological factors

Wind power generation is the most important form of wind energy utilization, and it is also one of the fields with mature technology and the most commercial development prospect in the world. The wind potential of an area is not only reflected in wind speed, but also influenced by other environmental factors such as wind direction, temperature, air pressure and air density. Therefore, this paper comprehensively considers the historical output data, wind speed and other meteorological factors, selects the prediction factor with large correlation coefficient for information fusion, and constructs multi-dimensional data features based on a variety of meteorological data. The purpose is to establish a complete wind power conversion model, explore the rules of the dynamic change of wind power and improve the accuracy of power output prediction. Finally, we combined with the least square support vector machine (LSSVM) to realize the wind farm output prediction.

Research on Ultra-short term Forecasting Technology of Wind Power Output Based on Wake Model

The concept of sustainable development promotes the development of new energy power generation industry. At the same time, in the field of wind power, wind curtailment and power limit gradually appear, and the output prediction of power plants has become one of research hotspots. Because of the wake effect between wind farms, the wind speed of the wind tower can not represent the real numerical value. Therefore, in this paper, we established various wake models of wind farm group under different wind direction to modify the wind speed of wind tower and get more accurate results. We also used the Long Short-Term Memory network (LSTM) to forecast the wind farm output, and finally obtained excellent forecast results. Compared with traditional prediction algorithm, it had improved the prediction accuracy.

A hybrid SDS and WPT-IBBO-DNM based model for ultra-short term photovoltaic prediction

A hybrid SDS and WPT-IBBO-DNM based model for ultra-short term photovoltaic prediction

A Novel Framework for Ultra-Short Term Wind Power Prediction Based on Rf-Woa-Vmd and Bigru Optimized by Attention Mechanism

A Novel Framework for Ultra-Short Term Wind Power Prediction Based on Rf-Woa-Vmd and Bigru Optimized by Attention Mechanism

Ultra-short term wind power prediction applying a novel model named SATCN-LSTM

Accurate and reliable wind power forecasting has become very important to power system scheduling and safely stable operating. In this paper, a novel self-attention temporal convolutional network (SATCN) is combined with long-short term memory (LSTM) to forecast wind power for guaranteeing the continuous electricity supply. In the proposed SATCN-LSTM model, the structure of SATCN with a self-attention mechanism is conducted to pay more attention to features that contribute more to the output. The strength of SATCN is performed through extracting temporal feature of meteorological data and correlation characteristics between variables. LSTM is used after SATCN to further build the connection between features and outputs for predicting future ultra-short time wind power. The effectiveness and advancement of the proposed method is tested by using meteorological data and wind power data from two different wind farms in the U.S. The experimental results reveal that the SATCN-LSTM model is more accurate comparing to other methods. Taking California's fourth quarter wind power forecast results as an example, the proposed method has carried out a reduction of 17.56%, 10.99%,11.34% and 3.68% on the root mean square error compared with LSTM, TCN, CNN-LSTM, TCN-LSTM.

Ultra Short Term Power Prediction of Offshore Wind Power Based on Support Vector Machine Optimized by Improved Dragonfly Algorithm

In order to improve the prediction effect of ultra short term power of offshorewind power, the prediction model based on support vector machine optimizeddragonfly algorithm is constructed. Based on summary of the predictionmethods of wind power, the support vector machine optimized by dragonflyalgorithm is established. Finally, prediction simulation analysis of offshorewind power is carried out, results show that the proposed prediction model inthis research can effectively improve the computing prediction precision.

A new hybridization filtering-based linear-nonlinear models for time series forecasting

In recent years, the idea of using a mathematical model to describe the behavior of physical phenomena has been very much considered. Specifically, a definitive model, based on physical laws, enables researchers to calculate the number of time dependencies precisely at any moment in time. However, in the real world, we often face time-dependent phenomena with many unknown or unavailable factors (Lindley, 2010; Roulston et al., 2003). In this case, when it is not possible to achieve a definite - model, the prediction methods are wide used, especially when the past observations of a variable and primary relationships between specific observations are available. Forecasting methods that are used in different fields of science can be categorized based on various aspects. For example, the prediction methods used in the field of wind energy can be divided into four categories of 1) ultra short term (several seconds to four hours), 2) short term (4 to 24 hours), 3) medium-term (1 to 7 days), and 4) long term (more than 7 days) (Zack, 2003; Soman et al., 2010). Also, the structure of forecasting methods can be divided into two types of 1) single methods and 2) hybrid methods. Each of these categories can also be subdivided into smaller subgroups.

