Short-term Prediction Method Research Articles

Short-term photovoltaic power generation power prediction method based on SOA-BP neural network

The size of photovoltaic power generation has a certain uncertainty, which affects the quality of photovoltaic power generation, and affects the safety and stable operation of the power grid. By predicting the photovoltaic power generation, this problem can be better solved. A photovoltaic power generation power prediction method combining SOA (Seagull optimization algorithm) and BP neural network is proposed. The convergence speed of BP neural network prediction model is relatively slow, and it is easy to fall into local optimum. Optimizing its weights and thresholds through SOA can improve these situations. Through experiments, Through experimental comparison, it is found that the BP neural network method optimized by seagull algorithm has higher accuracy in predicting photovoltaic power generation.

Research on short-term power prediction of wind power generation based on WT-CABC-KELM

Under the framework of the gradual scarcity of fossil energy, how to make better use of wind power to provide a safe and effective guarantee for the power grid has become a hot topic today. Aiming at the problems of existing short-term wind power prediction models that are easy to fall into local optimum, slow training speed, and poor accuracy, this paper proposes an improved artificial bee colony algorithm based on wavelet transform (WT) combined with a nuclear extreme learning machine. Power prediction method (WT-CABC-KELM). First, aiming at the volatility of historical wind power data, it is proposed to use wavelet transform to extract the hidden main features at each frequency to improve the prediction accuracy. Then, because the two parameters C and λ of the KELM model have a greater impact on the prediction accuracy, The improved artificial bee colony algorithm (CABC) is used to optimize the parameters of its model to achieve the purpose of improving the prediction accuracy, and the WT-CABC-KELM short-term wind power prediction model is established. In order to prove the effectiveness of the method proposed in this paper, 4 Each season is forecasted separately, and compared with traditional SVM and BP neural network. Finally, the results are compared and analyzed by 4 kinds of error indicators. The experimental results show that the short-term wind power prediction method (WT-CABC-KELM) mentioned in this article can effectively improve the short-term power prediction accuracy of wind power.

Short-Term Prediction Method of Solar Photovoltaic Power Generation Based on Machine Learning in Smart Grid

In order to improve the accuracy of ultra short-term power prediction of the photovoltaic power generation system, a short-term photovoltaic power prediction method based on an adaptive k-means and Gru machine learning model is proposed. This method first introduces the construction process of the model and then builds a short-term photovoltaic power generation prediction model based on an adaptive k-means and Gru machine learning models. Then, the network structure and key parameters are determined through experiments, and the initial training set of the prediction model is selected according to the short-term photovoltaic power generation characteristics. And the adaptive k-means is used to cluster the initial training set and the photovoltaic power on the forecast day. The Gru model is trained on the initial training set data of each category, and the generated power is predicted in combination with the trained Gru model. Finally, considering three typical weather types, the proposed method is used for simulation analysis and compared with the other three traditional photovoltaic power generation single prediction models. The comparison results show that the proposed short-term photovoltaic power generation prediction method based on an adaptive k-means and Gru network has better effect, better robustness, and less error.

A Novel Hybrid Method for Short-Term Wind Speed Prediction Based on Wind Probability Distribution Function and Machine Learning Models

The need to deliver accurate predictions of renewable energy generation has long been recognized by stakeholders in the field and has propelled recent improvements in more precise wind speed prediction (WSP) methods. Models such as Weibull-probability-density-based WSP (WEB), Rayleigh-probability-density-based WSP (RYM), autoregressive integrated moving average (ARIMA), Kalman filter and support vector machines (SVR), artificial neural network (ANN), and hybrid models have been used for accurate prediction of wind speed with various forecast horizons. This study intends to incorporate all these methods to achieve a higher WSP accuracy as, thus far, hybrid wind speed predictions are mainly made by using multivariate time series data. To do so, an error correction algorithm for the probability-density-based wind speed prediction model is introduced. Moreover, a comparative analysis of the performance of each method for accurately predicting wind speed for each time step of short-term forecast horizons is performed. All the models studied are used to form the prediction model by optimizing the weight function for each time step of a forecast horizon for each model that contributed to forming the proposed hybrid prediction model. The National Oceanic and Atmospheric Administration (NOAA) and System Advisory Module (SAM) databases were used to demonstrate the accuracy of the proposed models and conduct a comparative analysis. The results of the study show the significant improvement on the performance of wind speed prediction models through the development of a proposed hybrid prediction model.

