Very-Short-Term Wind Power Forecasting for New-Built Wind Farms Based on Online Sparse Vector Autoregressive Model

In recent years, new energy has achieved rapid development in the whole world. However, with the gradual increase in the installed capacity of new energy, because of the uncertainty and volatility of new energy output, new energy has brought many impacts and challenges to the operation of the power system. In order to guarantee the safe and stable operation of the power system, the power system has to provide sufficient flexibility to cope with the volatility and uncertainty of the new energy output. Improving the prediction error accuracy of new energy output can effectively reduce the uncertainty of new energy output, so as to reduce the demand for power system flexibility, reduce the operating cost, and improve the system operation reliability. This paper utilizes the data mining technology to establish the prediction error identification module. Based on the data feature extracted from the wind power prediction input data, the potential abnormal wind power prediction error can be identified, and the original wind power output prediction result can be corrected, so as to improve the predication accuracy of the wind power generation. At the same time, in the prediction and identification process, the wind speed prediction data and historical wind power output data of neighbouring regional wind farms will also be utilized to improve the prediction accuracy of wind farm output in this region. The predicated time scale is 12 hours ahead of time, and the predicted result is the wind power output of the next 12 hours (a predicted resolution of 1 hour, and a total of 12 predicted values).

The fluctuation of active power output of wind farm has many negative impacts on large-scale wind power integration into power grid. In this paper, flywheel energy storage system (FESS) was connected to AC side of the doubly-fed induction generator (DFIG) wind farm to realize smooth control of wind power output. Based on improved wind power prediction algorithm and wind speed-power curve modeling, a new smooth control strategy with the FESS was proposed. The requirement of power system dispatch for wind power prediction and flywheel rotor speed limit were taken into consideration during the process. While smoothing the wind power fluctuation, FESS can track short-term planned output of wind farm. It was demonstrated by quantitative analysis of simulation results that the proposed control strategy can smooth the active power fluctuation of wind farm effectively and thereby improve power quality of the power grid.

With the rapid development of renewable energy, accurately forecasting wind power is crucial for the stable operation of power systems and effective energy management. This paper proposes a short-term wind power forecasting method based on the Orthogonalized Maximal Information Coefficient (OMNIC) combined with an Adaptive fractional Generalized Pareto motion (fGPm) model. The method quantifies the influence of meteorological factors on wind power prediction and identifies the optimal set and number of influencing factors. The model accounts for long-range dependence (LRD) in time series data and constructs an uncertainty model using the properties and parameters of the fractional generalized Pareto distribution (GPD), significantly improving prediction accuracy under nonlinear conditions. The proposed approach was validated using a real dataset from a wind farm in northwest China and compared with other models such as Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU). Results show that the adaptive fGPm model reduces RMSE by 0.448 MW and 0.466 MW, MAPE by 6.936% and 9.702%, and achieves an average R2 of 0.9826 compared to CNN-GRU and CNN-LSTM. The improvement is due to the dynamic adjustment to data trends and effective use of LRD features. This method provides practical value in improving wind power prediction accuracy and addressing grid integration and regulation challenges.

Accurate short-term forecasting of wind farm power generation is of great significance to economic development and stable operation of power system. In order to improve the accuracy of wind power prediction, this paper presents a short-term wind power prediction method based on empirical mode decomposition and deep learning. We firstly decompose historical data into two-dimensional tensors by using empirical mode decomposition, and then extract the local features of data by using convolutional neural network. The features are transmitted to the bidirectional long short-term memory for prediction. The measured wind power data form wind farm in Xinjiang are used for verification. We use root mean square error and mean absolute error as evaluation indexes to test the algorithm. Results of the experiment show that the reconstructed two-dimensional tensor is beneficial to the ability of convolutional neural network to extract local features, and the prediction accuracy of our method is higher than that of other traditional prediction algorithms.

Wind power predication is significant for the safety and stability of power system operation. Due to the power forecasting single model or combination model without considering the information fusion and the influence of forecasting error on power dispatching, forecasting results are not accurate enough. In the paper, a novel combination model for wind power forecasting with error evaluation parameter based on cross entropy theory is proposed. Wind power prediction is seen as information fusion, and the weights of different wind power prediction methods is set according to their cross levels. Besides, an error evaluation parameter is also proposed to estimate the different influences of positive and negative error on power scheduling. Application results in an actual wind farm show that the cross levels of different wind power prediction methods can be identified effectively and prediction precision is improved by the proposed method.

During recent years, wind energy has moved from an emerging technology to a nearly competitive technology. This fact, coupled with an increasing global focus on environmental concern and a political desire of a certain level of diversification in the energy supply, ensures wind energy an important role in the future electricity market. For this challenge to be met in a cost-efficient way, a substantial part of new wind turbine installations is foreseen to be erected in big onshore or offshore wind farms. This fact makes the production, loading and reliability of turbines operating under such conditions of particular interest.

This paper proposes a coordinated operational strategy of energy storage system (ESS) and wind farm to reduce wind power spillage and alleviate wind power fluctuation. Firstly, power curve of a wind farm, in the form of an equivalent wind turbine, is determined by least square fitting. Based on the power output characteristic of the wind farm and historic operating data, the statistical properties of wind power spillage and fluctuation can be obtained. Secondly, the target for wind farm and ESS power output is calculated. Power generation/absorption capabilities of ESS and available power output reducing range of the wind farm are quantified as well, considering the operational constraints of ESS and dispatched wind power output. Finally, a coordinated operational strategy is developed not only to adjust the combined ESS and wind farm to meet the dispatch requirement, but also to ensure ESS and the wind farm to satisfy operational constraints. Case studies are conducted on a practical wind farm with battery energy storage system installed, which demonstrate that the coordination of ESS can greatly boost the revenue and improve power quality of wind farm.

