Prediction Of Wind Power Generation Research Articles

Short-Term Wind Power Prediction Based on Encoder-Decoder Network and Multi-Point Focused Linear Attention Mechanism.

Wind energy is a clean energy source that is characterised by significant uncertainty. The electricity generated from wind power also exhibits strong unpredictability, which when integrated can have a substantial impact on the security of the power grid. In the context of integrating wind power into the grid, accurate prediction of wind power generation is crucial in order to minimise damage to the grid system. This paper proposes a novel composite model (MLL-MPFLA) that combines a multilayer perceptron (MLP) and an LSTM-based encoder-decoder network for short-term prediction of wind power generation. In this model, the MLP first extracts multidimensional features from wind power data. Subsequently, an LSTM-based encoder-decoder network explores the temporal characteristics of the data in depth, combining multidimensional features and temporal features for effective prediction. During decoding, an improved focused linear attention mechanism called multi-point focused linear attention is employed. This mechanism enhances prediction accuracy by weighting predictions from different subspaces. A comparative analysis against the MLP, LSTM, LSTM-Attention-LSTM, LSTM-Self_Attention-LSTM, and CNN-LSTM-Attention models demonstrates that the proposed MLL-MPFLA model outperforms the others in terms of MAE, RMSE, MAPE, and R2, thereby validating its predictive performance.

Enhancing wind power generation prediction using relevance assessment-based transfer learning

Accurate wind power generation forecasting can help build a reliable grid; however, the limited dataset makes accurate forecasting results a challenging work. This study introduces a relevant assessment-based transfer learning architecture to solve this problem. Linear fuzzy neighborhood mutual information is adopted to assess the relevance of the source domain selection. A convolutional structure with long-term memory architecture is designed as the deep learning model. The pre-trained model is transferred to the other wind turbines by calculating the linear fuzzy neighborhood mutual information. The proposed model avoids the lack of a dataset from analogous turbines. The simulation results indicate the proposed model surpasses the popular models in forecasting accuracy and exhibits superior time efficiency compared to popular deep-learning approaches.

Medium and Long Term Wind Power Generation Forecasting Method Based on Multi-model Fusion

Abstract Accurate, long-term wind power forecasting is crucial for effective power grid operation planning. It can enhance the stability and security of the power system. We propose a combined medium and long-term wind power generation prediction method based on multi-model fusion to study forecasting. By analyzing the connection between meteorological data and wind power generation, we identify the pivotal factors that impact wind power output and determine the best input data scheme. Based on the predictive outcomes, we select the suitable core predictive sub-models and use the particle swarm optimization algorithm to achieve the dynamic optimization of the weight for each sub-model. Afterwards, we allocate the optimized weights to each sub-model and establish a fused multi-model advantage-based combined predictive platform for wind power generation for the medium and long term. The wind power forecast is enhanced through the dynamic weighted combination forecasting technique, which significantly enhances forecast precision.

A Time Series Prediction Model for Wind Power Based on the Empirical Mode Decomposition–Convolutional Neural Network–Three-Dimensional Gated Neural Network

In response to the global challenge of climate change and the shift away from fossil fuels, the accurate prediction of wind power generation is crucial for optimizing grid operations and managing energy storage. This study introduces a novel approach by integrating the proportional–integral–derivative (PID) control theory into wind power forecasting, employing a three-dimensional gated neural (TGN) unit designed to enhance error feedback mechanisms. The proposed empirical mode decomposition (EMD)–convolutional neural network (CNN)–three-dimensional gated neural network (TGNN) framework starts with the pre-processing of wind data using EMD, followed by feature extraction via a CNN, and time series forecasting using the TGN unit. This setup leverages proportional, integral, and differential control within its architecture to improve adaptability and response to dynamic wind patterns. The experimental results show significant improvements in forecasting accuracy; the EMD–CNN–TGNN model outperforms both traditional models like autoregressive integrated moving average (ARIMA) and support vector regression (SVR), and similar neural network approaches, such as EMD–CNN–GRU and EMD–CNN–LSTM, across several metrics including mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R2). These advancements substantiate the model’s effectiveness in enhancing the precision of wind power predictions, offering substantial implications for future renewable energy management and storage solutions.

