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Wind speed/power has received increasing attention around the earth due to its renewable nature as well as environmental friendliness. With the global installed wind power capacity rapidly increasing, wind industry is growing into a large-scale business. Reliable short-term wind speed forecasts play a practical and crucial role in wind energy conversion systems, such as the dynamic control of wind turbines and power system scheduling. In this paper, an intelligent hybrid model for short-term wind speed prediction is examined; the model is based on cross correlation (CC) analysis and a support vector regression (SVR) model that is coupled with brainstorm optimization (BSO) and cuckoo search (CS) algorithms, which are successfully utilized for parameter determination. The proposed hybrid models were used to forecast short-term wind speeds collected from four wind turbines located on a wind farm in China. The forecasting results demonstrate that the intelligent hybrid models outperform single models for short-term wind speed forecasting, which mainly results from the superiority of BSO and CS for parameter optimization.
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69A hybrid approach based on the Gaussian process with t-observation model for short-term wind speed forecasts
A hybrid approach based on the Gaussian process with t-observation model for short-term wind speed forecasts
8Deep learning based multi-step short term wind speed forecasts with LSTM
Wind energy is one of the fastest progressing renewable energy sources. Wind speed forecasting has gained more attention as wind energy never causes air pollution and other hazards to the environment. Eco-friendliness and cost-effectiveness are the main reasons for the high demand for wind energy. Accurate and reliable prediction model for wind speed forecasting is very challenging because of its intermittent nature. Deep learning based architectures like Recurrent Neural Networks (RNN) and Long Short Term Memory (LSTM) are considered as better models for time series prediction. LSTM neural network models are more appropriate for learning long term dependencies. In this paper, an LSTM network for a multi-step-ahead short term wind speed forecasting is proposed. As lag values have importance in time series prediction problems, tests are conducted to find optimal lag value. The value which contributes minimum mean square error is selected as optimal lag value. The study of the results reveals that the proposed LSTM model is more effective and efficient with high predictive accuracy compared to Auto-Regressive Integrated Moving Average (ARIMA) model.
Read more13A novel wind speed forecasting model for wind farms of Northwest China
ABSTRACTWind resources are becoming increasingly significant due to their clean and renewable characteristics, and the integration of wind power into existing electricity systems is imminent. To maintain a stable power supply system that takes into account the stochastic nature of wind speed, accurate wind speed forecasting is pivotal. However, no single model can be applied to all cases. Recent studies show that wind speed forecasting errors are approximately 25% to 40% in Chinese wind farms. Presently, hybrid wind speed forecasting models are widely used and have been verified to perform better than conventional single forecasting models, not only in short-term wind speed forecasting but also in long-term forecasting. In this paper, a hybrid forecasting model is developed, the Similar Coefficient Sum (SCS) and Hermite Interpolation are exploited to process the original wind speed data, and the SVM model whose parameters are tuned by an artificial intelligence model is built to make forecast. The results of case studies show that the MAPE value of the hybrid model varies from 22.96% to 28.87 %, and the MAE value varies from 0.47 m/s to 1.30 m/s. Generally, Sign test, Wilcoxon’s Signed-Rank test, and Morgan--Granger--Newbold test tell us that the proposed model is different from the compared models.
Read more130A hybrid technique for short-term wind speed prediction
A hybrid technique for short-term wind speed prediction
10Research and Application of a New Hybrid Forecasting Model Based on Genetic Algorithm Optimization: A Case Study of Shandong Wind Farm in China
With the increasing depletion of fossil fuel and serious destruction of environment, wind power, as a kind of clean and renewable resource, is more and more connected to the power system and plays a crucial role in power dispatch of hybrid system. Thus, it is necessary to forecast wind speed accurately for the operation of wind farm in hybrid system. In this paper, we propose a hybrid model called EEMD-GA-FAC/SAC to forecast wind speed. First, the Ensemble empirical mode decomposition (EEMD) can be applied to eliminate the noise of the original data. After data preprocessing, first-order adaptive coefficient forecasting method (FAC) or second-order adaptive coefficient forecasting method (SAC) can be employed to do forecast. It is significant to select optimal parameters for an effective model. Thus, genetic algorithm (GA) is used to determine parameter of the hybrid model. In order to verify the validity of the proposed model, every ten-minute wind speed data from three observation sites in Shandong Peninsula of China and several error evaluation criteria can be collected. Through comparing with traditional BP, ARIMA, FAC, and SAC model, the experimental results show that the proposed hybrid model EEMD-GA-FAC/SAC has the best forecasting performance.
Read more207Short-term wind speed forecasting based on the Jaya-SVM model
Short-term wind speed forecasting based on the Jaya-SVM model
9Research and Application of Hybrid Wind-Energy Forecasting Models Based on Cuckoo Search Optimization
Wind energy is crucial renewable and sustainable resource, which plays a major role in the energy mix in many countries around the world. Accurately forecasting the wind energy is not only important but also challenging in order to schedule the wind power generation and to ensure the security of wind-power integration. In this paper, four kinds of hybrid models based on cyclic exponential adjustment, adaptive coefficient methods and the cuckoo search algorithm are proposed to forecast the wind speed on large-scale wind farms in China. To verify the developed hybrid models’ effectiveness, wind-speed data from four sites of Xinjiang Uygur Autonomous Region located in northwest China are collected and analyzed. Multiple criteria are used to quantitatively evaluate the forecasting results. Simulation results indicate that (1) the proposed four hybrid models achieve desirable forecasting accuracy and outperform traditional back-propagating neural network, autoregressive integrated moving average as well as single adaptive coefficient methods, and (2) the parameters of hybrid models optimized by artificial intelligence contribute to higher forecasting accuracy compared with predetermined parameters.
