Wind Power Prediction System Research Articles

Ensemble optimization approach based on hybrid mode decomposition and intelligent technology for wind power prediction system

Wind power is a critical source of renewable energy, but the variability of wind speed can impact the efficiency and reliability of wind power generation. To improve the accuracy of wind speed prediction, this paper proposes a novel system that combines deep learning and decomposition algorithms based on ensemble optimization methods. Firstly, the paper proposes an innovative hybrid modal decomposition (HMD) method that extracts accurate features from wind speed data using Singular Value Decomposition (SVD), modal number selection method, and Variational Mode Decomposition (VMD). Secondly, the variable screening method identifies relevant factors affecting wind speed as fixed inputs. Then, the stationary test method is used to identify the sequences of different features in the mode and establish different prediction models: a bidirectional long-term short-time neural network model (Bi-LSTM) model optimized by artificial hummingbird algorithm (AHA) is used for non-stationary sequences; an ARIMA model is used for stationary sequences. Data from Sotavento and Changma wind farms are used to test this forecasting system. Simulation experiments are carried out from multiple dimensions. The results show that all error indicators are significantly improved, and the proposed prediction system is better than other comparative forecasting schemes.

Research on the Control Strategy of Micro Wind-Hydrogen Coupled System Based on Wind Power Prediction and Hydrogen Storage System Charging/Discharging Regulation

Research on the Control Strategy of Micro Wind-Hydrogen Coupled System Based on Wind Power Prediction and Hydrogen Storage System Charging/Discharging Regulation

Advanced Machine Learning Techniques for Accurate Very-Short-Term Wind Power Forecasting in Wind Energy Systems Using Historical Data Analysis

Accurate wind power forecasting plays a crucial role in the planning of unit commitments, maintenance scheduling, and maximizing profits for power traders. Uncertainty and changes in wind speeds pose challenges to the integration of wind power into the power system. Therefore, the reliable prediction of wind power output is a complex task with significant implications for the efficient operation of electricity grids. Developing effective and precise wind power prediction systems is essential for the cost-efficient operation and maintenance of modern wind turbines. This article focuses on the development of a very-short-term forecasting model using machine learning algorithms. The forecasting model is evaluated using LightGBM, random forest, CatBoost, and XGBoost machine learning algorithms with 16 selected parameters from the wind energy system. The performance of the machine learning-based wind energy forecasting is assessed using metrics such as mean absolute error (MAE), mean-squared error (MSE), root-mean-squared error (RMSE), and R-squared. The results indicate that the random forest algorithm performs well during training, while the CatBoost algorithm demonstrates superior performance, with an RMSE of 13.84 for the test set, as determined by 10-fold cross-validation.

Power prediction of a wind farm cluster based on spatiotemporal correlations

Accurate power prediction of wind farm clusters is important for safe and economic operation of power systems with high wind power penetration. Current superposition and statistical scaling methods used in wind power prediction systems do not fully consider the relationships among wind farms in a cluster, thereby leading to insufficient power prediction accuracies. To improve the power prediction accuracy of wind farm clusters, a new method based on spatiotemporal correlations is proposed herein. First, three correlation coefficients are used to represent spatiotemporal correlation characteristics of wind farms in a wind cluster. The Shapley value method is used to weight these coefficients, and a standard wind farm is found by combining the nominal capacities of the wind farms. Then, considering the spatiotemporal factors that affect wind power generation, a characteristic matrix of the wind farm cluster is constructed, and the key characteristics are extracted using a convolutional neural network (CNN). Considering the time series characteristics of wind power generation, a long and short term memory (LSTM) neural network is used to establish the mapping relationship between key characteristics and power generation, and power prediction of a wind farm cluster is performed. Finally, by utilizing the actual operating data of wind farm clusters in North China as an example, feasibility and effectiveness of the proposed method are verified. The proposed system provides a new high-precision method for future wind farm cluster power predictions.

Performance Analysis and Optimization Research of Wind Power Forecasting System

The power fluctuation of wind farm will affect the operation of regional power grid, mainly due to the power fluctuation caused by voltage fluctuation. In order to submit information to power dispatching department as soon as possible, it is convenient for system dispatching. It is necessary to research and develop wind power forecasting technology, especially ultra-short term wind power forecasting technology. At present, the power forecasting system of the dispatching side and some wind farms in China has been widely used. However, the system has some shortcomings in stable operation. Combined with the actual production experience of the existing wind power prediction system, analyze the performance of the existing wind power prediction system, find out the problems that affect the smooth operation of the system, and refer to domestic and foreign data to provide targeted information for each problem. Finally, some constructive optimization Suggestions and a set of optimization scheme are given. In addition, according to the final proposed solution, this paper also developed an optimization model for wind power prediction system, and compared the changes in system performance before and after optimization to verify whether the optimization plan is correct.

