Cluster-Driven Block-Flip Transformer With Dynamic Positional Learning for IoT-Based Wind Power Forecasting

Wind Power Forecasting

Wind power forecasting based on new hybrid model with TCN residual modification

At present, the accuracy of short-term wind power forecasting is low, level errors are commonly used to evaluate index, while few statistics about phase errors(delayed or ahead of time)are studied. In this paper, a method based on translating and interpolating correction of phase and level errors was put forward to improve forecasting accuracy, and proposed the conception of phase error constant trend duration, and classified the day-ahead wind power forecasting curve and the actual curve into three modes: the constant trend of power increasing, decreasing and unchanging, the calculation model of constant trend duration was established, that based on probabilistic and statistical methods to obtain deviated value and lagged or lead direction between the forecasting and the actual time value. Using the translated and interpolated method, combining phase and level error to correct the day-ahead power forecasting error. The simulated result showed : the wind power forecasting phase error was 3.78h, and the direction was delay, and level absolute error was 40.05MW, what the phase error value that after the translated and interpolated correction was 2.56h, the error of which was reduced by 32.34%, level absolute error was 32.58MW, reduced by 18.65%, which effectively improved the accuracy of short-term wind power forecasting.

A novel hybrid model based on Empirical Mode Decomposition and Echo State Network for wind power forecasting

Energy storage scheduling design on friendly grid wind power

A new intelligent method based on combination of VMD and ELM for short term wind power forecasting

Urbanization’s relentless advance intensifies the quest for sustainable energy sources, with smart cities leading the shift toward sustainability. In these innovative urban landscapes, wind power is pivotal in the clean energy paradigm. Efficient wind energy utilization hinges on accurate wind power forecasting, essential for energy management and grid stability. This review explores the use of neural network models for wind power forecasting in smart cities, driven by wind power’s growing importance in urban energy strategies and the expanding role of artificial neural networks (ANNs) in wind power prediction. Wind power integration mitigates greenhouse gas emissions and enhances energy resilience in urban settings. However, wind’s inherently variable nature necessitates precise forecasting. The surge in ANN use for wind power forecasting is another key driver of this review, as ANNs excel at modelling complex relationships in data. This review highlights the synergy between wind power forecasting and neural network models, emphasizing ANNs’ vital role in enhancing the accuracy of wind power predictions in urban environments. It covers neural network fundamentals, data preprocessing, diverse neural network architectures, and their applicability in short-term and long-term wind power forecasting. It also delves into training, validation methods, performance assessment metrics, challenges, and prospects. As smart cities champion urban sustainability, neural network models for wind power forecasting are poised to revolutionize urban energy systems, making them cleaner, more efficient, and more resilient.

Wind speed and wind power are two important indexes for wind farms. Accurate wind speed and power forecasting can help to improve wind farm management and increase the contribution of wind power to the grid. However, nonlinear and non-stationary wind speed and wind power can influence the forecasting performance of different models. To improve forecasting accuracy and overcome the influence of the original time series on the model, a forecasting system that can effectively forecast wind speed and wind power based on a data pre-processing strategy, a modified multi-objective optimization algorithm, a multiple single forecasting model, and a combined model is developed in this study. A data pre-processing strategy was implemented to determine the wind speed and wind power time series trends and to reduce interference from noise. Multiple artificial neural network forecasting models were used to forecast wind speed and wind power and construct a combined model. To obtain accurate and stable forecasting results, the multi-objective optimization algorithm was employed to optimize the weight of the combined model. As a case study, the developed forecasting system was used to forecast the wind speed and wind power over 10 min from four different sites. The point forecasting and interval forecasting results revealed that the developed forecasting system exceeds all other models with respect to forecasting precision and stability. Thus, the developed system is extremely useful for enhancing forecasting precision and is a reasonable and valid tool for use in intelligent grid programming.

Accurate wind power forecasting is essential for the reliable and efficient operation of renewable energy–dominated power systems, as it directly affects power system scheduling and grid stability. Nevertheless, the inherent intermittency, strong nonlinearity, and stochastic characteristics of wind power generation pose significant challenges to short-term forecasting accuracy. To tackle these issues, this paper develops a hybrid forecasting framework that integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Variational Mode Decomposition (VMD), and a deep learning predictor based on Convolutional Neural Networks and Long Short-Term Memory networks (CNN–LSTM). Specifically, CEEMDAN is first employed to decompose the original wind power time series into multiple intrinsic mode functions at different temporal scales. Subsequently, VMD is applied to the high-frequency components to further suppress noise and enhance signal stationarity. Each decomposed subsequence is then modeled and predicted using a CNN–LSTM architecture, and the final wind power forecast is obtained through reconstruction of all predicted components. Extensive experiments conducted on real-world wind farm data that the proposed hybrid model consistently outperforms several benchmark methods in terms of forecasting accuracy, thereby verifying its effectiveness and practical applicability for short-term wind power forecasting.

