Short-term Power Prediction Research Articles

Enhancing Photovoltaic Power Predictions with Deep Physical Chain Model

Predicting solar power generation is a complex challenge with multiple issues, such as data quality and choice of methods, which are crucial to effectively integrate solar power into power grids and manage photovoltaic plants. This study creates a hybrid methodology to improve the accuracy of short-term power prediction forecasts using a model called Transformer Bi-LSTM (Bidirectional Long Short-Term Memory). This model, which combines elements from the transformer architecture and bidirectional LSTM (Long–Short-Term Memory), is evaluated using two strategies: the first strategy makes a direct prediction using meteorological data, while the second employs a chain of deep learning models based on transfer learning, thus simulating the traditional physical chain model. The proposed approach improves performance and allows you to incorporate physical models to refine forecasts. The results outperform existing methods on metrics such as mean absolute error, specifically by around 24%, which could positively impact power grid operation and solar adoption.

Wind power short-term prediction based on digital twin technology

Wind power short-term prediction based on digital twin technology

Short-term PV power prediction study based on EEMD-KPCA-LSTM

Abstract Short-term PV power prediction is important for PV power generation stability and power dispatch. In order to cope with the noise disturbances occurring in PV power generation, this paper proposes a prediction model with an improved algorithm. Firstly, the annual PV data are decomposed by using EEMD to obtain the eigenmode function components; KPCA is used to reduce the dimensionality of the original sequences brought by EEMD, eliminate redundancy, and divide the test set and training set; The LSTM neural network is used to model the multivariate feature sequences in dynamic time to achieve the prediction of PV power. The model compares the prediction model of a single algorithm, and the accuracy of the prediction is improved.

3DTCN-CBAM-LSTM short-term power multi-step prediction model for offshore wind power based on data space and multi-field cluster spatio-temporal correlation

The accuracy of offshore wind power forecasting (OWPF) is the basis for guaranteeing the safe dispatch and economic operation of power systems, which can reduce the technical and economic risks faced by power market participants. Combining the theoretical approaches of data space and deep learning, this study proposes a hybrid short-term OWPF framework based on the integrated consideration of spatial and temporal correlations of regional field clusters in the data space and the 3DTCN-CBAM-LSTM model. Firstly, other offshore wind farms with spatio-temporal correlation with the target offshore wind farm are screened out using the K-means spatial clustering algorithm. The historical power sequences determined to be similar to the target offshore wind farm and feature data of target offshore wind farms are used as inputs to the prediction model to improve the efficiency of the framework's power prediction. Secondly, the short-term power prediction framework of offshore wind power based on the 3DTCN-CBAM-LSTM model is constructed, using the 3DTCN model's ability to capture the spatio-temporal dynamic characteristics of offshore wind farm data to improve the accuracy of the prediction model. The temporal characteristics of the data are strengthened using the LSTM model to improve the stability of the prediction model. Finally, the CBAM mechanism is introduced to combine channel attention and spatial attention to eliminate irrelevant information and capture the spatio-temporal characteristics of the data in a more targeted way, which further improves the accuracy of OWPF. To verify the practicality and reliability of the wind power prediction framework described in this study, the differences between the prediction results of the framework and those of the benchmark model are compared using single-step prediction and multi-step prediction (4, 6, 8, and 12 steps). Compared with the benchmark model (BPNN), the single-step prediction results show that the MAE, MAPE, and MSE metrics of the 3DTCN-CBAM-LSTM model are reduced by 20.004 MW, 50.13%, 55.61%, and 80.29%, respectively, and the R2 is improved by 3.39%, which is a substantial improvement in prediction performance. The multi-step prediction results show that, with the progression of the prediction steps, the 3DTCN-CBAM-LSTM model has the highest prediction accuracy and model fit at each step. Therefore, the proposed 3DTCN-CBAM-LSTM model presents obvious advantages and reliability in the prediction of offshore wind power multi-step power.

Improved informer PV power short-term prediction model based on weather typing and AHA-VMD-MPE

Precise prediction of PV power in the short term is crucial for maintaining power system stability and balance. However, the performance of conventional time series prediction models on short-term long series prediction is scarcely sufficient because of the stochastic and turbulent character of PV power data. This work suggests a PV short-term power forecast model based on weather type, AHA-VMD-MPE decomposition reconstruction, and improved Informer combination to tackle this issue. Firstly, a SUM-ApEn-K-mean++ multidimensional clustering method to group the dataset by weather conditions. Then an AHA-VMD-MPE decomposition model is proposed to decompose the historical power data Finally the Informer model is improved and the improved model is utilized to predict the PV power under various weather conditions. The model exhibits great accuracy and stability in short-term PV power prediction, as demonstrated by the experimental results, which were validated using measured data from many PV power plants.