A Diverse Ensemble Deep Learning Method for Short-Term Traffic Flow Prediction Based on Spatiotemporal Correlations

In this paper, considering spatiotemporal correlations, we propose a novel short-term traffic flow prediction method that is based on diverse ensemble deep learning. First, a new measurement function of spatial correlation is established, quantifying the spatial correlation of traffic flow parameters. Second, the convolutional neural network model embedded long short-term memory (LSTM-CNN) is used, enhancing the learning ability of the spatiotemporal correlation of traffic flow parameters. Third, according to the network structure, ensemble rules of diverse LSTM-CNNs are constructed, combining a series of different and moderately accurate LSTM-CNN models and improving the robustness of the algorithm. Finally, a dynamic optimization method for the weight parameters of the ensemble elements is proposed to accommodate the changes in the actual road networks. The experiments show that the measuring method of spatial correlations, the ensemble combination rules of diverse LSTM-CNN and the dynamic optimization method of ensemble element weight parameters, which are proposed in this paper, have positive effects on improving the performance of the prediction algorithm. In addition, the short-term traffic flow prediction method proposed in this paper can adapt to changes in the traffic flow. It can obtain a better prediction effect even with a small sample, and the prediction performance is better than that of the other four classical short-term traffic flow prediction methods.

A novel hybrid model combining a fuzzy inference system and a deep learning method for short-term traffic flow prediction

Deep learning techniques have been widely used in traffic flow prediction. They can perform much better than shallow models. However, most existing deep learning models only focus on deterministic data, ignoring the fact that traffic flow contains a large amount of uncertain data. Therefore, this paper proposes a novel hybrid model called FGRU combining a fuzzy inference system (FIS) and a gated recurrent unit (GRU) neural network to predict short-term traffic flows. The GRU model is applied to capture the temporal dependencies within traffic flow data, and the FIS makes up for the shortcomings of deep learning by lessening the influence of uncertain data. In addition, a temporal feature enhancement mechanism is proposed to calculate the appropriate time intervals as model inputs. The most appropriate model structure and parameters are explored by performing comparative experiments. Finally, the simulation results show that the mean absolute error of FGRU is 7.75% and 3.05% lower than ARIMA and the state-of-the-art traffic flow prediction model based on deep learning.

A Short-Term Forecasting Method of Inter-Frequency Clock Bias for GPS Block IIF Satellites

The time-varying biases within carrier phase observations are integrated into satellite clock offset parameters for precise clock estimation. Consequently, when the precise satellite clock bias is applied to the third frequency observation for precise point positioning (PPP), a new type of inter-frequency clock bias (IFCB) with satellite dependence should be noticed. If the IFCB is estimated together with the receiver coordinates, tropospheric wet delay, ambiguity and other parameters, it will increase the computational burden and lead to more time consumption. In order to solve this problem, the IFCB of GPS Block IIF satellites were estimated using 162 global uniformly distributed Multi-GNSS Experiment (MGEX) stations. By analyzing the time-varying characteristic of each satellite IFCB and combining the lag characteristics of the final ephemeris products, a modeling method of short-term IFCB prediction based on the epoch-by-epoch sliding Pearson autocorrelation function is proposed. The feasibility of this method was verified through the Student’s t-distribution, comparison with the measured IFCB, the posteriori residual of the third frequency carrier phase and the kinematic/static PPP solutions. The results showed that since the IFCB period was not a complete 24 h, the difference in the IFCBs time series on different days was increasingly significant with the passage of lag time, and the correlation constantly decreased. The peak-to-peak amplitudes of the IFCB difference reached 1.13, 3.44, 6.86 and 11.25 cm when the lag time was 1, 9, 19 and 29 days, respectively. In addition, based on the lag characteristic of final precise ephemerides released by the International GNSS Service (IGS) analysis centers, the prediction accuracy of the IFCB was evaluated with a time lag of 7 days. The root mean square of the posteriori residuals at the third-frequency observation decreased by approximately 51.3% compared to that without considering for IFCB correction. The triple-frequency uncombined PPP in the horizontal and vertical directions improved by approximately 33.2% and 17.2% for the static PPP solutions and 50.2% and 39.7% for the kinematic PPP solutions, respectively. In general, the accuracy and convergence time of the triple-frequency uncombined PPP were equivalently improved when the predicted IFCB and the measured IFCB were used.

