Wind power prediction based on long short-term memory neural network

Using enhanced crow search algorithm optimization-extreme learning machine model to forecast short-term wind power

A new multi-timescale optimal scheduling model considering wind power uncertainty and demand response

With the rapid development of wind energy, the imperative for precise wind power predictions has intensified, with the crux lying in forecasting wind speeds. The accurate short-term (1 to 3 days) forecast of wind speeds at the hub height in boundary layer poses a significant scientific challenge. Generating such forecasts for wind farms 1 to 3 days in lead time necessitates reliance on global weather forecast products and the WRF model. In pursuit of heightened accuracy, artificial intelligence (AI) algorithms are employed to refine WRF-predicted wind speeds based on observational data. This study draws upon observational data from five operational wind farms over three years, employing diverse deep time-series models, to examine the effectiveness and limitations of these models in post-processing corrections for WRF-predicted wind speeds. Based on our examination, we conclude that: 1) Transformer-based models have significant untapped potential, with the Pyraformer model emerging as a well-suited temporal model for post-processing corrections in wind speed and power predictions. 2) Traditional full-attention mechanisms are less effective, highlighting the importance of sparse attention as a vital approach for capturing temporal correlations in such problems. 3) The optimal model demonstrates a reduction of approximately 20% in RMSE for single-point post-processing corrections. In addition, wind speed prediction accuracy reaches around 86%, and power prediction accuracy is approximately 82%. 4) AI-based post-processing corrections may encounter challenges, including the underestimation for high-value and difficulties in reproducing forecasts below the average value.

Dispatch results depend on the forecast wind power in an electric power grid. High levels of uncertainty in wind power lead to large forecast errors (FEs). Wind power FE creates an imbalance between power load demand and supply, which poses risks to the power grid. To minimise the risk caused by uncertainty in wind power, FE was analysed. The actual and forecast power of wind generations were studied to determine which factors had strong relationships with FE. After being analysed by principle component analysis, these factors were used to build an assessment model to estimate FE. According to the output of the assessment model, risk factor (RF) was defined to evaluate the risk‐level associated with wind power. Moreover, based on a grid including wind generation and fossil fuel‐fired power plants, a dispatch model was built to account for the cost of fossils fuels and RF. This dispatch model was studied to find strategies for decreasing the impact of wind power on the grid. The results from their research will increase the safety and reliability of power grids with uncertain levels of wind power and promote the widespread use of wind power.

Multi-step prediction of offshore wind power based on Transformer network and Huber loss

The increasing penetration of renewable energy, and wind power in particular, requires accurate short-term forecasting to ensure grid stability, reduce operational uncertainty, and facilitate large-scale integration of intermittent resources. This study evaluates Transformer-based architectures for wind power forecasting using hourly generation data from Spain (2020–2024). Time series were segmented into input windows of 12, 24, and 36 h, and multiple model configurations were systematically tested. For benchmarking, LSTM and GRU models were trained under identical protocols. The results show that the Transformer consistently outperformed recurrent baselines across all horizons. The best configuration, using a 36 h input sequence with moderate dimensionality and shallow depth, achieved an RMSE of 370.71 MW, MAE of 258.77 MW, and MAPE of 4.92%, reducing error by a significant margin compared to LSTM and GRU models, whose best performances reached RMSEs above 395 MW and MAPEs above 5.7%. Beyond predictive accuracy, attention maps revealed that the Transformer effectively captured short-term fluctuations while also attending to longer-range dependencies, offering a transparent mechanism for interpreting the contribution of historical information to forecasts. These findings demonstrate the superior performance of Transformer-based models in short-term wind power forecasting, underscoring their capacity to deliver more accurate and interpretable predictions that support the reliable integration of renewable energy into modern power systems.

According to the wind speed prediction appeared in data acquisition difficulties, so as to the poor forecast accuracy, this paper proposed the history data of near BP-ANN wind short-term forecast model, with emphasis on BP model of input layer and hidden layer parameters are estimated. In the certain scope, enumerated input layer and hidden layer parameters, and use a large number of data simulation, at the same time, SSE and MAE two index to evaluate the simulation results, obtained the suitable for wind speed forecasting multiple different parameters BP model. At the same time more than the BP model to forecast, found that the forecast accuracy is higher, show that different parameters of BP model are can be used to forecast and BP model could get same effect with different parameters.

Accurate prediction of wind power is of great significance to reduce the impact of large-scale wind power connection and improve the security and stability of power grid. A short-term wind power forecasting method based on VMD-KPCA and CFSBOA-BiLSTM is proposed. Firstly, the key environmental factors restricting the volatility of wind power are fully considered, and the variational mode decomposition method is used to decompose the wind power and meteorological factors. Secondly, the kernel principal component analysis method is used to reduce the dimension and construct the sub-sequence characteristic data set. Finally, the multi-strategy improved butterfly algorithm is used to optimize the BiLSTM network hyperparameters and establish the CFSBOA-BiLSTM prediction model. The simulation analysis uses the measured data of a wind farm in northwest China. The experimental results show that the combined forecasting model can fully mine the hidden information of the data and improve the accuracy of short-term wind power forecasting. Compared with the BiLSTM model, RMSE, MAE and MAPE decreased by 60.37%, 66.6% and 67.59%, respectively.

