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Peng Lu
College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China

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Journal article
Published: 09 August 2021 in Renewable Energy
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To achieve a high penetration of renewable energy integration, an effective solution is to explore the interdependence between numerical weather prediction (NWP) data and historical wind power to improve prediction accuracy. This paper proposes a novel combined approach for wind power prediction. The characteristics of NWP and historical wind power data are extracted by using the feature extraction technique, the predictor is designed based on extreme learning machine (ELM) and least squares support vector machine (LSSVM) model, and then key parameters of the prediction models are optimized by improving cuckoo search (ICS) to obtain a reliable value, which is defined as the pre-combined prediction value (PPA). To obtain a reliable result, a variance strategy is developed to allocate the weights of the pre-combined prediction model to obtain the final predicted values. Four seasons dataset collected from regional wind farms in China is utilized as a benchmark experiment to evaluate the effectiveness of the proposed approach. The results of comprehensive numerical cases with different seasons show that the proposed approach, which considers multiple-error metrics, including error metrics, accuracy rate, qualification rate, and improvement percentages, achieves higher accuracy than other benchmark prediction models.

ACS Style

Peng Lu; Lin Ye; Yongning Zhao; Binhua Dai; Ming Pei; Zhuo Li. Feature extraction of meteorological factors for wind power prediction based on variable weight combined method. Renewable Energy 2021, 179, 1925 -1939.

AMA Style

Peng Lu, Lin Ye, Yongning Zhao, Binhua Dai, Ming Pei, Zhuo Li. Feature extraction of meteorological factors for wind power prediction based on variable weight combined method. Renewable Energy. 2021; 179 ():1925-1939.

Chicago/Turabian Style

Peng Lu; Lin Ye; Yongning Zhao; Binhua Dai; Ming Pei; Zhuo Li. 2021. "Feature extraction of meteorological factors for wind power prediction based on variable weight combined method." Renewable Energy 179, no. : 1925-1939.

Review
Published: 06 August 2021 in Applied Energy
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The integration of large-scale wind power introduces issues in modern power systems operations due to its strong randomness and volatility. These issues can be resolved via wind power forecasting that can provide comprehensive future information about wind power uncertainties. This paper presents a timely and comprehensive review of meta-heuristic algorithms in the framework of wind power forecasting. The framework is based on the auxiliary layer, forecasting base layer, and core layer. The auxiliary layer, such as the data-decomposition layer, decomposes the wind power time series into many relatively stationary subseries, and uses prediction models, including artificial neural networks (ANNs) and machine learning (ML). The core layer is based on meta-heuristic algorithms, which include evolutionary-based algorithms, physics-based algorithms, human-based algorithms, swarm-based algorithms, hybrid algorithms, and multi-objective optimization algorithms. These algorithms aim to search for the optimal solutions under constraints, which is highly significant for optimizing the key parameters of the prediction models. Besides, multiple error evaluation metrics, e.g., deterministic, uncertainty, and testing methods used in the field of wind power prediction are described. A quantitative analysis focusing on their advantages, disadvantages, forecasting accuracy, and computational costs are also provided. Finally, a few open research issues and trends related to the topic are discussed, which can contribute to improving the understanding of each wind power forecasting method. In general, this review paper provides valuable insights to wind power engineers.

ACS Style

Peng Lu; Lin Ye; Yongning Zhao; Binhua Dai; Ming Pei; Yong Tang. Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges. Applied Energy 2021, 301, 117446 .

AMA Style

Peng Lu, Lin Ye, Yongning Zhao, Binhua Dai, Ming Pei, Yong Tang. Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges. Applied Energy. 2021; 301 ():117446.

Chicago/Turabian Style

Peng Lu; Lin Ye; Yongning Zhao; Binhua Dai; Ming Pei; Yong Tang. 2021. "Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges." Applied Energy 301, no. : 117446.

Journal article
Published: 11 January 2020 in Journal of Cleaner Production
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The integration of a large number of wind farms poses big challenges to the secure and economical operation of power systems, and ultra-short-term wind power forecasting is an effective solution. However, traditional approaches can only predict an individual wind farm power at a time and ignore the spatio-temporal correlation of wind farms. In this paper, a novel ultra-short-term forecasting framework based on spatio-temporal (ST) analysis, multi-output support vector machine (MSVM) and grey wolf optimizer (GWO) which defined ST-GWO-MSVM model is proposed to predict the output wind power from multiple wind farms; the ST-GWO-MSVM model includes data analysis stage, parameters optimization stage, and modeling stage. In the data analysis stage, the person correlation coefficient and partial autocorrelation function are used to analyze the spatio-temporal correlation of wind power. In the parameters optimization stage, to avoid obtaining the unreliable forecasting results due to the parameters are chosen empirically, the GWO algorithm is used to optimize the kernel function parameters of the MSVM model. In the modeling stage, an innovative forecasting model with optimal parameter of MSVM is proposed to predict the output wind power of 15 wind farms. Results show that the performance of ST-GWO-MSVM is better than other benchmark models in terms of multiple-error metrics including fractional bias, direction accuracy, and improvement percentages.