Informer Short-Term PV Power Prediction Based on Sparrow Search Algorithm Optimised Variational Mode Decomposition

The output power of PV systems is influenced by various factors, resulting in strong volatility and randomness, which makes it difficult to forecast. Therefore, this paper proposes an Informer prediction model based on optimised VMD for predicting short-term PV power. Firstly, the temporal coding of the Informer model is improved and, secondly, the original sequence is decomposed into multiple modal components using VMD, and then optimisation of the results of VMD in conjunction with the optimisation strategy of SSA improves the characteristics of the time series data. Finally, the refined data are fed into the Informer framework for modelling and prediction, utilising the self-attention mechanism and multiscale feature fusion of Informer to precisely forecast PV power. The power of PV prediction data from the SSA-VMD-Informer model and four other commonly used models is compared. Experimental results indicate that the SSA-VMD-Informer model performs exceptionally well in short-term PV power prediction, achieving higher accuracy than traditional methods. As an example, the results of predicting the PV power on 24 April in a region of Xinjiang are 1.3882 for RMSE, 0.8310 for MSE, 1.14 for SDE, and 0.9944 for R2.

Forecasting a Short-Term Photovoltaic Power Model Based on Improved Snake Optimization, Convolutional Neural Network, and Bidirectional Long Short-Term Memory Network.

The precision of short-term photovoltaic power forecasts is of utmost importance for the planning and operation of the electrical grid system. To enhance the precision of short-term output power prediction in photovoltaic systems, this paper proposes a method integrating K-means clustering: an improved snake optimization algorithm with a convolutional neural network-bidirectional long short-term memory network to predict short-term photovoltaic power. Firstly, K-means clustering is utilized to categorize weather scenarios into three categories: sunny, cloudy, and rainy. The Pearson correlation coefficient method is then utilized to determine the inputs of the model. Secondly, the snake optimization algorithm is improved by introducing Tent chaotic mapping, lens imaging backward learning, and an optimal individual adaptive perturbation strategy to enhance its optimization ability. Then, the multi-strategy improved snake optimization algorithm is employed to optimize the parameters of the convolutional neural network-bidirectional long short-term memory network model, thereby augmenting the predictive precision of the model. Finally, the model established in this paper is utilized to forecast photovoltaic power in diverse weather scenarios. The simulation findings indicate that the regression coefficients of this method can reach 0.99216, 0.95772, and 0.93163 on sunny, cloudy, and rainy days, which has better prediction precision and adaptability under various weather conditions.

Research on the Short-Term Prediction of Offshore Wind Power Based on Unit Classification

The traditional power prediction methods cannot fully take into account the differences and similarities between units. In the face of the complex and changeable sea climate, the strong coupling effect of atmospheric circulation, ocean current movement, and wave fluctuation, the characteristics of wind processes under different incoming currents and different weather are very different, and the spatio-temporal correlation law of offshore wind processes is highly complex, which leads to traditional power prediction not being able to accurately predict the short-term power of offshore wind farms. Therefore, aiming at the characteristics and complexity of offshore wind power, this paper proposes an innovative short-term power prediction method for offshore wind farms based on a Gaussian mixture model (GMM). This method considers the correlation between units according to the characteristics of the measured data of units, and it divides units with high correlation into a category. The Bayesian information criterion (BIC) and contour coefficient method (SC) were used to obtain the optimal number of groups. The average intra-group correlation coefficient (AICC) was used to evaluate the reliability of measurements for the same quantized feature to select the representative units for each classification. Practical examples show that the short-term power prediction accuracy of the model after unit classification is 2.12% and 1.1% higher than that without group processing, and the mean square error and average absolute error of the short-term power prediction accuracy are reduced, respectively, which provides a basis for the optimization of prediction accuracy and economic operation of offshore wind farms.

Dual NWP wind speed correction based on trend fusion and fluctuation clustering and its application in short-term wind power prediction

With the continuous growth of grid-connected installed capacity of wind power, the inevitable gap, volatility and randomness of wind power pose challenges to the stability of real-time operation of power system. Accurate wind power prediction (WPP) can effectively ensure the safe and stable operation of power system. At present, the main input of short-term power prediction based on data-driven model is numerical weather prediction (NWP), and its prediction accuracy leads to the failure to effectively improve the accuracy of short-term power prediction. To solve this problem, this paper proposes a dual NWP wind speed (WS) correction method based on trend fusion and fluctuation clustering. First, the WS trend is used to construct a new input feature, and the NWP WS error distribution is determined to establish a more correct and stable mapping relationship with the NWP WS error. Secondly, the error is decomposed by Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and the trend and fluctuation components are defined by Pearson coefficient. The trend curve features in the short 24h period were extracted from the two dimensions of time and value, and the trend and fluctuation curve databases under different scenarios were formed by K-Medoids clustering. Finally, the trend component is modified by the Attention-GRU model, and the corresponding trend component is searched and matched from the database to correct the fluctuation component. The NWP WS is modified by the superposition of the two components and the short-term WPP is made. Two wind farms in Mengxi, China were used for example analysis, and the prediction accuracy was improved by 10 % and 13.1 % respectively, which demonstrated the effectiveness of the proposed method.

