Accurate Short-term Prediction Research Articles

Double-level optimal fuzzy association rules prediction model for time series based on DTW-i[formula omitted] fuzzy C-means

Information granulation theory has been widely used in short-term time-series forecasting research and holds significant weight. However, the error accumulation due to the lack of granular accuracy, along with information redundancy or deficiency in predictions, significantly affects short-term prediction accuracy. To compensate for these shortcomings, this paper proposes a double-level optimal fuzzy association rules prediction model for short-term time-series forecasting, which can strengthen the performance of information granulation in prediction. Firstly, this paper proposes a concept of breakpoints, which can accurately segment complex linear trends in time series and thus obtain a granular time series with highly accurate linear fuzzy information granules (LFIGs). Secondly, a improved distance is proposed to more accurately reflect the similarity between LFIGs by addressing counter-intuitive problems in the original distance. Theoretical analysis shows that the improved distance can effectively reduce errors in granular calculation. Then, a granule-suited fuzzy c-means algorithm is proposed for clustering LFIGs. Finally, this paper proposes a double-level optimal fuzzy association rules prediction model, which establishes the optimal rules for each cluster and selects the optimal two rules for prediction by the contribution of the clusters. The experimental results show that the prediction method effectively avoids the problems of information redundancy and information deficiency, and increases forecast accuracy. The model’s exceptional performance is demonstrated through comparative analysis with existing models in experimental investigations.

A wind speed forecasting model using nonlinear auto-regressive model optimized by the hybrid chaos-cloud salp swarm algorithm

Wind energy forecasting is significantly affected by the strong volatility, intermittency and variability of the wind speed sequence itself. Therefore, to improve forecast accuracy and stability, based on nonlinear auto-regressive model with exogenous inputs (NARX) and the hybrid chaos-cloud salp swarm algorithm (CC-SSA), a short-term wind speed prediction method is proposed. Firstly, to reduce the complexity of the original wind speed data and generate subcomponents with different patterns and low complexity, a mixed modal decomposition method is carried out by combining variational modal decomposition (VMD) based on Pearson correlation coefficient and generalized S-transform (GST) based on adaptive sample entropy. Therefore, the complementary advantages of different mode decompositions are obtained by combining the two different subcomponents into a mixed component. Secondly, by using cloud model and chaotic map, the improved CC-SSA algorithm is proposed to improve the convergence performance of salp swarm algorithm (SSA). Finally, by using CC-SSA algorithm to optimize the weights of NARX, the CC-SSA-NARX forecasting model is established to predict wind speed. The experimental results show that for the 1-Step, 2-Step, and 3-Step datasets of the actual wind speed in the studied region, the MAE, MAPE, RMSE, and R2 of the CC-SSA-NARX model are 0.23, 6 %, 0.27, and 0.97 respectively. The proposed model present the highest prediction index among the other 7 comparative models, showing high accuracy and generalization ability in short-term wind speed prediction, it can provide a certain reference for enhancing the stability of wind power generation and promoting the sustainable development of the wind power industry to some extent.

A structural depth network embedding stacking model based on Moran’s index and seasonal trend for short-term solar irradiance prediction

Solar irradiance prediction of multi-station is influenced by complex spatial characteristics and temporal fluctuations, so how to obtain accurate short-term irradiance prediction by using photovoltaic cluster data becomes a challenge. In order to address the challenge, a structural depth network embedding stacking model based on Moran’s I and seasonal trend for short-term solar irradiance prediction is proposed. Firstly, the deep spatial characteristics of photovoltaic clusters are analyzed by using bivariate Moran index to capture strong correlations between stations. Secondly, seasonal trend decomposition is used to smooth the seasonal factors in time series, and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise is used to decompose the residual components to reduce the fluctuation of signals and the complexity of data feature mapping. Finally, based on the Stacking framework, Long Short-term Memory Network, Least Absolute Selection and Shrinkage Operator, Gradient Boosted Decision Tree, and Artificial Neural Network are superimposed. And the aggregated sub-modal components are introduced into the robust integration model to predict the solar irradiance in multiple steps. Through the case study of the National Renewable Energy Laboratory open-source dataset in the United States, the conclusions are as follows: (1) The spatio-temporal method in this paper can effectively extract the deep spatial characteristics of photovoltaic stations. (2) The fusion strategy of embedded structured deep network effectively improves the stacking effect. (3) Comparing with many classical forecasting models, it shows that this method can effectively improve the forecasting effect.