WSBP/NWBP small-world neural network and its performance for wind power forecasting

A small-world neural network has stronger generalization ability with high transfer efficiency than that of the regular neural networks. This paper presents two novel small-world neural networks, the Watts-Strogatz small-world based on a BP neural network (WSBP) and a Newman-Watts small-world neural network based on a BP neural network (NWBP), related to previous research of complex networks. The algorithms are developed separately by adopting WS and NW small-world networks as their topological structures, and their derivation and convergence criterion are progressively discussed. After that, the proposed models are subsequently tested by two typical nonlinear functions which confirm their significant improvement over the regular BP networks and other algorithms. Finally, a wind power prediction system is advanced to verify their generalization abilities, and show that the models are practically feasible and effective with improved accuracy and acceptable forecasting errors caused by wind fluctuation and randomness with a time scale up to 24 h.

Short-Term Forecasting of the Output Power of a Building-Integrated Photovoltaic System Using a Metaheuristic Approach

The rapidly increasing use of renewable energy resources in power generation systems in recent years has accentuated the need to find an optimum and efficient scheme for forecasting meteorological parameters, such as solar radiation, temperature, wind speed, and sun exposure. Integrating wind power prediction systems into electrical grids has witnessed a powerful economic impact, along with the supply and demand balance of the power generation scheme. Academic interest in formulating accurate forecasting models of the energy yields of solar energy systems has significantly increased around the world. This significant rise has contributed to the increase in the share of solar power, which is evident from the power grids set up in Germany (5 GW) and Bavaria. The Spanish government has also taken initiative measures to develop the use of renewable energy, by providing incentives for the accurate day-ahead forecasting. Forecasting solar power outputs aids the critical components of the energy market, such as the management, scheduling, and decision making related to the distribution of the generated power. In the current study, a mathematical forecasting model, optimized using differential evolution and the particle swarm optimization (DEPSO) technique utilized for the short-term photovoltaic (PV) power output forecasting of the PV system located at Deakin University (Victoria, Australia), is proposed. A hybrid self-energized datalogging system is utilized in this setup to monitor the PV data along with the local environmental parameters used in the proposed forecasting model. A comparison study is carried out evaluating the standard particle swarm optimization (PSO) and differential evolution (DE), with the proposed DEPSO under three different time horizons (1-h, 2-h, and 4-h). Results of the 1-h time horizon shows that the root mean square error (RMSE), mean relative error (MRE), mean absolute error (MAE), mean bias error (MBE), weekly mean error (WME), and variance of the prediction errors (VAR) of the DEPSO based forecasting is 4.4%, 3.1%, 0.03, −1.63, 0.16, and 0.01, respectively. Results demonstrate that the proposed DEPSO approach is more efficient and accurate compared with the PSO and DE.

Wind power prediction using deep neural network based meta regression and transfer learning

An innovative short term wind power prediction system is proposed which exploits the learning ability of deep neural network based ensemble technique and the concept of transfer learning. In the proposed DNN-MRT scheme, deep auto-encoders act as base-regressors, whereas Deep Belief Network is used as a meta-regressor. Employing the concept of ensemble learning facilitates robust and collective decision on test data, whereas deep base and meta-regressors ultimately enhance the performance of the proposed DNN-MRT approach. The concept of transfer learning not only saves time required during training of a base-regressor on each individual wind farm dataset from scratch but also stipulates good weight initialization points for each of the wind farm for training. The effectiveness of the proposed, DNN-MRT technique is expressed by comparing statistical performance measures in terms of root mean squared error (RMSE), mean absolute error (MAE), and standard deviation error (SDE) with other existing techniques.

The Development of the Wind Power Prediction System using Ordinary Kriging

As wind power market is increasing, the interest of the system connection technology is also increasing. In order to integrate wind power into electrical grid, it is essential to predict wind power outputs as wind power generator’s outputs is influenced by space-time causes such as wind speed, wind direction, temperature, and etc. Many of wind power predictions use the past data based on time-series. As wind power is influenced by space and time. Therefore, we need to predict wind power outputs using spatial modeling based on geographic information such as longitude and latitude. In this paper, we propose a wind power prediction system based on Ordinary Kriging. The empirical data, Jeju wind farm’s 2014 hourly power output data, is considered to verify the proposed prediction model based on OK.