Short-term PV power prediction study based on FCM-BiGRU

Addressing the issue of large prediction error in short-term load prediction of existing neural network models, this study proposes a short-term load forecasting approach that combines fuzzy C-mean clustering and a two-way gated recurrent neural network model. Fuzzy C-mean clustering is first applied to cluster the original data into three typical days, and the grouped data are trained using a bidirectional gated recurrent neural network model for load prediction. The conclusive experiment demonstrates that the proposed approach introduced in this study exhibits a high prediction accuracy in the context of short-term photovoltaic output forecasting, and there is also a substantial error reduction compared with the existing neural network methods.

A short-term power prediction method for wind farm cluster based on the fusion of multi-source spatiotemporal feature information

In recent years, the installed capacity of wind power has rapidly increased. And the wind power prediction is the foundation for ensuring large-scale wind power grid connection. The current short-term prediction methods of wind farm cluster (WFC) are difficult to sufficiently extract spatiotemporal features to achieve high-precision prediction. The article proposes a short-term power prediction method for WFC based on deep attention embedded graph clustering-TimesNet (DAEGC-TimesNet). Firstly, the directed power curves of WFC are proposed to analyze wind data. Then, a graph attention network is constructed based on geographic location and numerical weather prediction (NWP) information to guide clustering algorithms to achieve effective cluster partitioning. Finally, the input of model is constructed based on the feature information from various sub-clusters of WFC and the prediction result is obtained through the TimesNet. The method is applied to WFCs in Inner Mongolia, Jilin province and Yunnan province of China. The result shows that the RMSE reduces 0.0155 and the MAE reduces 0.0156 as well as the coefficient of determination and accuracy rate are highest comparing with comparative algorithms averagely based on above three WFCs. The simulation results are superior to the comparison algorithms, which makes greater contributions to ensure regional power supply.

Optimal Scheme Selection Model of Distributed Photovoltaic Consumption in Smart City Based on Gaussian Mixture Model

In an effort to guarantee the stable action and photovoltaic absorption capability of the spread mesh after a macro digit of distributed solar panels are related to the mesh, this paper puts forward the definitive scheme selection model of distributed photovoltaic absorption in smart cities based on Gaussian mixture model. The principal constituent analysis arithmetic is used to process the chronicle data of distributed photovoltaic action, and the processed data are transmitted to the Gaussian compound model. According to this model, the short-term power situation of distributed photovoltaic is predicted, and the external ability and restrict requirements of the definitive programme of distributed photovoltaic consumption are determined according to the prediction fruits. The definitive programme of distributed photovoltaic consumption is selected by the approximate ideal solution sorting method. The measurement fruits show that the model has a good short-term power prediction effect of distributed photovoltaic, and the prediction fruits are in high agreement with the actual fruits, and the coverage rate and interval average bandwidth fruits are above 0.918 and below 0.11 respectively. The photovoltaic consumption of the definitive programme is above 130%.

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 photovoltaic output power prediction based on similar day and optimized BP neural network

Abstract The photovoltaic (PV) output power is affected by the ambient temperature, seasons, weather and other factors, which makes the PV output power very unstable. Therefore, accurate prediction of the PV output power is highly beneficial. This paper is dedicated to finding a simple and reliable PV short-term output power prediction method. First, we choose four key parameters, season, solar irradiance, temperature and relative humidity, to predict PV output power by using the similar day theory, which is mainly because the above four parameters are decisive for PV output power, although more parameters being taken into account will make the prediction accuracy higher, but it brings along with it an increase in the complexity; secondly, we choose the backpropagation (BP) neural network because it is very suitable for the PV output power prediction due to its excellent learning ability; finally, we optimize the standard BP neural network in loss functions, activation functions and optimizers to further improve its prediction accuracy. We validate the proposed method in different seasons and under other weather conditions. The results show that the proposed method has better prediction results, the optimized BP neural network has better performance compared with the standard BP neural network, and the standard deviation of the prediction is improved from ~1382, ~1571, ~1457, ~989 to ~903, ~792, ~333 and ~409.