An algorithm for forecasting day-ahead wind power via novel long short-term memory and wind power ramp events

Economic feasibility of a wind-battery system in the electricity market with the fluctuation penalty

The accuracy of wind power forecasting has a very important influence on the safe and stable operation of power system. However, wind power prediction is very difficult, especially under the environment of massive data. This paper presents a novel multi-step ahead wind power prediction model based on recurrent neural network (RNN) with long short-term memory (LSTM) unit or gated recurrent unit (GRU) to improve the accuracy. Firstly, an overall forecasting framework for wind power with diverse forms of optional hybrid models is proposed. Moreover, an innovative LSTM/GRU-based forecasting model is developed with a wind speed correction process using numerical weather prediction (NWP) data. Through the application of GRU network, the correction process can provide corrected wind speed at the predicted moment as an input of the forecasting model. Finally, the experimental results demonstrate the superiority of the proposed RNN approaches as compared to the ARIMA method and SVM method while the main distinctions between the LSTM and GRU network are also illustrated.

Accurate wind power prediction is of great significance for ensuring the stable operation of power systems. Although the rapid development of deep learning technology has significantly improved the accuracy of wind power prediction, existing methods often lack sufficient capability for information interaction among variables when processing multivariate time series data, and struggle to capture long-range dependencies in longer sequences, which limits their predictive performance. To address this, this paper proposes a novel model named Efficient Feature Aggregation based on Mamba (EFAMamba) for multistep wind power prediction. This model introduces Mamba to more effectively capture long-term dependencies in time series. The EFA module aggregates all variable sequences into a global representation, which is then dispatched and fused with each variable sequence representation, significantly promoting information interaction among variables. The entire framework of EFAMamba can comprehensively consider historical data of multiple variables as well as future meteorological variable information, making it particularly suitable for multi-step wind power prediction. Extensive experiments conducted on real wind farm dataset have validated that the proposed EFAMamba demonstrates significant advantages over current state-of-the-art (SOTA) methods in terms of prediction accuracy for multi-step forecasting tasks, particularly excelling under longer prediction horizons. Specifically, when predicting 72 hours ahead, the NMAE, NRMSE, and QR are improved by 9.96%, 3.77%, and 1.39%, respectively, demonstrating the effectiveness of the proposed EFAMamba in wind power prediction. The source code and datasets are available at https://github.com/USTCAI4EEE/EFAMamba.

It is of great significance for the safe and stable operation of power system to master the fluctuation characteristics of wind power output. On the basis of analyzing a large number of field measured data, a weighted mixed Gaussian probability model is proposed to simulate short-time wind power fluctuation characteristics of wind farm cluster, that evaluation indices to reflect the short-time maximum fluctuation of wind power output and maximum likelihood estimation algorithm based on Expectation Maximization (EM) to estimate model parameters are put forward. This model is compared with various other kinds of probability distribution model and the simulation results show that the weighted mixed Gaussian probability model possesses the highest precision, so as the effectiveness of the weighted mixed Gaussian probability model is verified.

Day-ahead wind power forecasting based on the clustering of equivalent power curves

With the rapid increase in wind power, its strong randomness has brought great challenges to power system operation. Accurate and timely ultra-short-term wind power prediction is essential for the stable operation of power systems. In this paper, an LsAdam–LSTM model is proposed for ultra-short-term wind power prediction, which is obtained by accelerating the long short-term memory (LSTM) network using an improved Adam optimizer with loss shrinkage (LsAdam). For a specific network topology, training progress heavily depends on the learning rate. To make the training loss of LSTM shrink faster with standard Adam, we use the past training loss-changing information to finely tune the next learning rate. Therefore, we design a gain coefficient according to the loss change to adjust the global learning rate in every epoch. In this way, the loss change in the training process can be incorporated into the learning progress and a closed-loop adaptive learning rate tuning mechanism can be constructed. Drastic changes in network parameters will deteriorate learning progress and even make the model non-converging, so the gain coefficient is designed based on the arctangent function with self-limiting properties. Because the learning rate is iteratively tuned with past loss-changing information, the trained model will have better performance. The test results on a wind turbine show that the LsAdam–LSTM model can obtain higher prediction accuracy with much fewer training epochs compared with Adam–LSTM, and the prediction accuracy has significant improvements compared with BP and SVR models.

The output power prediction of wind farm is the key to effective utilization of wind energy and reduction of wind curtailment. However, the prediction of output power has long been a difficulty faced by both academia and the wind power industry, due to the high stochasticity of wind energy. This paper attempts to improve the ultra-short-term prediction accuracy of output power in wind farm. For this purpose, an output power prediction model was constructed for wind farm based on the time sliding window (TSW) and long short-term memory (LSTM) network. Firstly, the wind power data from multiple sources were fused, and cleaned through operations like dimension reduction and standardization. Then, the cyclic features of the actual output powers were extracted, and used to construct the input dataset by the TSW algorithm. On this basis, the TSW-LSTM prediction model was established to predict the output power of wind farm in ultra-short-term. Next, two regression evaluation metrics were designed to evaluate the prediction accuracy. Finally, the proposed TSW-LSTM model was compared with four other models through experiments on the dataset from an actual wind farm. Our model achieved a super-high prediction accuracy 92.7% as measured by d_MAE, an evidence of its effectiveness. To sum up, this research simplifies the complex prediction features, unifies the evaluation metrics, and provides an accurate prediction model for output power of wind farm with strong generalization ability.