A hybrid neuro-fuzzy power prediction system for wind energy generation

Abstract Wind energy generation is expected to increase in future electric grids. The generated wind power has an intermittent nature which may affect power system stability and increase the risk of blackouts. Therefore, a prediction system for wind power generation is essential for optimum operation of a power system with a significant share of wind energy conversion systems. In this paper, a hybrid neuro-fuzzy wind power prediction system is proposed. A wireless sensor network (WSN) is used to measure and transmit the required parameters for the prediction model at the operator centre. Those parameters are the major factors affecting wind farm output power, namely air temperature, wind speed, air density and air pressure. Considering all these factors will increase the prediction accuracy of the proposed model. The proposed prediction model is designed and tested using fuzzy rules with adaptive network. To decide the optimal number of fuzzy rules, the clustering of the data using modified Fuzzy C-Means (FCM) is used to implement hybrid optimization method. The prediction model is tested using four subsets of data divided into four seasons of year. The proposed prediction model is implemented using Matlab. Analysis of results shows that the proposed model has good prediction accuracy and provides a useful qualitative description of the prediction system.

Machine Learning based short term wind power prediction using a hybrid learning model

Depletion of conventional resources has led to the exploration of renewable energy resources. In this regard, wind power is taking significant importance, worldwide. However, to acquire consistent power generation from wind, the expected wind power is required in advance. Consequently, various prediction models have been reported for wind power prediction. However, we observe that Support Vector Regression (SVR), and specially, a hybrid learning model based on SVR offer better performance and generalization compared to multiple linear regression (MLR) and is thus quite suitable for the development of short-term wind power prediction system. To this end, a new methodology ML-STWP namely Machine Learning based Short Term Wind Power Prediction is proposed for short-term wind power prediction. This approach utilizes a combination of machine learning (ML) techniques for feature selection and regression. The proposed methodology is thus a hybrid ML model, which makes use of feature selection through irrelevancy and redundancy filters, and then employs SVR for auxiliary prediction. Finally, the wind power is predicted using enhanced particle swarm optimization and a hybrid neural network.The wind power dataset on which the model is tuned and tested consists of real-time daily values of wind speed, relative humidity, temperature, and wind power. The obtained results demonstrate that the proposed prediction model performs better as compared to the existing methods and demonstrates the efficacy of the proposed intelligent system in accurately predicting wind power on daily basis.

Wind power prediction system for wind farm based on auto regressive statistical model and physical model

Extracting energy from renewable sources such as wind energy is widely investigated in the past decades to mitigate the global energy crisis and environmental pollution. For a wind farm that converts wind energy into electricity power, a real-time prediction system of the output power is significant. In this paper, a prediction system is developed with a method of combining statistical model and physical model. In this system, the inlet condition of the wind farm is forecasted by the auto regressive model. The flow field is computed by the Reynolds average Navier-stokes simulation in the computational fluid dynamics model. The wake flow is calculated by the particle model, which can be used over complex terrain. Taking also the terrain condition, the property of turbines and wake flow model into account, the output power of the wind farm can be further predicted. The proposed prediction system is tested by the data from Wattle Point Wind Farm in Australia. Through the data post-processing, the error of the mean daily output power is less than 5%. The proposed system is effective for power output prediction of wind farm.

Research on Wind Power Prediction Based on Distributed Wind Farms Siting

This paper proposes a new method to predict the wind power of the distributed wind farms, considering the models such as the roughness model, the orography model. In order to predict the wind power accurately, this method calculates the loss of the wind speed directly, caused by the roughness model and the orography model. At the same time, this paper proposed the structure of the wind power prediction system, which provides the reference for the prediction of the wind power of distributed wind farms.

Algorithm and Realization of Wind Power Prediction System

The application of wind power prediction system (WPPS) contributes to security economic dispatching of power grid and stable operation of wind farm. This paper established short-term prediction model based on BP neural network and ultrashort-term prediction model based on improved time-series algorithm according to Xichang Wind Farm Phase I Project. A new probability model using two consecutive power points before prediction time was built to improve the traditional time-series algorithm. The system framework was designed. C# Language and SQL Server 2008 were taken to develop the system on the Microsoft .net platform. The WPPS uses distributed architecture, realizing seamless connection with the energy management system (EMS) of Xichang dispatching department.

Meteorological Data Acquisition System Based on Wind Power Prediction Methods and Research

Wind energy as a clean and can continue to use the new energy is widely used in central and western regions of China. Wind power has a strong volatility, when the large capacity of wind power connected to the grid, this volatility has many negative effects on the safe and stable operation of the grid. Therefore, the wind power prediction system has become the most wind farm necessary security early warning systems. There is an important part of the system that is called the wind power meteorological data acquisition system and its function is to provide for the system the original meteorological data, meteorological data will become an important basis for the prediction function. This article will describe the developed by the Changchun Meteorological Instrument Research Institute, "AMS-II-E wind electric power meteorological data acquisition system", the composition and the core processing algorithms. The system has been equipped with 15 wind power plants in four provinces in China (Inner Mongolia, Jilin, Liaoning, Jiangsu).