Research on Stock Price Prediction and Quantitative Stock Picking Strategy Based on Deep Learning

With the continuous development of the domestic stock market and the continuous improvement of the financial system system, and at the same time, the domestic stock market gradually rises in the financial system, based on the prediction research of the domestic stock market will become more and more important. In order to solve the problems of low precision and poor accuracy of short-term stock price prediction, this paper selects the bi-directional long- and short-term memory network of attention mechanism (WOA-BiLSTM-Attenion) model under the whale optimization algorithm for stock price prediction. The modeling of bi-directional long- and short-term memory network with attention mechanism can reduce the loss of historical information and increase the influence of important information. On this basis, Whale Optimization Algorithm (WOA) is then used for hyperparameter selection to reduce human interference. The experimental results show that compared with BP, LSTM, BiLSTM, BiLSTM-Attention, the WOA-BiLSTM-Attenion model has a better effect on stock closing price prediction, with a value of 13.9446, and the value of 0.9477, which has a higher accuracy, with a view to providing certain reference for the prediction research in other fields.

Global Navigation Satellite System Receiver Positioning in Harsh Environments via Clock Bias Prediction by Empirical Mode Decomposition and Back Propagation Neural Network Method.

This paper proposes a novel method to improve the clock bias short-term prediction accuracy of navigation receivers then solve the problem of low positioning accuracy when the satellite signal quality deteriorates. Considering that the clock bias of a navigation receiver is equivalent to a virtual satellite, the predicted value of clock bias is used to assist navigation receivers in positioning. Consequently, a combined prediction method for navigation receiver clock bias based on Empirical Mode Decomposition (EMD) and Back Propagation Neural Network (BPNN) analysis theory is demonstrated. In view of systematic errors and random errors in the clock bias data from navigation receivers, the EMD method is used to decompose the clock bias data; then, the BPNN prediction method is used to establish a high-precision clock bias prediction model; finally, based on the clock bias prediction value, the three-dimensional positioning of the navigation receiver is realized by expanding the observation equation. The experimental results show that the proposed model is suitable for clock bias time series prediction and providing three-dimensional positioning information meets the requirements of navigation application in the harsh environment of only three satellites.

Impact of Traffic Flow Rate on the Accuracy of Short-Term Prediction of Origin-Destination Matrix in Urban Transportation Networks

Information about spatial distribution (OD flows) is a key element in traffic management systems in urban transport networks that enables efficient traffic control and decisions to redirect traffic to less congested sections of the network in emergencies. With the development of modern techniques of remote sensing, more and more advanced methods are used to measure traffic and determine OD flows. However, they may produce results with different levels of errors caused by various factors. The article examines the impact of traffic volume and its variability on the error values of short-term prediction of the OD matrix in the urban network. The OD flows were determined using a deep learning network based on data obtained from video remote sensing devices. These data were recorded at earlier intervals concerning the forecasting time. The extent to which there is a correlation between the size of OD flows and the prediction error was examined. The most frequently used measure of prediction accuracy, i.e., MAPE (mean absolute percentage error), was considered. The analysis carried out made it possible to determine the ranges of traffic flow rate for which the MAPE stabilizes at the level of approximately 6%. A set of video remote sensing devices was used to collect spatiotemporal data. They were located at the entrances and exits from the study area on important roads of a medium-sized city in Poland. The conclusions obtained may be helpful in further research on improving methods to determine OD matrices and estimate their reliability. This, in turn, involves the development of more precise methods that allow for reliable traffic forecasting and improve the efficiency of traffic management in urban areas.