Wind Power Forecasting Based on CNN and LSTM Models

Nowadays more and more sustainable and environmentally friendly energy sources are demanded and wind power is playing an increasingly significant role in the energy market. For example, according to the Chinese Academy of Sciences, China plans to generate more than ten times the current scale of wind power in the next decade. Accurate prediction of wind generation is essential for the scheduling, operation, and energy trading of power systems. In this study, a method combining long and short-term memory network (LSTM) and convolutional neural network (CNN) is proposed for wind power generation prediction. In this study, a wind power generation data set from Longyuan Power Group is used to adapt to the input requirements of the model. The data are entered into the LSTM model to capture long-term trends in the data. Next, this paper uses the CNN to process the local spatial features to improve the prediction accuracy. Combining the outputs of these two models, this paper obtains an efficient wind power generation prediction method. In order to evaluate the performance of the proposed method, root mean square error is used to analyze the prediction data. The results show that the combined method of LSTM and CNN is better than individual methods in prediction accuracy and stability. In conclusion, this study proposes a wind power forecasting method combining LSTM and CNN. This method helps to improve the dispatching and operation efficiency of the power system and provides strong support for the promotion of wind power in the energy market.

A combined approach for wind power generation prediction based on CNN and double BiLSTM neural network

With the demand for electricity is increasing in the world. The power supply share of renewable energy generation is currently increasing and will require a large amount of power supply. However, renewable energy generation will cause large power fluctuations, which will affect the charging efficiency and security of the power system. Improving the prediction accuracy of renewable energy generation can effectively control and reduce power fluctuations in power supply. In this paper, the method based on convolutional neural network (CNN) and bi-directional long-term and short-term memory (BiLSTM) is applied to wind power generation prediction using the past time-series data sets. Experimental results on 12 different really world datasets show that the proposed model can reduce the prediction error by up to 77.8% compared with CNN-LSTM.

Winds of Change: Enhancing Wind Power Generation Forecasting with LSTM Models and Advanced Techniques in a Single Turbine Wind Farm

This article presents a comprehensive analysis of wind power generation using a Long Short-Term Memory (LSTM) model in the context of a wind farm with a single turbine. The novelty of the research lies in the integration of a technical model, predictive model, heat map analysis, and LSTM architecture to enhance forecasting accuracy. The performance of the forecasted model is evaluated using standard metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). In the estimation phase, wind power generation is predicted based on current weather conditions and historical trends. Leveraging multivariate time series forecasting with LSTM through the Kera library, different look-back values are explored to optimise prediction accuracy. In particular, the study achieves favourable performance metrics, including MSE of 27.781, RMSE of 5.271, MAE of 3.281, and variance of 0.886 with a 70% training and 30% test split. Furthermore, a 60% training and 40% test split yield improved accuracy with MSE of 28.791, RMSE of 5.366, MAE of 3.376, and variance of 0.888. In the prediction phase, the LSTM model is employed to forecast power generation without relying on future weather information. Through various experiments, the optimal look-back period and number of neurones for the LSTM model are determined. Notably, the achieved mean absolute percent errors (MAPE) of 11.433% and 11.158% for 24 and 48 hours of data respectively showcase the impact of varying neuron counts on prediction accuracy. To further enhance predictive capabilities, optimisation techniques are implemented, involving adjustments to the LSTM model, including increased input batch size, batch normalisation, and dense layers. This optimisation leads to a substantial decrease in MAPE from 92.53% to 87.14% in a monthly prediction experiment. Furthermore, forward predictions are made for multiple days in the future, where LSTM successfully captures patterns and fluctuations, resulting in MAPEs of 70.74% for 24 hours, 39.26% for 2 days, and 51.48% for 1 week. Additionally, a comparison of two LSTM-based models, Autoencoder LSTM and FFT-Encoder-Decoder-LSTM, demonstrates the superior performance of the latter in terms of RMSE. This study emphasizes the potential of LSTM models in wind power generation prediction while introducing novel insights into optimizing model performance for accurate forecasting. The integration of technical and predictive models, along with heat map analysis and LSTM architecture, sets the groundwork for advancing wind power prediction methodologies.

Wind power generation prediction based on the SSA-CNN-BiLSTM neural network model

This research utilized a series of historical wind speed observations and corresponding wind power generation data as training data. Based on the SSA-CNN-BiLSTM neural network model, the paper first preprocessed the historical data to extract relevant features related to wind power generation. Subsequently, a deep learning model based on the integration of Sparrow Search Algorithm, Convolutional Neural Network, and Bidirectional Long Short-Term Memory Network was constructed. This model leveraged the global search optimization capability of SSA and the advantages of CNN-BiLSTM in sequence and image processing. Through experimentation, predictive results were obtained and compared with real data to validate the rationality and accuracy of the algorithm. The experimental outcomes demonstrated a significant improvement in wind power generation prediction using the SSA-CNN-BiLSTM model. This confirmed the superiority of the model in addressing wind power generation prediction tasks and provided valuable references and applications for enhancing the reliability and efficiency of the wind power industry.

