Wind speed forecasting based on hybrid model with model selection and wind energy conversion

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.

Accurate and consistent wind speed forecasting is vital for efficient energy management and the market economy. Wind speed is non-linear, non-stationary, and irregular, so it is very difficult to forecast. There are many forecasting methods currently in use; however, selecting and developing the most appropriate method for a particular region in wind speed forecasting is still a hot topic. This study presents a new and unique neural network-based ensemble system for forecasting wind speed, which is very difficult to predict but is directly related to the power generated by wind farms for individual and different sites. With the developed ensemble model, average mean absolute error, mean absolute percentage error and root mean square error values are obtained as 0.1269, 3.074%, 0.1596 respectively. Test results demonstrate significant contributions of the proposed system compared to existing statistical, heuristic and ensemble models, indicating that the developed model is a promising alternative for wind speed forecasting models. The obtained results show that this system is an effective and useful intelligent tool that can be used by various companies and government facilities that invest and operate in intelligent wind energy technologies.

Short-term wind speed forecasting based on the Jaya-SVM model

Ultra-short-term wind speed forecasting based on EMD-VAR model and spatial correlation

Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models

This study investigates the effect of data decomposition to improve the performance of artificial neural networks (ANNs), widely used in wind speed forecasting in the wind energy sector. Artificial neural networks are essential tools for planning and optimizing the daily generation of wind power plants. However, prediction errors can lead to significant problems in power generation and energy grid management. The results show that data decomposition substantially affects the wind speed forecasting performance of neural networks. These findings are essential for researchers and industry professionals interested in developing more accurate forecasting models for power generation planning and management in the wind energy sector. By integrating artificial neural networks and data disaggregation methods, the study stands out as an essential step forward to improve the accuracy of wind speed forecasts and optimize the efficiency of wind energy facilities.

Wind energy has gained significant attention as a clean, renewable resource due to fossil fuels' environmental impact. Accurate wind speed forecasting is essential to address variability and intermittency challenges. Current forecasting difficulties arise from wind speed's high susceptibility to meteorological conditions. This study proposes a GA-based ensemble framework that combines forecasting models using genetic algorithms. We systematically compared 14 models: linear models (AR, ARMA), advanced neural networks (MLP, RBF), hybrid models, and ensembles. Models were evaluated using minute-by-minute data from five major Brazilian cities: Brasília, Florianópolis, Petrolina, Natal, and São Luís. Key findings include: I) Superior Performance: The proposed framework achieved MSE values from 0.0802 to 0.9020 and MAE values from 0.1970 to 0.6140 across all datasets; II) Robust Prediction: values ranged from 0.7139 to 0.8723, demonstrating strong predictive capability; III) Statistical Validation: Friedman test ( ) confirmed significant differences with perfect rank stability across all locations; IV) High Scalability: Runtimes ranged from 58,077.3 to 77,815.7 s, determined by the base model combination; and V) Computational Efficiency: One-step-ahead forecasting requires only 0.0003 s for weighting and combination.

Research and application based on the swarm intelligence algorithm and artificial intelligence for wind farm decision system

A compound structure of ELM based on feature selection and parameter optimization using hybrid backtracking search algorithm for wind speed forecasting

Hybrid wind speed forecasting using ICEEMDAN and transformer model with novel loss function

A combined forecasting model for time series: Application to short-term wind speed forecasting

Many renewable energy resources, including wind energy, are uncertain and often unavailable when needed, with high variability and dependency on atmospheric and climatic conditions. Variability and uncertainty of wind energy follow those wind speed and occur at multiple timescales; that is, from seconds to minutes and then to hours. They also require movement of other resources to ensure the balance between generation and load. This requires an accurate wind speed prediction. However, most existing wind speed forecasting models are based on data that do not take into account the difference between day and night, which can limit the accuracy of wind speed forecasting. Therefore, to improve the prediction accuracy, this study proposes three scenarios for wind speed prediction: first, the prediction model does not take into account the difference between day and night. The second model uses only data recorded during the day, and the third model deals with data measured during the night only. A comparative analysis and comprehensive evaluation will be given at the end of this work to verify the proposed hypothesis. The recorded climate data and measured wind speed at AUMET station will be used in this study.

Accurate forecasting of wind speed plays a fundamental role in enabling reliable operation and planning for large‐scale integration of wind turbines. It is difficult to obtain the accurate wind speed forecasting (WSF) due to the intermittent and random nature of wind energy. In this paper, a multiperiod‐ahead WSF model based on the analysis of variance, stacked denoising autoencoder (SDAE), and ensemble learning is proposed. The analysis of variance classifies the training samples into different categories. The stacked denoising autoencoder as a deep learning architecture is later built for unsupervised feature learning in each category. The ensemble of extreme learning machine (ELM) is applied to fine‐tune the SDAE for multiperiod‐ahead wind speed forecasting. Experimental results are made to demonstrate that the proposed model has the best performance compared with the classic WSF methods including the single SDAE‐ELM, ELMAN, and adaptive neuron‐fuzzy inference system (ANFIS).

A novel ensemble system for short-term wind speed forecasting based on hybrid decomposition approach and artificial intelligence models optimized by self-attention mechanism

Wind speed forecasting is particularly important for wind farms due to cost-related issues, dispatch planning, and energy markets operations. This paper presents a multi-target learning method, in order to model historical wind speed data and yield accurate forecasts of the wind speed on the day-ahead (24 h) horizon. The proposed method is based on the analysis of historical data, which are represented at multiple scales in both space and time. Handling multi-scale data allows us to leverage the knowledge hidden in both the spatial and temporal variability of the shared information, in order to identify spatio-temporal aided patterns that contribute to yield accurate wind speed forecasts. The viability of the presented method is evaluated by considering benchmark data. Specifically, the empirical study shows that learning multi-scale historical data allows us to determine accurate wind speed forecasts.