Online probability density prediction of wind power considering virtual and real concept drift detection

To support high-level wind energy utilization, wind power prediction has become a more and more attractive topic. To improve prediction accuracy and flexibility, joint point-interval prediction of wind power via a stepwise procedure is studied in this paper. Firstly, time-information-granularity (TIG) is defined for ultra-short-term wind speed prediction. Hidden features of wind speed in TIGs are extracted via principal component analysis (PCA) and classified via adaptive affinity propagation (ADAP) clustering. Then, Gaussian process regression (GPR) with joint point-interval estimation ability is adopted for stepwise prediction of the wind power, including wind speed prediction and wind turbine power curve (WTPC) modeling. Considering the sequential uncertainties of stepwise prediction, theoretical support for an uncertainty enlargement effect is deduced. Uncertainties’ transmission from single-step or receding multi-step wind speed prediction to wind power prediction is explained in detail. After that, normalized indexes for point-interval estimation performance are presented for GPR parameters’ optimization via a hybrid particle swarm optimization-differential evolution (PSO-DE) algorithm. K-fold cross validation (K-CV) is used to test the model stability. Moreover, due to the timeliness of data-driven GPR models, an evolutionary prediction mechanism via sliding time window is proposed to guarantee the required accuracy. Finally, measured data from a wind farm in northern China are acquired for validation. From the simulation results, several conclusions can be drawn: the multi-model structure has insignificant advantages for wind speed prediction via GPR; joint point-interval prediction of wind power is realizable and very reasonable; uncertainty enlargement exists for stepwise prediction of wind power while it is more significant after receding multi-step prediction of wind speed; a reasonable quantification mechanism for uncertainty is revealed and validated.

Predictability of global offshore wind and wave power

A comprehensive evaluation of machine learning and deep learning algorithms for wind speed and power prediction

For power grid with large-scale wind energy, the short-term wind power prediction is important to the grid’s scheduling and stable operating. The overall short-term forecast for wind power connected to the grid relies on the wind velocity and historical power data. Firstly, K-means clustering is introduced to model the power grid, so that the relationship between wind velocity and power can be perfectly described. Considering that there are multiple factors contributing to the prediction of wind velocity and power, we use real data of 15 wind generating set to obtain dependable weight factors of all those dimensions. With the support of mass data, the prediction of power is proved by several measurements (ME, MRE, MAE, RMSE) to be accurate.

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.

The energy turnaround in Europe increases the importance of wind speed as well as power predictions. This article provides a review of different forecasting approaches for wind speed and wind power. Moreover, recent time series models are discussed in more detail. The focus of this article are accurate short-and medium-term wind speed and power predictions. Finally, recent wind speed and power out-of-sample results are discussed and the problem of asymmetric loss is covered within this article. Precisely, over-and underestimation of wind power predictions have to be weighted in a different way. Therefore, it is reasonable to introduce an asymmetric accuracy measure. To cover the impact of asymmetric loss on wind speed and power predictions, a small example is presented which covers forecasts up to 24 hours.

Wind power prediction holds significant value for the stability of the electrical grid when wind power is connected to the grid. Using neural networks for wind power prediction may have some limitations, such as slow speed and low accuracy. This paper proposes to enhance the power prediction accuracy and speed by optimizing the neural network through health assessment wind turbines. Firstly, based on wind turbine actual operating data, a health assessment is conducted to obtain a health matrix of wind turbine. Then, by calculating the weights of the matrix, the power prediction strategy of the network is optimized. Following that, matrix approximation hyperparameters are utilized to expedite the optimization process. Finally, some tests are conducted on neural network power prediction, act as optimized back propagation (BP) neural network and whale swarm algorithm–support vector regression (WSA-SVR) neural networks are employed for wind power prediction. Results show noticeable optimization: after optimizing the BP network, power prediction accuracy increased by about 40%, and prediction speed rose by about 20%; after optimizing the WSA-SVR network, power prediction accuracy improved by 10%, and prediction speed surged by about 45%. Further analysis shows that this method can improve the accuracy and speed of most neural network wind power prediction algorithms.

Much recent research in wind power forecasting has been focused on predicting large, sudden changes in wind power output, called wind ramps. However, the current wind power forecasting methodology, which combines Numerical Weather Prediction (NWP) models and machine learning methods, has limitations in addressing the prediction accuracy in different weather conditions. Based on existing wind power forecasting methods, this paper proposes a separate power forecasting method that addresses different weather regimes. A framework for wind power predictions is first built, which uses wind forecasts from a Weather Research and Forecasting Model (WRF) model as input and converts the input into future wind power generation using a Multi-Layer Perceptron Neural Network (MLP-NN). Specific power prediction systems are then built for each subset of data, which is divided according to the hourly wind speed changes, the synoptic atmospheric circulation types, and the K-means clustering of meteorological variables. Wind ramp events were then identified based on predicted power series. Experiments showed that dynamic weather made wind power and ramp prediction difficult and thus forecasts had lower accuracy whereas stable weather allowed forecasts with higher accuracy, evidencing that the proposed strategy can provide the information of weather types to electrical grid operators, together with the expected corresponding forecast accuracy under that weather pattern.