Complex terrains and wind power: enhancing forecasting accuracy through CNNs and DeepSHAP analysis

Accurate prediction of wind power generation in regions characterised by complex terrain is a critical gap in renewable energy research. To address this challenge, the present study articulates a novel methodological framework using Convolutional Neural Networks (CNNs) to improve wind power forecasting in such geographically diverse areas. The core research question is to investigate the extent to which terrain complexity affects forecast accuracy. To this end, DeepSHAP—an advanced interpretability technique—is used to dissect the CNN model and identify the most significant features of the weather forecast grid that have the greatest impact on forecast accuracy. Our results show a clear correlation between certain topographical features and forecast accuracy, demonstrating that complex terrain features are an important part of the forecasting process. The study’s findings support the hypothesis that a detailed understanding of terrain features, facilitated by model interpretability, is essential for improving wind energy forecasts. Consequently, this research addresses an important gap by clarifying the influence of complex terrain on wind energy forecasting and provides a strategic pathway for more efficient use of wind resources, thereby supporting the wider adoption of wind energy as a sustainable energy source, even in regions with complex terrain.

Wind power generation prediction during the COVID-19 epidemic based on novel hybrid deep learning techniques

Wind power generation (WPG) has been expanding rapidly because wind energy is clean, sustainable, and environmentally friendly. Accurate forecasting of WPG is particularly important for maintaining a functioning electricity system in a secure and steady manner. However, WPG is affected by many factors, such as weather, and the accurate prediction of WPG is complex and difficult, especially under special circumstances (e.g., during epidemics, war, or severe weather). To accurately predict WPG under special conditions, a novel hybrid deep learning approach (MLP-LSTM) combined with the long short-term memory (LSTM) network and the multilayer perceptron (MLP) network with fully connected layers is proposed in this paper. The MLP-LSTM method exhibited excellent predictive performance in forecasting WPG. Specifically, considering the external environment, we investigated the influence of factors by forecasting the WPG difference between normal and special cases with statistical regression modeling. The identified determinant information regarding climate factors and epidemic response indices was gathered and matched to the WPG difference and input into the MLP models. The prediction result of the MLP model was further utilized to correct the prediction values of the LSTM network for predicting WPG to obtain precise and reliable prediction values. To further assess the predictive performance of the MLP-LSTM method, WPG data from seven Nordic countries were used in the prediction models. The outcomes demonstrated that the MLP-LSTM method had high accuracy and robustness in predicting WPG, and it outperformed benchmark methods. Moreover, the combined method's prediction error was much lower than that of the single method.

Data-driven methods for situation awareness and operational adjustment of sustainable energy integration into power systems

In the context of increasing complexity in power system operations due to the integration of renewable energy sources, two main challenges arise: accurate short-term wind power forecasting and power flow convergence control. Accurate wind power forecasting plays a crucial role in power system scheduling, while controlling power flow convergence is essential for system stability. This study proposes a concise short-term wind power generation prediction model that combines a feature selection-based convolutional neural network-bidirectional long short-term memory network (CNN-BiLSTM) model. By effectively screening multidimensional feature datasets, the model optimizes the selection of highly correlated feature parameters and assigns weights to input data based on feature correlation. The CNN-BiLSTM combination model is then employed to establish a predictive model for wind power generation based on multiple features. Additionally, this study introduces an automatic adjustment model for power flow convergence using the D3QN (Double Dueling Q Network) reinforcement learning algorithm. This addresses the challenge of power imbalance leading to flow non-convergence, enabling effective control of power flow convergence and adaptive adjustment of operating modes. Experiments conducted using the KDD Cup 2022 wind power prediction dataset validate the wind power prediction method. The results demonstrate that the CNN-BiLSTM model effectively utilizes time-series data, surpassing other neural networks in prediction accuracy. Simulation results based on the PYPOWER case39 standard case reveal that the reinforcement learning model’s reward value increases with training rounds and stabilizes at 40. Remarkably, more than 72% of abnormal flow samples achieve rapid convergence within 10 steps, affirming the proposed method's efficacy and computational efficiency. The findings of this study contribute to enhancing the accurate awareness of new energy integration into power systems and provide a novel adaptive control method for power flow.