Read more3Development of Maximum Power Point Tracking for Doubly-Fed Induction Generators in Wind Energy Conversion Systems
Maximum power point tracking (MPPT) control is one of the essential requirements in harnessing wind power of wind energy conversion systems (WECS). The more precise the maximum power point (MPP) is determined, the more optimal the WECS is operated. Amongst MPPT algorithms, a hill-climb search (HCS) algorithm is preferred because of its simplicity however it also has few drawbacks such as the difficulty of selecting an appropriate step size, the premature convergence phenomenon and the speed-efficiency trade-off. A cuckoo search (CS) algorithm is proposed in this paper for finding out a MPP of a WECS driven by a doubly-fed induction generator (DFIG) under various wind speeds which mostly overcomes the above disadvantages. Then, the DFIG-WECS is controlled to track the MPP. This ensures that the DFIG-WECS is always operated at MPPs regardless of various wind speeds. It is realized that the proposed CS algorithm is also a population-based algorithm inspired from the breeding behavior of cuckoos but it is quite simple, powerful and especially requires less parameters to define optimal solutions than a genetic algorithm (GA) and a particle swarm optimization (PSO) algorithm. Though numerical results, the CS algorithm shows its effectiveness in finding out MPPs in the DFIG-WECS. Furthermore, the obtained results using the CS algorithm are also compared with those using the HCS and PSO algorithms. The comparison demonstrates that the convergence value and speed of the CS algorithm are always better than those of the HCS and PSO algorithms in the MPPT application in the DFIG-WECS.
Read more270A compound structure of ELM based on feature selection and parameter optimization using hybrid backtracking search algorithm for wind speed forecasting
A compound structure of ELM based on feature selection and parameter optimization using hybrid backtracking search algorithm for wind speed forecasting
Read more79Comparison between ARIMA and ANN Models Used in Short-Term Wind Speed Forecasting
Wind power is a significant alternate energy in times of energy crisis. In virtue of its intermittency and fluctuation, it poses several operational challenges to grid interfaced wind energy systems. This paper introduced autoregressive integrated moving average (ARIMA) model and artificial neural network (ANN) to forecast the hourly wind speed one to four hours ahead. The models are applied to wind speed records for each month separately from a wind park in Hubei province of China. The experimental results demonstrate that these models are in good agreement with measurement values. The ANN model does a better job than ARIMA model in forecasting short-term hourly wind speed. Besides, different time series with different variance must construct different models. But when the variance is too high, it needs combined models or numerical weather predicting method to pursue better results.
Read moreMachine Learning approach for Short Term Wind Speed Forecasting
Due to depletion of conventional energy resources, the exploration of renewable energy resources has gained a lot of significance. Better forecasting models for the forthcoming supply of renewable energy resources are necessary to reduce the energy consumption from conventional power plants. Wind is a fluctuating kind of energy and accurately predicting the output power of wind energy is important to obtain optimal energy utilization in today's grid operation, dealing with the power load and pollution free atmosphere. This paper presents two machine learning tactics for short term wind power forecasting and that are Support Vector Regression (SVR) and Random Forest Regression (RFR). For this we take number of past observations of a series and that we have used to form the input pattern to train our both the models with which forecasts can be made for a present data point.
Read more418Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization
Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization
Read more55A novel ensemble system for short-term wind speed forecasting based on hybrid decomposition approach and artificial intelligence models optimized by self-attention mechanism
A novel ensemble system for short-term wind speed forecasting based on hybrid decomposition approach and artificial intelligence models optimized by self-attention mechanism
Read more3Probabilistic machine learning-based forecasting of wind speed uncertainty using adaptive kernel density estimation.
Short-term wind speed forecasting is essential for enhancing the efficiency and dependability of wind renewable energy installations. Although often used, conventional point predictions generated by machine learning techniques frequently fail to accurately capture the natural uncertainty associated with wind speed variation. Modeling this type of uncertainty is crucial for providing credible information as the level of uncertainty increases. Prediction intervals (PIs) offer a probabilistic framework for quantifying forecast uncertainty. This paper presents a hybrid forecasting methodology that combines support vector regression (SVR) with adaptive kernel density estimation (AKDE) to estimate wind speed prediction intervals over various short-term horizons (10, 30, 60, and 120 minutes). In contrast to standard kernel density estimation (KDE), which employs a uniform bandwidth and may overlook local data attributes, the adaptive KDE approach adjusts the bandwidth in accordance with the local distribution of forecast errors, thereby facilitating more precise and locally tuned uncertainty quantification. The efficacy of the proposed SVR-AKDE model is evaluated against conventional KDE-based interval estimation. Outcomes are assessed by recognized PI quality indicators, including prediction interval coverage probability (PICP), prediction interval normalized average width (PINAW), and coverage width-based criterion (CWC). Simulation findings confirm the efficacy of our approach and demonstrate that the SVR-AKDE-based PI forecasting consistently provides enhanced coverage and narrower widths compared to traditional KDE. This approach provides a comprehensive solution for short-term wind speed forecasting with quantifiable uncertainty, therefore enhancing its application in operational wind energy control.
Read more307A combined forecasting model for time series: Application to short-term wind speed forecasting
A combined forecasting model for time series: Application to short-term wind speed forecasting
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