Wind Speed Forecasting Based on Phase Space Reconstruction and a Novel Optimization Algorithm

Wind speed forecasting based on model selection, fuzzy cluster, and multi-objective algorithm and wind energy simulation by Betz's theory

Multi-step ahead wind speed forecasting approach coupling PSR, NNCT-based multi-model fusion and a new optimization algorithm

<p>Wind speed forecasting is very important for a lot of real-life applications, especially for controlling and monitoring of wind power plants. Owing to the non-linearity of wind speed time series, it is hard to improve the accuracy of runoff forecasting, especially several days ahead. In order to improve the forecasting performance, many forecasting models have been proposed. Recently, deep learning models have been paid great attention, since they excel the conventional machine learning models. The majority of existing deep learning models take the mean squared error (MSE) loss as the loss function for forecasting. MSE loss is linear. Consequently, it hinders further improvement of forecasting performance over nonlinear wind speed time series data.   <br> <br>In this work, we propose a new weighted MSE loss function for wind speed forecasting based on deep learning. As is well known, the training procedure is dominated by easy-training samples in applications. The domination will cause the ineffectiveness and inefficiency of computation. In the new weighted MSE loss function, loss weights of samples can be automatically reduced, according to the contribution of easy-training samples. Thus, the total loss mainly focuses on hard-training samples. To verify the new loss function, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) have been used as base models. <br> <br>A number of experiments have been carried out by using open wind speed time series data collected from China and Unites states to demonstrate the effectiveness of the new loss function with three popular models. The performances of the models have been evaluated through the statistical error measures, such as Mean Absolute Error (MAE). MAE of the proposed weighted MSE loss are at most 55% lower than traditional MSE loss. The experimental results indicate that the new weighted loss function can outperform the popular MSE loss function in wind speed forecasting. </p>

Accurate wind speed forecasting exerts a critical role in energy conversion and management of wind power. In term of this purpose, a hybrid model based on multi-stage principal component extraction, kernel extreme learning machine (KELM) and gated recurrent unit (GRU) network is developed in this paper, where the multi-stage principal component extraction combines complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), singular spectrum analysis (SSA) and phase space reconstruction (PSR). Firstly, CEEMDAN is employed to decompose the raw wind speed data into a sequence of intrinsic mode functions (IMFs) and a residual component. Then the principal components and residual components of all IMFs are captured by SSA. Meanwhile, all residual components obtained by CEEMDAN decomposition and SSA processing are added to form a new component. Subsequently, PSR is utilized to construct each forecasting component obtained by CEEMDAN-SSA into the input and output of training set and testing set for the prediction model. Later, KELM and GRU neural network are conducted to predict the high-frequency and low-frequency components, respectively. Eventually, the prediction values of each component are accumulated to acquire the final prediction result. To evaluate the performance of the proposed model, four datasets from Sotavento Galicia wind farm are adopted to conduct experimental research. The experimental results manifest that the proposed model achieves higher accuracy of multi-step prediction than other comparative models.

Composite quantile regression extreme learning machine with feature selection for short-term wind speed forecasting: A new approach

A convolutional Transformer-based truncated Gaussian density network with data denoising for wind speed forecasting

A new compound structure combining DAWNN with modified water cycle algorithm-based synchronous optimization for wind speed forecasting

Multi-step wind speed forecasting model based on wavelet matching analysis and hybrid optimization framework

As one of the effective renewable energy sources, wind energy has received attention because it is sustainable energy. Accurate wind speed forecasting can pave the way to the goal of sustainable development. However, current methods ignore the temporal characteristics of wind speed, which leads to inaccurate forecasting results. In this paper, we propose a novel SSA-CCN-ATT model to forecast the wind speed. Specifically, singular spectrum analysis (SSA) is first applied to decompose the original wind speed into several sub-signals. Secondly, we build a new deep learning CNN-ATT model that combines causal convolutional network (CNN) and attention mechanism (ATT). The causal convolutional network is used to extract the information in the wind speed time series. After that, the attention mechanism is employed to focus on the important information. Finally, a fully connected neural network layer is employed to get wind speed forecasting results. Three experiments on four datasets show that the proposed model performs better than other comparative models. Compared with different comparative models, the maximum improvement percentages of MAPE reaches up to 26.279%, and the minimum is 5.7210%. Moreover, a wind energy conversion curve was established by simulating historical wind speed data.