EMD-based gray combined forecasting model - Application to long-term forecasting of wind power generation

Wind power is the most promising renewable energy source after hydropower because of its mature technology and low price, and has great potential for carbon emission reduction. Long-term forecasts of its power generation can help power companies to develop operational plans, grid configuration and power dispatch, and can also provide a basis for the government to formulate energy and environmental policies. However, due to the characteristics of China's monsoon climate and wind power industry development, wind power generation data are characterized by nonlinear cycles and small data volume, which makes accurate prediction more difficult. To this end, this paper develops a new prediction model and applies it to the long-term prediction of wind power generation in China, and proposes some targeted policy recommendations based on the prediction results to promote the development of China's wind power industry.

Prediction of Wind Power Generation in Offshore Wind Farms Based on Deep Learning Using Ocean Buoy Data

Prediction of Wind Power Generation in Offshore Wind Farms Based on Deep Learning Using Ocean Buoy Data

Role of Machine Learning Algorithms for Wind Power Generation Prediction in Renewable Energy Management

The electrical energy demand is growing every day. Fossil fuel-based electrical power generation pollutes the environment. So, to fulfil the electrical energy demand, clean renewable energy sources like solar and wind are emerging as viable alternatives. But the amount of power generated from solar and wind energy sources is varying in magnitude and is intermittent in nature. So, predicting solar and wind power generation is indispensable for efficient planning and controlling of a renewable energy system. This paper aims to identify a machine learning model suitable for time-series prediction of wind power and also surveys the predominantly existing machine learning followed by the deep learning approaches. The main objective of this study is to solve a real-life problem in the renewable energy sector by accurately estimating the amount of power generation production per hour by applying machine learning techniques using historical wind power energy production data. Hence, historical wind data set with input parameters like wind speed, wind direction air temperature, pressure, and output parameters of wind power generation is considered for training and testing using mean absolute error for evaluating the prediction accuracy. Layers from simple to complex deep layers are trained and tested for prediction accuracy, i.e. baseline, linear, dense, multidense, convolution neural networks, and long short-term memory. Investigating further, LSTM and Residual LSTM prove to have higher mean absolute prediction accuracy of 0.0987 and 0.0958. This eventually enlightens the wind power forecasting models and new paths for further research applications.

Techno-economic evaluation of green hydrogen production with low-temperature water electrolysis technologies directly coupled with renewable power sources

It is obvious that green hydrogen production needs to increase to solve climate change issues, but green hydrogen has not been actively produced due to economic disadvantages. In order to secure the economic feasibility of green hydrogen production, it is necessary to find the optimal combination of renewable power sources and water electrolysis types. In this study, the combinations of three types of renewable power plant such as offshore wind, onshore wind, and onshore photovoltaic and two types of water electrolysis such as alkaline water electrolysis (AWE) and polymer electrolyte membrane water electrolysis (PEMWE) are analyzed in the economic evaluation. Previous researches on economic evaluation of green hydrogen production have only been conducted under rated operating conditions, but this study conducted realistic calculations by considering partial load operation according to fluctuating power generation. Accurate hydrogen production efficiency was derived by using actual renewable energy generation data for year with sample time of 1hr, and water electrolysis system simulation results according to power input. The offshore wind power generation data was generated using a wind power generation prediction model using a machine learning method. LCOH for each case was calculated using cost data of system components, and the impact of each price component on LCOH was analyzed through sensitivity analysis. As a result of the calculations, the onshore wind power-AWE case has the lowest LCOH with 7.25 $/kg while PV-PEMWE case has the highest LCOH with 13.44 $/kg. The capacity factor of renewable energy is shown to have the greatest impact on LCOH by sensitivity analysis, followed by the capital expenditure (CAPEX) of the renewable power plant or the operating expenditure (OPEX). This study provides an insight to establish the methodology finding out the most efficient green hydrogen production system based on water electrolysis dedicated to renewable power sources.

Power Curve Modeling of Wind Turbines through Clustering-Based Outlier Elimination

The estimation of power curve is the central task for efficient operation and prediction of wind power generation. It is often the case, however, that the actual data exhibit a great deal of variations in power output with respect to wind speed, and thus the power curve estimation necessitates the detection and proper treatment of outliers. This study proposes a novel procedure for outlier detection and elimination for estimating power curves of wind farms by employing clustering algorithms of vector quantization and density-based spatial clustering of applications with noise. Testing different parametric models of power output curve, the proposed methodology is demonstrated for obtaining power curves of individual wind turbines in a Korean wind farm. It is asserted that the outlier elimination procedure for power curve modeling outlined in this study can be highly efficient at the presence of noises.