A short-term wind power prediction approach based on an improved dung beetle optimizer algorithm, variational modal decomposition, and deep learning

Accurate short-term wind power prediction is crucial for the efficient and safe operation of wind power systems. To enhance the accuracy of short-term wind power prediction, this paper proposes a hybrid short-term wind power prediction model, IDBO-VMD-TCN-GRU-Attention, based on the Improved Dung Beetle Optimizer algorithm (IDBO), Variational Modal Decomposition (VMD), Temporal Convolutional Network (TCN), Gated Recurrent Unit (GRU), and Attention Mechanism. The IDBO is used to optimize the parameters of the VMD. Then the optimized IDBO-VMD is used to decompose the original data into modal components with lower volatility to fully extract the data features. These modal components are inputted into the TCN-GRU-Attention to predict the short-term wind power and obtain the final prediction value. The model is tested and validated using real data from four different months and compared against 11 other models. Results demonstrate that our proposed model significantly reduces the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) by an average of at least 22.86 %, 18.82 %, and 19.99 %, respectively, across the four different months. This indicates that the proposed model provides a superior fit to the data, offers higher prediction accuracy, and holds significant practical value.

A Water Quality Prediction Method Based on Deep LSTM Network

Water quality parameters are key factors affecting marine ranching. The water quality parameters are not consistent, which are usually complex and variable. The traditional water quality prediction methods have problems such as low long-term prediction accuracy and weak generalization ability. In order to solve the above problems, this paper proposes a multivariate water quality prediction model based on WT-LSTM, which is compared with the BP neural network model for short-term prediction and long-term prediction of dissolved oxygen water quality parameters, and the experimental results show that the WT-LSTM model has good accuracy and generalization in both short-term and long-term prediction, and the short-term prediction accuracy can be up to 98.47% and the long-term prediction accuracy can be up to 98.28%.

Short-term PV power prediction based on meteorological similarity days and SSA-BiLSTM

Accurate short-term photovoltaic (PV) power forecasting can reduce the un- certainty of PV power generation, which is crucial for grid operation as well as energy dispatch. Considering the influence of seasonal and meteorological factors on short-term PV power prediction, a short-term PV power predic- tion method based on meteorological similarity day and sparrow search algo- rithm and bi-directional long and short-term memory network combination (SSA-BiLSTM) is proposed. Firstly, the correlation between meteorological factors and PV power generation is calculated by using Pearson coefficients, getting the strongly correlated meteorological factors affecting PV power generation; afterwards,the historical data of the strongly correlated meteorological factors are clustered by fuzzy C-means clustering to achieve meteorological similar day clustering; then, the best similar day is selected from the meteorological similar day according to the test day seasonal features and meteorological data, and Forming a training set with historical data, and training the original BiLSTM network. the SSA algorithm was used to find the optimal BiLSTM network parameters. Finally, Using the optimal parameters construct BiLSTM network to achieve short-term PV power prediction. The experiments were conducted with historical data from a PV power plant in Xinjiang, and also compared with existing prediction algorithms.The results show that the accuracy of PV power prediction in different weather conditions is 33.1 %, 31.9 % and 24.1 % higher than that under the same intelligent optimization algorithm and different neural networks, the accuracy of PV power prediction in different weather conditions is 27.9 %, 24.7 % and 18.0 % higher than that under the different intelligent algorithms and same neural network. Therefore, the algorithm in this paper has better accuracy in short-term PV power prediction under different seasons and different weather conditions.

Short-Term Prediction of the Intermediate Point Temperature of a Supercritical Unit Based on the EEMD–LSTM Method

The quality of the intermediate point temperature control of a supercritical unit is directly related to the quality of the coal–water ratio and main steam temperature control of the supercritical unit, which is also related to the economy and safety of the unit. In order to improve the accuracy of short-term predictions of the intermediate point temperature, a short-term prediction model of the intermediate point temperature based on the EEMD (Ensemble Empirical Mode Decomposition)-LSTM (Long Short-Term Memory) model is proposed. This model uses the data of a 600 MW thermal power station in 2022 as a sample. The EEMD method is used to decompose the historical data into IMF components and residual components, and the correlation between each component and the original data is calculated. The relevant components are sent to the LSTM neural network, and all the sub-components are superimposed to obtain the final intermediate point temperature prediction results. At the same time, the BP and LSTM models are built to compare the errors with the proposed model. The results show that the single model will produce large errors when predicting the factors of large data fluctuations. The EEMD–LSTM coupling model can fully extract the detailed features and the prediction effect is obvious. The prediction accuracy of the EEMD–LSTM prediction model built in this paper is significantly better than that of the other two models. It has certain application value in the research field of intermediate point temperature prediction and can meet the requirements of short-term predictions of the intermediate point temperature.