ACS Style

Peng Lu; Lin Ye; Wuzhi Zhong; Ying Qu; Bingxu Zhai; Yong Tang; Yongning Zhao. A novel spatio-temporal wind power forecasting framework based on multi-output support vector machine and optimization strategy. Journal of Cleaner Production 2020, 254, 119993 .

AMA Style

Peng Lu, Lin Ye, Wuzhi Zhong, Ying Qu, Bingxu Zhai, Yong Tang, Yongning Zhao. A novel spatio-temporal wind power forecasting framework based on multi-output support vector machine and optimization strategy. Journal of Cleaner Production. 2020; 254 ():119993.

Chicago/Turabian Style

Peng Lu; Lin Ye; Wuzhi Zhong; Ying Qu; Bingxu Zhai; Yong Tang; Yongning Zhao. 2020. "A novel spatio-temporal wind power forecasting framework based on multi-output support vector machine and optimization strategy." Journal of Cleaner Production 254, no. : 119993.

Journal article
Published: 21 March 2018 in Energies
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Wind power time series data always exhibits nonlinear and non-stationary features, making it very difficult to accurately predict. In this paper, a novel hybrid wind power time series prediction model, based on ensemble empirical mode decomposition-permutation entropy (EEMD-PE), the least squares support vector machine model (LSSVM), and gravitational search algorithm (GSA), is proposed to improve accuracy of ultra-short-term wind power forecasting. To process the data, original wind power series were decomposed by EEMD-PE techniques into a number of subsequences with obvious complexity differences. Then, a new heuristic GSA algorithm was utilized to optimize the parameters of the LSSVM. The optimized model was developed for wind power forecasting and improved regression prediction accuracy. The proposed model was validated with practical wind power generation data from the Hebei province, China. A comprehensive error metric analysis was carried out to compare the performance of our method with other approaches. The results showed that the proposed model enhanced forecasting performance compared to other benchmark models.

ACS Style

Peng Lu; Lin Ye; Bohao Sun; Cihang Zhang; Yongning Zhao; Jingzhu Teng. A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA. Energies 2018, 11, 697 .

AMA Style

Peng Lu, Lin Ye, Bohao Sun, Cihang Zhang, Yongning Zhao, Jingzhu Teng. A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA. Energies. 2018; 11 (4):697.

Chicago/Turabian Style

Peng Lu; Lin Ye; Bohao Sun; Cihang Zhang; Yongning Zhao; Jingzhu Teng. 2018. "A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA." Energies 11, no. 4: 697.

Journal article
Published: 17 January 2018 in IEEE Transactions on Power Systems
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The ever-increasing number of wind farms has brought challenges and opportunities in wind power forecasting techniques to take advantage of interdependencies between hundreds of spatially distributed wind farms, e.g., over a region. In this paper, a Sparsity-Controlled Vector Autoregressive (SC-VAR) model is introduced to obtain sparse model structures in a spatio-temporal wind power forecasting framework by reformulating the original VAR model into a constrained Mixed Integer Non-Linear Programming (MINLP) problem. It allows controlling the sparsity of the coefficient matrices in direct manner. However this original SC-VAR is difficult to implement due to its complicated constraints and the lack of guidelines for setting its parameters. To reduce the complexity of this MINLP and to make it possible to incorporate prior expert knowledge to benefit model building and forecasting, the original SC-VAR is modified and a Correlation-Constrained SC-VAR (CCSC-VAR) is proposed based on spatial correlation information about wind farms. Our approach is evaluated based on a case study of very-short-term forecasting for 25 wind farms in Denmark. Comparison is performed with a set of traditional local methods and spatio-temporal methods. Results show the proposed CCSC-VAR has better overall performance than both the original SC-VAR and other benchmark methods, taking into account all evaluation indicators, including sparsity-control ability, sparsity, accuracy and efficiency.

ACS Style

Yongning Zhao; Lin Ye; Pierre Pinson; Yong Tang; Peng Lu. Correlation-Constrained and Sparsity-Controlled Vector Autoregressive Model for Spatio-Temporal Wind Power Forecasting. IEEE Transactions on Power Systems 2018, 33, 5029 -5040.

AMA Style

Yongning Zhao, Lin Ye, Pierre Pinson, Yong Tang, Peng Lu. Correlation-Constrained and Sparsity-Controlled Vector Autoregressive Model for Spatio-Temporal Wind Power Forecasting. IEEE Transactions on Power Systems. 2018; 33 (5):5029-5040.

Chicago/Turabian Style

Yongning Zhao; Lin Ye; Pierre Pinson; Yong Tang; Peng Lu. 2018. "Correlation-Constrained and Sparsity-Controlled Vector Autoregressive Model for Spatio-Temporal Wind Power Forecasting." IEEE Transactions on Power Systems 33, no. 5: 5029-5040.