BackgroundThe planning and control of wind power production rely heavily on short-term wind speed forecasting. Due to the non-linearity and non-stationarity of wind, it is difficult to carry out accurate modeling and prediction through traditional wind speed forecasting models.MethodsIn the paper, we combine empirical mode decomposition (EMD), feature selection (FS), support vector regression (SVR) and cross-validated lasso (LassoCV) to develop a new wind speed forecasting model, aiming to improve the prediction performance of wind speed. EMD is used to extract the intrinsic mode functions (IMFs) from the original wind speed time series to eliminate the non-stationarity in the time series. FS and SVR are combined to predict the high-frequency IMF obtained by EMD. LassoCV is used to complete the prediction of low-frequency IMF and trend.ResultsData collected from two wind stations in Michigan, USA are adopted to test the proposed combined model. Experimental results show that in multi-step wind speed forecasting, compared with the classic individual and traditional EMD-based combined models, the proposed model has better prediction performance.ConclusionsThrough the proposed combined model, the wind speed forecast can be effectively improved.

A new hybrid model for wind speed forecasting combining long short-term memory neural network, decomposition methods and grey wolf optimizer

Improving the accuracy of wind speed forecast can increase wind power generation and better achieve wind energy grid connection. Therefore, a two-stage wind speed prediction model based on Ensemble Empirical Modal Decomposition (EEMD) and the combination prediction of Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), eXtreme Gradient Boosting (XGBOOST), Gate Recurrent Unit (GRU), Temporal Convolutional Network (TCN) is proposed. First, the original wind speed series is separated into Intrinsic Mode Functions (IMFs) using EEMD. Then, RNN, LSTM, XGBOOST, GRU, TCN multiple prediction models are established to learn features from each subsequence and superimpose the prediction results of subsequences. Finally, Particle Swarm Optimization (PSO) is applied to the results of multiple prediction models to assign weights, combined with weight superimposing sequences to achieve higher accuracy and more robust wind speed prediction. Simulation analysis using data from St. Thomas, Virgin Islands wind measurement station to validate the validity of the combined prediction model. The experimental simulation results show that the model proposed in this paper has a good result on increasing wind speed prediction accuracy.

A novel hybrid machine learning model for short-term wind speed prediction in inner Mongolia, China

A high precise wind speed forecasting method is one of current wind power research hotspots. This paper presented a combined wind speed forecasting model based on support vector machine (SVM) optimized by particle swarm optimization (PSO) using historical data of wind speed at the site. The model took the results of back propagation neural network (BPNN), radial basis function neural network (RBFNN), genetic neural network (GNN) and wavelet neural network (WNN) as the inputs, and adopted the actual wind speed as the output. Meanwhile, particle swarm optimization was used to optimize model parameters. Apply this model in hourly prediction of wind speed using historical data from a wind farm in Shanxi Province. It is observed that its prediction accuracy was not only higher than that of any of its single network but higher than traditional linear combined forecasting model and neural network combined forecasting model.

The global impacts of climate change have become increasingly pronounced in recent years due to the rise in greenhouse gas emissions from fossil fuels. This trend threatens water resources, ecological balance, and could lead to desertification and drought. To address these challenges, reducing fossil fuel consumption and embracing renewable energy sources is crucial. Among these, wind energy stands out as a clean and renewable source garnering more attention each day. However, the variable and unpredictable nature of wind speed presents a challenge to integrating wind energy into the electricity grid. Accurate wind speed forecasting is essential to overcome these obstacles and optimize wind energy usage. This study focuses on developing a robust wind speed forecasting model capable of handling non-linear dynamics to minimize losses and improve wind energy efficiency. Wind speed data from the Bandırma meteorological station in the Marmara region of Turkey, known for its wind energy potential, was decomposed into intrinsic mode functions (IMFs) using robust empirical mode decomposition (REMD). The extracted IMFs were then fed into a long short-term memory (LSTM) architecture whose parameters were estimated using the African vultures optimization (AVO) algorithm based on tent chaotic mapping. This approach aimed to build a highly accurate wind speed forecasting model. The performance of the proposed optimization algorithm in improving the model parameters was compared with that of the chaotic particle swarm optimization (CPSO) algorithm. Finally, the study highlights the potential of utilizing advanced optimization techniques and deep learning models to improve wind speed forecasting, ultimately contributing to more efficient and sustainable wind energy generation. This robust hybrid model represents a significant step forward in wind energy research and its practical applications.