Ultra-short-term Wind Power Output Prediction Based On LS-WMC Combined Model

In this paper, an ultra short term wind power output prediction method based on least square and weighted Markov chain model is proposed. Firstly, the wind farm output is fitted based on the least square method, and then the prediction accuracy is improved by using the weighted Markov chain model. At the same time, the partial exponential weighting method is used to improve the prediction accuracy of ultra short-term wind power output on the basis of reducing the generation frequency of transition probability matrix, so as to optimize and improve the prediction results. The results show that the method can improve the prediction accuracy.

Ultra-Short Term Power Load Prediction Based on Gated Cycle Neural Network and XGBoost Models

Load forecasting is an important guarantee for power system design, planning and operation. In order to further improve the accuracy of power load forecasting, a combined forecasting model based on gating recurrent neural network (GRU) and limit gradient lifting (XGBoost) is proposed. Firstly, according to the load data and the input structure of GRU and XGBoost models, the data are preprocessed, and the preprocessed data are input into the corresponding model respectively. Then, the model is weighted by entropy weight method to obtain the predicted value of the final combination model. Finally, the first mock exam is used to compare the combination forecasting model with the common forecasting models. The results show that the proposed method can effectively combine the advantages of the two models, and take into account the continuous time series and discontinuous feature variables, which is more accurate than the single model and the common forecasting models.

Ultra Short Term Wind Power Prediction Model Based on WRF Wind Speed Prediction and CatBoost

Due to the urgent requirements of clean energy development, the installed capacity of wind turbine is gradually increasing. In order to achieve effective wind turbine grid control, accurate wind power prediction is very important. In this paper, based on the US Atmospheric Research Center (NCAR) Weather Research and Forecast (WRF) model, the predicted wind field is obtained. Then, a super short-term fast wind power prediction model is proposed, which is composed of Spearman correlation coefficient method for feature extraction and catboost algorithm. With historical power, historical wind speed and predicted wind speed as inputs, the predicted power in the next 4 hours (15 minutes interval) is output. In order to evaluate the prediction ability of the proposed model, four algorithms, LSTM, CatBoost, XGBoost and LightBGM, are adopted. Taking root mean square error (RMSE), mean absolute error (MAE) and training time as evaluation indexes, the results show that CatBoost has the best comprehensive performance.

High frequency sequence stratigraphic framework and sedimentary characteristics of P oil layer in the southern part of A area

Based on the high resolution sequence stratigraphy theory, 42 wells and 2745 well logging curves are used to study the evolution of all levels of base level cycles and the development pattern of high-resolution sequence in the P reservoir in the southern part of Songfangtun area. The results show that the 4 sequence interface of the grape flower beds in the Songfangtun area can be divided into 2 medium-term, 5 short term and 12 ultra short term sequences. On this basis, the sedimentary microfacies type and distribution evolution law have been carried out. It is considered that the shallow water delta depositional system is developed in P reservoir in the Songfangtun area. The main development of delta front subfacies is to identify 8 kinds of microfacies such as subwater distributary channel, inter channel, inter channel, slime, overflow sand, mouth fan, estuarine dam, far sand bar and mat sand. The underwater distributary channel sandbodies are widely developed at the front of the delta. Due to frequent bifurcations and diversions, the underwater distributary channel sandbodies cut and overlap each other, forming branches and striped sand bodies. The sandbody reservoir has good physical property and is the main reservoir sand body with oil and gas enrichment. The detailed study of sedimentary microfacies provides a reference for further exploration and development of lithologic reservoirs in this area.