Hybrid Model for Method for Short-Term Traffic Flow Prediction Based on Secondary Decomposition Technique and ELM

Strong nonstationary and nonlinearity are the main characteristics in the short-term traffic flow data, which frustrates traditional methods (e.g., autoregressive integrated moving average and deep belief network) to provide a satisfactory prediction. To address the above problem, a novel forecasting method, which is composed of a secondary decomposition technique and extreme learning machine, is proposed in this study. This developed technique is a hybrid of time-varying filtering-empirical mode decomposition (TVF-EMD) and local mean decomposition (LMD), which not only can effectively handle the above complex data features by decomposing them into several regular subsets but also produce the smoother subseries that is beneficial to prediction. To verify the effectiveness of the proposed method, a case study based on two groups of actual traffic flow data with different characteristics is performed. Meanwhile, several single models and hybrid models based on the other decomposition methods (e.g., EMD and variational mode decomposition) are considered benchmark models. The experimental results reveal that the proposed model presents the best performance. For example, compared with the TVF-EMD-based method, the improvement by the proposed approach reaches 33.3% in terms of the evaluation criterion of mean absolute percentage error.

Short-Term Estimation and Prediction of Pedestrian Density in Urban Hot Spots Based on Mobile Phone Data

Short-term estimation and prediction of pedestrian density in urban hot spots (e.g., railway station, shopping mall, etc.) is an important topic for traffic management and control in densely populated areas. In this paper, we propose a short-term pedestrian density estimation and prediction method based on mobile phone data. Firstly, pedestrian density in hot spots is estimated using mobile phone data. To decrease the positioning errors of mobile phone data, a modified particle filter method, which considers the movements of pedestrians, is applied for pre-processing the data. An efficient spatial access method (i.e., Hilbert R-tree) is adopted to construct pedestrians’ position indexes for realizing the short-term estimation. Secondly, based on the estimation results, the spatiotemporal extended Kalman filter (SEKF) is proposed for the short-term prediction of pedestrian density. A massive mobile phone dataset collected in Nanjing, China is used in the case study. The estimated pedestrian density from Monday to Thursday is used for pedestrian density prediction on Friday. The results show that the proposed method can estimate and predict pedestrian density in hot spots, especially in small-scale sites of hot spots efficiently in a short time. Comparing with classical prediction methods, the proposed SEKF method predicts short-term pedestrian density in urban hot spots more accurately.

Short-term Wind Power Prediction Method Based on UAV Patrol and Deep Confidence Network

At present, wind power has become the most promising energy supply. However, the intermittent and fluctuating wind power also poses a huge challenge to accurately adjust the electrical load. In order to find a method capable of forecasting wind power generation in a short period of time, we propose a short-term wind power generation forecasting method based on an optimized deep belief network approach. Based on GEFCom2012 competition dataset, by continuously tuning the parameters of the deep belief network for 15 sets of experiments, we obtained three optimal laboratory combinations: Experiment 4, Experiment 10, and Experiment 12. The results show that the R-squared values of Experiment 4, Experiment 10 and Experiment 12 are the highest, which are 0.955, 0.93 and 0.98, respectively. The average R-squared value of these three tuned experiments is 0.2342 higher than the average of the other 12 experiments. At the same time, it is concluded that when the learning frequency is low, the linear function can learn the most obvious features more directly; When the learning frequency is high, the nonlinear function can learn the internal latent features more directly.

Short-term wind power prediction with harmony search algorithm: Belen region

Wind power is the fastest-growing technology among alternative energy production sources. Reliable forecasting of short-term wind power plays a critical role in the acquisition of most of the generated energy. In this study, short-term wind power forecast is performed using radial-based artificial neural networks, forecast error and cost to be minimized with the harmony search algorithm. Experimented results show that, we can predict wind power with fewer features and less error by using harmony search algorithm. A %7 percent improvement in RMSE rate has been achieved with the proposed method for short-term wind power prediction.

Direction Prediction of Traffic Flow in Vissim Simulation Based on K Nearest Neighbor Algorithm

In order to make short-term prediction of the direction of traffic flow in urban roads, a short-term prediction method of urban road travel time based on K nearest neighbor algorithm and vissim simulation is constructed. First, the intersection of Shiji Road and Yingbin Road was selected as the survey site, and the number of vehicles in each direction of each entrance lane of the intersection was investigated using manual counting, and the signal timing of each phase of the intersection was investigated. Input the survey data into the vissim simulation software to get the travel time of each entrance lane in each direction. Then build a vissim simulation traffic flow direction prediction model based on the KNN algorithm, including the construction of feature vectors, cross-validation methods to determine K values, and local estimation methods. The experimental results show that the average relative error between the predicted traffic flow direction and the actual traffic flow direction tends to 0.27. Due to the small amount of data, the prediction result is more accurate.