Adaptive temporal transformer method for short-term wind power forecasting considering shift in time series distribution

In wind power prediction, the input probability distributions in the different sub-periods are shifted owing to the strong randomness of the input features, such as wind speed and direction. This may violate the assumption for machine learning that the training and test data meet the condition of being independent and identically distributed, resulting in an insufficient generalization ability of the prediction model that is trained with the training data and applied to unknown test data. To address this problem, this study proposes an adaptive temporal transformer method for short-term wind power forecasting. First, a temporal transformer model with a gate recurrent unit and multi-head attention layers was used to extract the short- and long-term temporal information of the multiple input variables. Then, an adaptive learning strategy consisting of two stages—temporal distribution characterization and temporal distribution matching—was developed to explore the common knowledge hidden in each sub-period. The case results for an actual wind farm in northwest China showed that the proposed method could effectively weaken the adverse effects of the shifts in time series distribution on forecasting and improve the accuracy and generalization of short-term wind power prediction.

A wavelet-LSTM model for short-term wind power forecasting using wind farm SCADA data

Supervisory Control and Data Acquisition (SCADA) system collects massive operation and environment information which directly or indirectly affects the output power in wind farms. Therefore, it becomes an imperious demand to analyze the underlying information from SCADA data for improving the performance of short-term wind power prediction. In this paper, an effective deep learning framework for short-term wind power forecasting based on SCADA data analysis is proposed. A data denoising scheme is designed based on wavelet decomposition. In this method, all SCADA signals (except the wind power signal itself) are decomposed into low-frequency component A and high-frequency component D respectively by the wavelet transform. Then, the maximum information coefficient (MIC) method is applied to choose features that have strong correlation with wind power. Finally, all the selected features and wind power are defined as input vector that are used to train long short-term memory networks. The simulation results based on real data extracted from a SCADA system installed in wind farm indicate that the designed deep learning framework can significantly improve the accuracy of short-term wind power prediction.

Short-term PV power prediction based on VMD-CNN-IPSO-LSSVM hybrid model

Abstract This article discusses the significance and obstacles of short-term power prediction in photovoltaic systems and introduces a hybrid model for photovoltaic short-term power prediction technology based on variational mode decomposition (VMD), convolutional neural network (CNN), improved particle swarm optimization (IPSO) and least squares support vector machine (LSSVM). In the initial stage, the photovoltaic generation signal is decomposed into multiple Intrinsic mode functions (IMFs) using VMD to enhance the extraction of signal time–frequency characteristics. Subsequently, CNN is utilized for feature learning and extraction of each IMF, modeling the nonlinear and non-stationary features. Following this, the IPSO-LSSVM optimization algorithm is employed to establish and optimize multiple LSSVM models, predicting power fluctuations at different time scales. Finally, the predictions from each model are synthesized to obtain the final photovoltaic short-term power forecast. Through validation with actual photovoltaic generation data, this hybrid model demonstrates high accuracy and reliability in short-term power prediction, showing an average relative error and root mean square error reduction of 15.23 and 53.60%, respectively, compared to a certain comparative model. This proposed method based on VMD-CNN-IPSO-LSSVM hybrid model for photovoltaic power prediction holds promising prospects and practical value in the operation and scheduling of photovoltaic generation systems.

Short-term wind power prediction method based on CEEMDAN-GWO-Bi-LSTM

In order to improve the short-term prediction accuracy of wind power and provide the basis for power grid dispatching, a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) -grey wolf optimization (GWO) -bidirectional long short-term memory network (Bi-LSTM) prediction model is proposed to predict the short-term output power of wind farms. Firstly, the original wind power data is preprocessed, and then the original wind power data is decomposed into components that are easy to extract features by using CEEMDAN. The Bi-LSTM prediction model is established for each component, and then the grey wolf optimization algorithm is used to optimize the parameters of the Bi-LSTM model. The optimized hyperparameters are brought into the Bi-LSTM model to output the prediction results of each component. Finally, the prediction results of each component are superimposed and reconstructed to obtain the final prediction results of wind power. The simulation analysis of the power data of a wind farm in Gansu Province shows that the CEEMDAN-GWO-Bi-LSTM model has better accuracy in short-term wind power prediction.