Accurate forecasting is the key factor in promoting wind power consumption and improving the stable operation of power systems. A short-term wind power forecasting (WPF) and uncertainty analysis method based on whale optimization algorithm (WOA), least squares support vector machine (LSSVM), and nonparametric kernel density estimation (NPKDE) was proposed in this paper. The advantages of WOA (fast convergence speed and high convergence accuracy) were used to optimize the penalty factor and kernel function width of the LSSVM model, and the calculation speed and forecasting accuracy of the LSSVM model were improved. The training sample set is classified according to the wind speed interval, and the WOA-LSSVM forecasting model is trained by subclass after classification to further improve the accuracy of short-term WPF. The NPKDE method is used to accurately calculate the probability density distribution characteristics of the forecasting error of wind power, and the confidence interval of the WPF is accurately calculated based on the probability density distribution characteristics. The calculation results show that the forecasting accuracy of the WOA-LSSVM model is higher than those of the LSSVM, long short-term memory (LSTM), and particle swarm optimization and least squares support vector machine (PSO-LSSVM) models, and the forecasting accuracy of the WOA-LSSVM model can be further improved after classifying the training sample set. The coverage of the confidence intervals in different time scales is higher than the corresponding confidence level, indicating that the NPKDE method can accurately describe the probability density distribution characteristics of the WPF errors.

With the large-scale wind power integration, the uncertainty of wind power poses a great threat to the safe and stable operation of the system. This paper proposes dynamic economic dispatch problem formulation in thermal power system incorporating stochastic wind and small-hydro (run-in-river) power, called thermal-wind-small hydropower system (TWSHS). Weibull and Gumbel probability density functions are used to calculate available wind and small-hydro power respectively. An improved differential evolution algorithm based on gradient descent information (DE-GD) is proposed to solve the dynamic economic dispatch (DED) problem considering uncertainty of wind power and small-hydro power, as well as complicated constraints in TWSHS. Based on the traditional differential evolution algorithm, the gradient information of the objective function is introduced after the mutation process to enrich the diversity of the population, thus increasing the possibility of convergence to the global optimization. Generation scheduling is simulated on a TWSHS with the proposed approach. Simulation results verify feasibility and effectiveness of the proposed method while considering various complex constraints in the thermal-windsmall hydropower system.

With the large-scale wind integration into power grid, the intermittent and stochastic nature of wind power possess a threat to the safety of power grid, and wind power prediction has become one of the most important solutions to the current grid connection problem. However, the prediction error in point prediction of wind power cannot be ignored, so it is necessary to make probability prediction of wind power and improve reliable information to the power grid dispatch department. In this paper, by combing with the wavelet decomposition technology and the long short-term memory (LSTM) network, an ultra short-term probability model is proposed. Firstly, the original time series are smoothed by wavelet decomposition technology, then the LSTM network prediction model of each sub-series sample is developed. The Gaussian distribution function of the prediction error is obtained by using the maximum likelihood estimation method, and finally the probability interval prediction of the future wind power in four hours could be realized. Finally, by using the data collected from a wind farm in northeast china, a numerical example is presented to illustrate the usefulness of the proposed method, which show that combining wavelet decomposition with deep learning method can improve the accuracy of prediction, improve the interval reliability of probability prediction, and enhance the generalization ability of the model.

This paper presents an adjustable interval optimization model for the energy and reserve clearance while treating the wind power variation and uncertainty. Instead of conventional predicted intervals (PIs), adjustable intervals (AIs) as subsets of PIs are proposed as more judicious choices for economically covering wind uncertainties. Ramp-capability reserve is modeled to ensure the existence of sufficient ramp capability needed to follow up wind power variation within AIs. However, realization of wind power beyond AIs, which is no longer compensable by ramp-capability reserve, would necessarily lead to either load shedding or wind spillage. In order to optimally determine AIs, apart from the energy and reserve procurement costs, the costs associated with load shedding and wind spillage are incorporated in the objective function. The confidence level theory is employed as well for tuning the robustness and conservatism of the final solution. Performance of the proposed method is examined on several case studies on an 8-bus and modified IEEE 118-bus test systems. Sensitivity analyses are conducted to specify the impacts of important parameters on the obtained solution. The results confirm the applicability and effectiveness of the proposed methodology.

Multistep short-term wind power forecasting model based on secondary decomposition, the kernel principal component analysis, an enhanced arithmetic optimization algorithm, and error correction

A new short-term wind power prediction methodology based on linear and nonlinear hybrid models

Data mining and wind power prediction: A literature review