An ensemble method for short-term wind power prediction considering error correction strategy

With a high proportion of renewable energy injected into the power grid, the accurate wind power prediction of wind farms is key to improving power quality and ensuring the stable operation of the power grid. In this paper, an ensemble learning prediction model considering the rolling error correction strategy used for short-term wind power prediction is proposed. Firstly, the ensemble learning prediction model integrating multiple Gradient Boosting Trees (GBDTs) based on Bayesian optimization is established, and the characteristics of prediction errors are analyzed. To further improve the prediction accuracy, a daily rolling error correction strategy based on the Swing Window segmentation method, Spearman correlation coefficient and Quantile Regression is established to obtain the optimal compensation amount. Finally, the corrected short-term wind power prediction results are obtained. The two-year dataset collected from a regional wind farm in China is used as the benchmark test. Different prediction models and error correction strategies are compared, and the effectiveness of the proposed method is comprehensively evaluated. The results from different seasons show that the proposed prediction method has good stability, and the proposed error correction strategy has good generalization ability.

A Multivariate Statistical Model of Water Leakage in Urban Water Supply Networks Based on Random Matrix Theory

In this paper, model construction of random matrix theory is carried out to analyze water leakage of urban water supply pipeline network, and a multivariate statistical model of water leakage of urban water supply pipeline network based on random matrix theory is proposed. This paper presents a three-level DMA, short-term water demand prediction method based on water use patterns, aiming to solve the problem of inaccurate water demand prediction caused by climate and social factors. Correlation analysis and factor analysis analyze the main factors affecting daily water demand changes. The extracted information of the main influencing factors and historical monitoring data are used to establish the Elman water demand prediction model. At the same time, the daily water consumption pattern was extracted by correlation analysis, matched with the water consumption pattern of the predicted day, and combined with the expected value of water demand for momentary water demand allocation, which improved the prediction accuracy of short-term water demand. This paper is based on the precise leakage location based on the real-time hydraulic model. The real-time hydraulic model under leakage conditions is established, GA and NSGA2 are designed, and the nondominated ranking algorithm with elite strategy is used to achieve accurate leakage location using hydraulic model calibration search. This paper uses a random matrix algorithm to establish the leakage estimation model. A laboratory case pipe network is built to simulate the water supply conditions in residential areas, and the operating data of the laboratory case pipe network are used to confirm that the random matrix leakage estimation model can estimate the leakage. The applicability of the random matrix leakage estimation model and the wavelet transform algorithm for leakage calculation are compared in an engineering example. The example confirms that the leakage amount calculated by the random matrix model has a less relative error and more similar trend than the wavelet transform algorithm.

Short-term wind speed prediction based on FEEMD-PE-SSA-BP.

As one of the renewable energy power generation methods, wind power generation shows a high growth trend. However, while wind power is connected to the grid, the volatility and instability of wind power make the power system produce a lot of uncertain fluctuations, which greatly affects the power quality and jeopardizes the stable operation of the power system. Therefore, high wind speed forecasting accuracy can provide a solid basis for grid management, improve the power system's ability to consume wind power, and ensure the safety and stabilization of the power system. In order to solve the problem of inaccurate prediction caused by the non-linearity and unsteadiness of wind speed series, this paper proposes a Fractal Ensemble Empirical Mode Decomposition (FEEMD)-Permutation Entropy (PE)-Sparrow Search Algorithm (SSA)-Error Back Propagation (BP) neural network method for short-term wind speed prediction. This method first uses FEEMD to decompose the original wind speed in order from high to low frequency; then calculates the entropy value of each component, and merges the components with similar entropy values to effectively reduce the computation; and finally, the new sub-series are predicted by SSA-BP model, and the predicted value of the merged new sub-sequences are accumulated to obtain the final wind speed prediction results. The simulation study shows that the proposed prediction model is not only fast and accurate, but also suitable for short-term wind speed prediction.