Short-Term Solar Irradiance Prediction with a Hybrid Ensemble Model Using EUMETSAT Satellite Images

Accurate short-term solar irradiance forecasting is crucial for the efficient operation of solar energy-driven photovoltaic (PV) power plants. In this research, we introduce a novel hybrid ensemble forecasting model that amalgamates the strengths of machine learning tree-based models and deep learning neuron-based models. The hybrid ensemble model integrates the interpretability of tree-based models with the capacity of neuron-based models to capture complex temporal dependencies within solar irradiance data. Furthermore, stacking and voting ensemble strategies are employed to harness the collective strengths of these models, significantly enhancing the prediction accuracy. This integrated methodology is enhanced by incorporating pixels from satellite images provided by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). These pixels are converted into structured data arrays and employed as exogenous inputs in the algorithm. The primary objective of this study is to improve the accuracy of short-term solar irradiance predictions, spanning a forecast horizon up to 6 h ahead. The incorporation of EUMETSAT satellite image pixel data enables the model to extract valuable spatial and temporal information, thus enhancing the overall forecasting precision. This research also includes a detailed analysis of the derivation of the GHI using satellite images. The study was carried out and the models tested across three distinct locations in Austria. A detailed comparative analysis was carried out for traditional satellite (SAT) and numerical weather prediction (NWP) models with hybrid models. Our findings demonstrate a higher skill score for all of the approaches compared to a smart persistent model and consistently highlight the superiority of the hybrid ensemble model for a short-term prediction window of 1 to 6 h. This research underscores the potential for enhanced accuracy of the hybrid approach to advance short-term solar irradiance forecasting, emphasizing its effectiveness at understanding the intricate interplay of the meteorological variables affecting solar energy generation worldwide. The results of this investigation carry noteworthy implications for advancing solar energy systems, thereby supporting the sustainable integration of renewable energy sources into the electrical grid.

Complex-valued artificial hummingbird algorithm for global optimization and short-term wind speed prediction

Environmental pollution and energy depletion have spurred the exploration of renewable energy sources. Wind energy, with its sustainability and eco-friendliness, stands out as a competitive option. However, its effectiveness relies on accurately predicting the fluctuating wind speeds, necessitating ongoing research in this area. In order to improve the accuracy of wind speed prediction, this article presents the first Complex-valued version of the Artificial Hummingbird Algorithm (CAHA), which utilizes the idea of doubles to increase the initial population diversity and enhance the algorithm's performance. Additionally, the article employs the CAHA to optimize the parameters of Artificial Neural Networks (ANNs). This marks the first application of the hybrid model to address the short-term wind speed prediction problem. The experiments begin with a performance evaluation of CAHA using the CEC2022 test set. Then, the CAHA is employed to optimize the parameters of six distinct ANNs to select the ANN model that can produce the best prediction result when synergized with CAHA. The results determine that the hybrid model based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) exhibits the best prediction performance. Furthermore, the performance of CAHA is compared with other classical optimization algorithms in short-term wind speed prediction problems based on ANFIS. Statistical results show that the ANFIS-CAHA model significantly improves the accuracy of short-term wind speed prediction, making it a potent tool for the integration of wind energy into smart grid engineering.