Short-Term Forecasting Based on Graph Convolution Networks and Multiresolution Convolution Neural Networks for Wind Power

Accurate prediction of wind power generation is of great significance for the efficient operation of wind farms. However, traditional deep learning-based methods predict the wind power without simultaneously considering the temporal features of wind power and spatial features between variables, which leads to low prediction accuracy. This article proposes a novel wind power forecasting approach based on a graph convolution network (GCN) and a multiresolution convolution neural network (CNN), combining spatial features and temporal features. In this approach, GCN merged with maximum information coefficient (MIC) is proposed to extract the spatial correlation features between input variables, which considers the effects of multiple variables on wind power and provides interpretability for deep learning-based forecasting. On the other hand, multiresolution CNN combines multiscale convolution kernels with a new self-attention mechanism to understand local and long-term temporal features, which enables simultaneous prediction of wind power and other variables. Experiments on a real dataset prove that the proposed method is effective and accurate in short-term wind power forecasting. Comparisons with the other three state-of-the-art methods and ablation experiments also reveal the advantages of the proposed approach.

Wind Power Prediction Based on Machine Learning and Deep Learning Models

Wind power is one of the sustainable ways to generate renewable energy. In recent years, some countries have set renewables to meet future energy needs, with the primary goal of reducing emissions and promoting sustainable growth, primarily the use of wind and solar power. To achieve the prediction of wind power generation, several deep and machine learning models are constructed in this article as base models. These regression models are Deep neural network (DNN), k-nearest neighbor (KNN) regressor, long short-term memory (LSTM), averaging model, random forest (RF) regressor, bagging regressor, and gradient boosting (GB) regressor. In addition, data cleaning and data preprocessing were performed to the data. The dataset used in this study includes 4 features and 50530 instances. To accurately predict the wind power values, we propose in this paper a new optimization technique based on stochastic fractal search and particle swarm optimization (SFS-PSO) to optimize the parameters of LSTM network. Five evaluation criteria were utilized to estimate the efficiency of the regression models, namely, mean absolute error (MAE), Nash Sutcliffe Efficiency (NSE), mean square error (MSE), coefficient of determination (R2), root mean squared error (RMSE). The experimental results illustrated that the proposed optimization of LSTM using SFS-PSO model achieved the best results with R2 equals 99.99% in predicting the wind power values.

A Selective Review on Recent Advancements in Long, Short and Ultra-Short-Term Wind Power Prediction

With large penetration of wind power into power grids, the accurate prediction of wind power generation is becoming extremely important. Planning, scheduling, maintenance, trading and smooth operations all depend on the accuracy of the prediction. However due to the highly non-stationary and chaotic behaviour of wind, accurate forecasting of wind power for different intervals of time becomes more challenging. Forecasting of wind power generation over different time spans is essential for different applications of wind energy. Recent development in this research field displays a wide spectrum of wind power prediction methods covering different prediction horizons. A detailed review of recent research achievements, performance, and information about possible future scope is presented in this article. This paper systematically reviews long term, short term and ultra short term wind power prediction methods. Each category of forecasting methods is further classified into four subclasses and a comparative analysis is presented. This study also provides discussions of recent development trends, performance analysis and future recommendations.

Research on short-term power prediction of wind power generation based on WT-CABC-KELM

Under the framework of the gradual scarcity of fossil energy, how to make better use of wind power to provide a safe and effective guarantee for the power grid has become a hot topic today. Aiming at the problems of existing short-term wind power prediction models that are easy to fall into local optimum, slow training speed, and poor accuracy, this paper proposes an improved artificial bee colony algorithm based on wavelet transform (WT) combined with a nuclear extreme learning machine. Power prediction method (WT-CABC-KELM). First, aiming at the volatility of historical wind power data, it is proposed to use wavelet transform to extract the hidden main features at each frequency to improve the prediction accuracy. Then, because the two parameters C and λ of the KELM model have a greater impact on the prediction accuracy, The improved artificial bee colony algorithm (CABC) is used to optimize the parameters of its model to achieve the purpose of improving the prediction accuracy, and the WT-CABC-KELM short-term wind power prediction model is established. In order to prove the effectiveness of the method proposed in this paper, 4 Each season is forecasted separately, and compared with traditional SVM and BP neural network. Finally, the results are compared and analyzed by 4 kinds of error indicators. The experimental results show that the short-term wind power prediction method (WT-CABC-KELM) mentioned in this article can effectively improve the short-term power prediction accuracy of wind power.