With the increase in installed capacity of wind turbines, the stable operation of the power system has been affected. Accurate prediction of wind power is an important condition to ensure the healthy development of the wind power industry and the safe operation of the power grid. This paper first introduces the current status of wind power prediction methods under normal weather, and introduces them in detail from three aspects: physical model method, statistical prediction method and combined prediction method. Then, from the perspectives of numerical simulation analysis and statistical prediction methods, the wind power prediction method under icy conditions is introduced, and the problems faced by the existing methods are pointed out. Then, the accurate prediction of wind power under icing weather is considered, and two possible research directions for wind power prediction under icy weather are proposed: a statistical prediction method for classifying and clustering wind turbines according to microtopography, combining large-scale meteorological parameters with small-scale meteorological parameter correlation models and using machine learning for cluster power prediction, and a power prediction model converted from the power prediction model during normal operation of the wind turbine to the power prediction model during icing. Finally, the research on wind power prediction under ice-covered weather is summarized, and further research in this area is prospected.

In the future high proportion of renewable energy grid-connected scenario, a large number of centrally developed wind farms and photovoltaic power stations will be integrated into the power system. There is a certain spatial and temporal correlation between wind and solar resources, which can provide additional information to help improve the power prediction accuracy. However, most of the existing wind and solar power prediction methods are focus on a single energy form, a single station or a single unit, which cannot fully and effectively reflect the space-time correlation between wind and solar resources. In addition, a single-field power prediction is no longer well enough to meet the needs of grid dispatching agencies. On the one hand, the power system as a whole, the dispatchers are more concerned about the total amount of uncertainty in the wind and solar power prediction; on the other hand, in order to make reasonable dispatch to the grid and avoid some off-grid events, the cluster power prediction is needed. Therefore, this paper proposes a joint forecasting method of regional wind and solar power. With NWP wind speed, irradiance and temperature data of several wind farms and photovoltaic power stations as input and measured power data as prediction target, based on the attention neural network algorithm, constructs a joint forecasting model which can reflect the spatial-temporal correlation of regional wind and solar resources. The proposed method is verified with the data of 8 wind farms and 7 photovoltaic power stations. The analysis results show that the proposed model can not only improve the power prediction accuracy, but also obtain the power prediction results of each target stations at the same time, which reduces the workload and has certain engineering application value.

Short-term Wind Power Prediction Based on Particle Filter and Radial Basis Function Neural Network

A frequency control approach based on wind power and load power prediction information is proposed for wind-diesel-battery hybrid power system (WDBHPS). To maintain the frequency stability by wind power and diesel generation as much as possible, a fuzzy control theory based wind and diesel power control module is designed according to wind power and load prediction information. To compensate frequency fluctuation in real time and enhance system disturbance rejection ability, a battery energy storage system real-time control module is designed based on ADRC (active disturbance rejection control). The simulation experiment results demonstrate that the proposed approach has a better disturbance rejection ability and frequency control performance compared with the traditional droop control approach.

With the growing penetration of wind power in power systems, more accurate prediction of wind speed and wind power is required for real-time scheduling and operation. In this paper, a novel forecast model for short-term prediction of wind speed and wind power is proposed, which is based on singular spectrum analysis (SSA) and locality-sensitive hashing (LSH). To deal with the impact of high volatility of the original time series, SSA is applied to decompose it into two components: the mean trend, which represents the mean tendency of the original time series, and the fluctuation component, which reveals the stochastic characteristics. Both components are reconstructed in a phase space to obtain mean trend segments and fluctuation component segments. After that, LSH is utilized to select similar segments of the mean trend segments, which are then employed in local forecasting, so that the accuracy and efficiency of prediction can be enhanced. Finally, support vector regression is adopted for prediction, where the training input is the synthesis of the similar mean trend segments and the corresponding fluctuation component segments. Simulation studies are conducted on wind speed and wind power time series from four databases, and the final results demonstrate that the proposed model is more accurate and stable in comparison with other models.

Accurate prediction of wind speed and wind power is of great significance to the operation, planning, dispatching and control of power system. In order to make full use of the effective information provided by SCADA system and NWP to further improve the prediction accuracy of wind speed and wind power. A short-term wind speed and wind power prediction method based on meteorological correction model is proposed in this paper. Firstly, the meteorological model based on matrix completion algorithm is established to modify the meteorological data. Secondly, the network is trained with the data of meteorological model modification as input and the actual power of fan as output, and the prediction model based on LSTM network is established. Finally, the short-term prediction of wind speed and wind power is completed. The measured data from a wind farm is used for verification. The research results show that the information in multiple data sources can be well used in the proposed method to complete the prediction of wind speed and wind power. And in the future, the waste of wind resources can be effectively reduced, so as to realize the economic and stable operation of the power grid.

This research paper presents an advanced approach to enhance the short-term wind power prediction based on artificial intelligence techniques. A high-quality wind power prediction is essential for power system planning, operation, and control. Thus, a new novel approach has been developed to improve the quality and reliability of the calculated results by integrating advanced time series processing method and the extreme learning machine technique. Moreover, historical records are utilised from numerical weather information and multiple observations points close to real wind farm sites within Australia regions. The wind speed is assessed by using the developed model in the first stage, and then the wind power and capacity factor is calculated using wind power–speed curve for each observation site. Artificial neural network, fuzzy logic (adaptive neuro-fuzzy inference system), and support vector machine models are used for model verifications, validations, and practical applications. The developed model is tested using real wind measurements by Bureau of Meteorology, 15 selected weather stations corresponded to the locations of nearby real wind farm sites in Australia. The demonstrated results and performance indicators, e.g. root mean square error and mean absolute error are compared with Khalid, persistence, and Grey predictor models for validations and verifications reasons. As the potential gains over other techniques, the proposed model has found more efficient and superior for wind power estimation and prediction than other developed conventional methods and models, which in turn improves the power system performance, and reduces the economic impacts.