An EMD-LSTM-SVR model for the short-term roll and sway predictions of semi-submersible

A reliable short-term prediction method is of significance in ensuring safety and reducing operation time. This paper proposes a hybrid empirical mode decomposition (EMD) model for the short-term prediction of motions of marine structures. The combination of long short term memory (LSTM) network and support vector regression (SVR) model offers an accurate prediction on the subcomponents of EMD process. The training and test data were provided by the model tests of a semi-submersible with the taut, catenary and tension leg mooring systems. The predicted results of present EMD-LSTM-SVR model were compared with those of the autoregressive (AR) model and the EMD-SVR model. The influence of the boundary effect, spectrum bandwidth and non-stationarity on the predicted results were investigated. The results of contrast experiments demonstrated that the proposed EMD-LSTM-SVR model obtains better predicted results than the other two models in most cases. The prediction accuracy has a negative correlation with the broad-banded spectrum and the strong non-stationarity. The EMD technique is beneficial for dealing with the broad spectral and non-stationary motion time series to reduce the prediction errors.

A short-term probabilistic photovoltaic power prediction method based on feature selection and improved LSTM neural network

With the increase of solar photovoltaic(PV) penetration in power system, the impact of random fluctuation of PV power on the secure operation of power grid becomes more and more serious. An efficient PV forecasting approach is proposed to accurately quantify the variability and uncertainty of the power production from PV systems. This study proposes a classification method of weather types based on cloud cover and visibility. A PV power forecasting model is proposed, based on various meteorological data including cloud cover and visibility and in order to make the model show better performance, Maximal Information Coefficient(MIC) is used to select the feature variables. Coupled Input and Forget Gate(CIFG) network is proposed to minimize structure without significantly decreasing the prediction accuracy. Furthermore, a new hybrid method combining Quantile Regression(QR) and CIFG network is proposed to predict the conditional quantile of PV output. Afterward, Kernel Density Estimation(KDE) method is used to estimate PV output probabilistic density function(PDF) according to these conditional quantiles of PV output. The effectiveness and high reliability of the proposed forecasting model are demonstrated through several other forecasting methods, and a significant improvement in PV power prediction is observed.

Short-Term Prediction of Wind Power Considering the Fusion of Multiple Spatial and Temporal Correlation Features

As the wind power penetration increases, the short-term prediction accuracy of wind power is of great importance for the safe and cost-effective operation of the power grid in which the wind power is integrated. Traditional wind farm power prediction uses numerical weather prediction (NWP) information as an important input but does not consider the correlation characteristics of NWP information from different wind farms. In this study, a convolutional neural network–long short-term memory based short-term prediction model for wind farm clusters is proposed. Additionally, a feature map is established for multiposition NWP information, the spatial correlation of NWP information from different wind farms is fully explored, and the feature map is trained using the spatiotemporal model to obtain the short-term prediction results of wind farm clusters. Finally, as a case study, the operational data of a wind farm cluster in China are analyzed, and the proposed model outperforms traditional short-term prediction methods in terms of prediction accuracy.

An integrated 3D CNN-GRU deep learning method for short-term prediction of PM2.5 concentration in urban environment

This study proposes a new model for the spatiotemporal prediction of PM2.5 concentration at hourly and daily time intervals. It has been constructed on a combination of three-dimensional convolutional neural network and gated recurrent unit (3D CNN-GRU). The performance of the proposed model is boosted by learning spatial patterns from similar air quality (AQ) stations while maintaining long-term temporal dependencies with simultaneous learning and prediction for all stations over different time intervals. 3D CNN-GRU model was applied to air pollution observations, especially PM2.5 level, collected from several AQ stations across the city of Tehran, the capital of Iran, from 2016 to 2019. It could achieve promising results compared to the methods such as LSTM, GRU, ANN, SVR, and ARIMA, which are recently introduced in the literature; it estimates 84% (R2 = 0.84) and 78% (R2 = 0.78) of PM2.5 concentration variations for the next hour and the following day, respectively.

A short-term wind speed prediction method based on interval type 2 fuzzy model considering the selection of important input variables

Wind speed prediction is a challenging task in wind energy resource prediction. Wind speed time series, which are a highly variable data source, require highly nonlinear time characteristics for prediction. Most prediction models use hybrid models to improve prediction accuracy, which increases the complexity of the model. This paper proposes an interval type-2 (IT2) wind speed prediction model based on the selection of important input variables. First, the two-stage fuzzy curve are used to determine the model input so as to reduce redundant input and further reduce the complexity of the model. Second, a Gaussian membership function with a variable centre is used to further improve the prediction accuracy of the model. Finally, the recursive least squares method is used to complete the identification of the conclusion parameters. The proposed model has high generalisability and IT2 robustness, and can thus realise deterministic wind speed prediction. The wind speed prediction results for a wind farm in Colorado, USA, in two time periods show that compared with the type-1 fuzzy model and other type-2 fuzzy models, the proposed model provides more accurate short-term wind speed forecasting.