Real-time Wind Power Prediction System Based on Smart-Grid in Jeju, Korea

Wind power prediction information is necessary for electric power system operation and electricity power market which is allowed to participate the wind power. In this study, a real-time wind power prediction system is designed for the real application for the electric power system of Smart-Grid Test Bed in Jeju island, South Korea. This study is on the first system for practicable application of wind power to the electric power system operation in South Korea. This is composed of the meteorological forecasting module, calculation module of wind power output and HMI visualization system. The final output data from this system is short-term (6hr ahead) and mid-term (48hr ahead) wind power prediction values. These values are produced by using physical and statistical model. The purpose of this study is to further improve the accuracy of this prediction system and to develop practical system for power system operation and energy market in Smart-Grid.

A Wind Power Forecasting System to Optimize Grid Integration

Wind power forecasting can enhance the value of wind energy by improving the reliability of integrating this variable resource and improving the economic feasibility. The National Center for Atmospheric Research (NCAR) has collaborated with Xcel Energy to develop a multifaceted wind power prediction system. Both the day-ahead forecast that is used in trading and the short-term forecast are critical to economic decision making. This wind power forecasting system includes high resolution and ensemble modeling capabilities, data assimilation, now-casting, and statistical postprocessing technologies. The system utilizes publicly available model data and observations as well as wind forecasts produced from an NCAR-developed deterministic mesoscale wind forecast model with real-time four-dimensional data assimilation and a 30-member model ensemble system, which is calibrated using an Analogue Ensemble Kalman Filter and Quantile Regression. The model forecast data are combined using NCAR's Dynamic Integrated Forecast System (DICast). This system has substantially improved Xcel's overall ability to incorporate wind energy into their power mix.

A model predictive control approach to the problem of wind power smoothing with controlled battery storage

The aim of this study is to design a controller, based on model predictive control (MPC), to smooth the wind power output, which is generated from a wind farm, and subject to a variety of constraints on the system model. In order to employ the model predictive controller, we propose a wind power prediction system, which is used by the controller within its predictive optimization. The proposed controller is capable of smoothing wind power by utilizing inputs from our prediction system, and optimizes the maximum ramp rate requirement and also the state of the charge of the battery under practical constraints. The proposed prediction model is capable of predicting the wind power several steps ahead which is used in the optimization part of the controller. We illustrate the effectiveness of the proposed controller with a simulation example, employing real wind farm data under a variety of hard constraints.

Predicting the Wind

Due to increasing wind power penetration, the need for and usage of wind power prediction systems have increased. At the same time, much research has been done in this field, which has led to a significant increase in the prediction accuracy recently. With many ongoing research programs in the field of numerical weather prediction (NWP), as well as in the power output prediction models (transforming wind speed into electrical power output), one can expect further improvements in the future. For the time being, three measures are taken as best practices to reduce prediction errors: Combinations of different models can be done with power output forecast models as well as with NWP models (multimodel and multischeme approaches). Reductions in RMSE of up to 20% were shown with intelligent combinations. As expected, a shorter forecast horizon leads to lower prediction errors. However, the organization of the electricity market as well as the conventional generation pool has a large influence on the needed forecast horizon. The forecast error depends on the number of wind turbines and wind farms and their geographical spread. In Germany, typical forecast errors for representative wind farm forecasts are 10-15% RMSE of installed power, while the error for the control areas calculated from these representative wind farms is typically 6-7% and that for the whole of Germany only 5-6%. Whenever possible, aggregating wind power over a large area should be performed as it leads to significant reduction of forecast errors as well as short-term fluctuations. a large area should be performed as it leads to significant reduction of forecast errors as well as short-term fluctuations.

ViLab: A Virtual Laboratory for Collaborative Research on Wind Power Forecasting

The consortium of the European Coordination Action ‘Prediction of Waves, Wakes and Offshore Wind’ (POW'WOW) has established a Virtual Laboratory (ViLab) for the development and operational use of wind power prediction systems. The objectives are to stimulate research in this field, to tighten the collaboration between forecasters and forecast users, and to follow and communicate the state of the art in short term prediction of wind generation. The present note clarifies the benefits for the participants to the ViLab. Also, it describes the methodology employed and some practical issues related to data and confidentiality.