Enhancing rock and soil hazard monitoring in open-pit mining operations through ultra-short-term wind speed prediction

Wind speed exacerbates challenges associated with rock stability, introducing factors such as heightened erosion and the possibility of particle loosening. This increased sensitivity to erosion can result in material displacement, thereby compromising the overall stability of rock layers within the open-pit mining site. Therefore, accurate wind speed predictions are crucial for understanding the impact on rock stability, ensuring the safety and efficiency of open-pit mining operations. While most existing studies on wind speed prediction primarily focus on making overall predictions from the entire wind speed sequence, with limited consideration for the stationarity characteristics of the sequence, This paper introduces a novel approach for effective monitoring and early warning of geotechnical hazards. Our proposed method involves dividing wind speed data into stationary and non-stationary segments using the sliding window average method within the threshold method, validated by the Augmented Dickey-Fuller test. Subsequently, we use temporal convolutional networks (TCN) with dilated causal convolution and long short-term memory to predict the stationary segment of wind speed, effectively improving prediction accuracy for this segment. For the non-stationary segment, we implement complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to reduce sequence complexity, followed by TCN with an attention mechanism (ATTENTION) to forecast wind speed one step ahead. Finally, we overlay the predictions of these two segments to obtain the final prediction. Our proposed model, tested with data from an open-pit mining area in western China, achieved promising results with an average absolute error of 0.14 knots, mean squared error of 0.05 knots2, and root mean squared error of 0.20 knots. These findings signify a significant advancement in the accuracy of short-term wind speed prediction. This advancement not only enables the rapid assessment and proactive response to imminent risks but also contributes to geotechnical hazard monitoring in open-pit mining operations.

Neural network-based climate index: Advancing rainfall prediction in EI Niño contexts

Accurate short-term climate predictions, specifically the prediction of dominant rain belts during flood seasons, represent a significant challenge, particularly in the absence of notable external forcing signal anomalies in the previous winter. To address this challenge, we propose a novel climate index based on an Auto-Encoder (AE) Neural Network that exhibits superior performance in indicating several significant precipitation events in the Yangtze and Huaihe River Basins under varying El Niño conditions. Notably, the novel climate index rectifies the discrepancy between the 2019/2020 Niño index and the 2020 summer precipitation. In order to understand the information source of AE, the causality between AE and other indices was further analyzed by Granger Causality. The results show that AE index mainly contains the information of subtropical high index and ocean index, which is consistent with previous studies.

Short-term voltage instability prediction using pre-identified voltage templates and machine learning classifiers

System operators use different procedures to detect disturbances which can lead to short-term voltage instability. These schemes are designed using the domain knowledge and generally specific for the corresponding system. The design procedure of such schemes is heuristic and cannot be directly applied to any other power system. This paper proposes a data-driven approach to predict short-term voltage stability using set of post-disturbance voltage templates and a machine learning-based classifier. In this scheme, a set of voltage templates corresponding to stable/unstable events is identified for selected buses using a suitably generated training data set. The proximity of real-time voltage trajectory to each of the template is computed and input to a trained machine learning classifier to predict the short-term voltage stability status. The proposed approach is validated using IEEE Nordic test system. The investigations show that this method provides accurate short-term voltage stability predictions. Furthermore, impact of adverse phenomena such as measurement noise and missing data on the proposed method is analyzed to confirm the robustness of this scheme in particle environments. © 2017 Elsevier Inc. All rights reserved.

A systematic analysis of design choices in short-term taxi demand prediction models

Accurate prediction of short-term taxi demand allows taxi services to strategically position idle vehicles in areas with insufficient supply, thus reducing idle times for taxis and waiting times for passengers. Towards this end, state-of-the-art approaches use Machine Learning (ML) models based on historic demand data, such as Long Short-Term Memory (LSTM), Fully Connected Feed Forward Neural Network (FCNN), Convolutional Neural Network (CNN), Graph Convolutional Network (GCN), or a combination of these. The majority of approaches use a grid representation of the environment to discretize the pickup locations of trips and time bins to order the temporal dimension of the large multi-target regression problem. While these models show good accuracy, many \ extit{design choices}, such as the size of the grid cells or the number of previous time steps inserted into a model, are made without justification or without proper tuning. This paper systematically investigates these design decisions. Our hypothesis is that considering them jointly rather than in isolation can improve accuracy. We investigate the influence of (1) the selected grid cell size, (2) the number of input maps, (3) the usage of time shift to enlarge the training data, and (4) the potential of transfer learning on the accuracy of short-term taxi demand prediction. We selected different network types and evaluated them on two large real-world datasets. The evaluation shows that our model outperforms state-of-the-art short-term taxi demand prediction models.