Adaptive Encoder-Decoder Model Considering Spatio-Temporal Features for Short-Term Power Prediction of Distributed Photovoltaic Station

Adaptive Encoder-Decoder Model Considering Spatio-Temporal Features for Short-Term Power Prediction of Distributed Photovoltaic Station

Hybrid Network Model Based on Data Enhancement for Short-term Power Prediction of New PV Plants

Hybrid Network Model Based on Data Enhancement for Short-term Power Prediction of New PV Plants

Adaptive SPP–CNN–LSTM–ATT wind farm cluster short-term power prediction model based on transitional weather classification

With the expansion of the scale of wind power integration, the safe operation of the grid is challenged. At present, the research mainly focuses on the prediction of a single wind farm, lacking coordinated control of the cluster, and there is a large prediction error in transitional weather. In view of the above problems, this study proposes an adaptive wind farm cluster prediction model based on transitional weather classification, aiming to improve the prediction accuracy of the cluster under transitional weather conditions. First, the reference wind farm is selected, and then the improved snake algorithm is used to optimize the extreme gradient boosting tree (CBAMSO-XGB) to divide the transitional weather, and the sensitive meteorological factors under typical transitional weather conditions are optimized. A convolutional neural network (CNN) with a multi-layer spatial pyramid pooling (SPP) structure is utilized to extract variable dimensional features. Finally, the attention (ATT) mechanism is used to redistribute the weight of the long and short term memory (LSTM) network output to obtain the predicted value, and the cluster wind power prediction value is obtained by upscaling it. The results show that the classification accuracy of the CBAMSO-XGB algorithm in the transitional weather of the two test periods is 99.5833% and 95.4167%, respectively, which is higher than the snake optimization (SO) before the improvement and the other two algorithms; compared to the CNN–LSTM model, the mean absolute error (MAE) of the adaptive prediction model is decreased by approximately 42.49%–72.91% under various transitional weather conditions. The relative root mean square error (RMSE) of the cluster is lower than that of each reference wind farm and the prediction method without upscaling. The results show that the method proposed in this paper effectively improves the prediction accuracy of wind farm clusters during transitional weather.

Short-term wind farm cluster power prediction based on dual feature extraction and quadratic decomposition aggregation

The intermittency and uncertainty of wind energy affect the accuracy of wind power prediction, which is not conducive to the safe and stable operation of the power system. Aiming at this problem, a short-term power prediction method for multi-Stacking wind power clusters based on dual feature extraction and quadratic decomposition aggregation is proposed. Firstly, a graph network matrix is built based on the spatial relationship of wind farm clusters to extract spatial features, and then a subset of features is filtered using a feature selection method combining filter and wrapper. The original signal is then decomposed using a quadratic decomposition method combining variational modal decomposition and swarm decomposition to reduce the volatility of the signal; the wind power signals are reconstructed using multiscale permutation entropy, and finally the reconstructed signals are fed into multi-Stacking models for information modelling depending on the frequency to predict rolling multi-step power of wind farm clusters. A real wind farm cluster arithmetic example is used for validation. The experimental results show that NRMSE, NMAE, and MAPE are improved by 15.8 %, 8.99 %, and 6.79 % compared with the traditional model. The method has important reference value for both grid scheduling and stable operation.

Short-Term Power Demand Forecasting Using Blockchain-Based Neural Networks Models

With the rapid development of blockchain technology, blockchain-based neural network short-term power demand forecasting has become a research hot spot in the power industry. This paper aims to combine neural network algorithms with blockchain technology to establish a trustworthy and efficient short-term demand forecasting model. By leveraging the distributed ledger and immutability features of blockchain, we ensure the security and reliability of power demand data. Meanwhile, short-term power demand forecasting research using neural networks has the potential to increase the stability of the power system and offer opportunities for improved operations. In this paper, the root mean-square-error model evaluation indicator was used to compare the back propagation (BP) neural network algorithm and the traditional forecasting algorithm. The evaluation was performed on the randomly selected five household power datasets. The results show that, by comparing the long short-term memory network (LSTM) model with the BP neural network model, it was determined that the average prediction impact increases by about 25.7% under stable power demand. The short-term power prediction model of the BP neural network has the average error values more than two times lower than the traditional prediction model. It was shown that the use of the BP neural network algorithm and blockchain could increase the accuracy of short-term power demand forecasting, allowing the neural network-based algorithm to be implemented and taken into account in the research on short-term power demand forecasting.

Research on Short-term Power Prediction of Distributed Photovoltaic Power Generation

With the vigorous development of solar photovoltaic power generation, accurate prediction of short-term photovoltaic power generation has become an important issue. However, since the short-term power generation of photovoltaic power generation is affected by many environmental factors and has great uncertainty, the safe operation of the power grid is challenged. Therefore, it is necessary to simulate the power generation in advance through data models and predict the power generation amount. The power grid relies on the valuation and makes corresponding adjustments to maintain the stability of the power grid system.