Accurate wind power forecasting is critical for enhancing the operational efficiency and stability of electrical power grids. Conventional single-variable signal decomposition forecasting methods ignore the coupling relationship between wind power and multiple meteorological data, thus limiting prediction accuracy. This study proposes an accurate and fast short-term wind power prediction approach based on series-core fusion technology considering multiple meteorological data. In the data preprocessing stage, the multivariate variational mode decomposition (MVMD) algorithm decomposes wind power and meteorological variables into the same predefined number of frequency-aligned intrinsic mode functions (IMFs), thereby enhancing feature representation and improving forecasting accuracy via a more comprehensive and detailed dataset representation. During the training stage, the series-core fused time series (SOFTS) model establishes the connection among wind power channel and other meteorological variable channels for each IMF, achieving fast convergence through its streamlined and parallel structure. In the forecasting stage, the final wind power prediction is generated by the reconstruction of all IMFs. Furthermore, we conducted a comprehensive performance evaluation by comparing the proposed MVMD-SOFTS model with eight alternative models, including the CNN model, the TCN model, the LSTM model, the GRU model, the Transformer model, the SOFTS model, the CEEMDAN-SOFTS model, and the VMD-SOFTS model. The results indicate that MVMD-SOFTS outperformed all other models, demonstrating its effectiveness in capturing the multifaceted relationships in wind power forecasting.

Accurate wind power forecasting is crucial for optimizing grid scheduling and improving wind power utilization. However, real-world wind power time series exhibit dynamic statistical properties, such as changing mean and variance over time, which make it difficult for models to apply observed patterns from the past to the future. Additionally, the execution speed and high computational resource demands of complex prediction models make them difficult to deploy on edge computing nodes such as wind farms. To address these issues, this paper explores the potential of linear models for wind power forecasting and constructs NFLM, a linear, lightweight, short-term wind power forecasting model that is more adapted to the characteristics of wind power data. The model captures both short-term and long-term sequence variations through continuous and interval sampling. To mitigate the interference of dynamic features, we propose a normalization feature learning block (NFLBlock) as the core component of NFLM for processing sequences. This module normalizes input data and uses a stacked multilayer perceptron to extract cross-temporal and cross-dimensional dependencies. Experiments with data from two real wind farms in Guangxi, China, showed that compared with other advanced wind power forecasting methods, the MSE of NFLM in the 24-step ahead forecasting of the two wind farms is respectively reduced by 23.88% and 21.03%, and the floating-point operations (FLOPs) and parameter count only require 36.366 M and 0.59 M, respectively. The results show that NFLM can achieve good prediction accuracy with fewer computing resources.

Renewable energies, such as solar and wind power, have become promising sources of energy to address the increase in greenhouse gases caused by the use of fossil fuels and to resolve the current energy crisis. Integrating wind energy into a large-scale electric grid presents a significant challenge due to the high intermittency and nonlinear behavior of wind power. Accurate wind power forecasting is essential for safe and efficient integration into the grid system. Many prediction models have been developed to predict the uncertain and nonlinear time series of wind power, but most neglect the use of Bayesian optimization to optimize the hyperparameters while training deep learning algorithms. The efficiency of grid search strategies decreases as the number of hyperparameters increases, and computation time complexity becomes an issue. This paper presents a robust and optimized long-short term memory network for forecasting wind power generation in the day ahead in the context of Ethiopia’s renewable energy sector. The proposal uses Bayesian optimization to find the best hyperparameter combination in a reasonable computation time. The results indicate that tuning hyperparameters using this metaheuristic prior to building deep learning models significantly improves the predictive performances of the models. The proposed models were evaluated using MAE, RMSE, and MAPE metrics, and outperformed both the baseline models and the optimized gated recurrent unit architecture.

The rapid expansion of wind power gives rise to a number of challenges for power system operators and electricity market participants. The key operational challenge is to efficiently handle the uncertainty and variability of wind power when balancing supply and demand in ths system. In this report, we analyze how wind power forecasting can serve as an efficient tool toward this end. We discuss the current status of wind power forecasting in U.S. electricity markets and develop several methodologies and modeling tools for the use of wind power forecasting in operational decisions, from the perspectives of the system operator as well as the wind power producer. In particular, we focus on the use of probabilistic forecasts in operational decisions. Driven by increasing prices for fossil fuels and concerns about greenhouse gas (GHG) emissions, wind power, as a renewable and clean source of energy, is rapidly being introduced into the existing electricity supply portfolio in many parts of the world. The U.S. Department of Energy (DOE) has analyzed a scenario in which wind power meets 20% of the U.S. electricity demand by 2030, which means that the U.S. wind power capacity would have to reach more than 300 gigawatts (GW). The European Union is pursuing a target of 20/20/20, which aims to reduce greenhouse gas (GHG) emissions by 20%, increase the amount of renewable energy to 20% of the energy supply, and improve energy efficiency by 20% by 2020 as compared to 1990. Meanwhile, China is the leading country in terms of installed wind capacity, and had 45 GW of installed wind power capacity out of about 200 GW on a global level at the end of 2010. The rapid increase in the penetration of wind power into power systems introduces more variability and uncertainty in the electricity generation portfolio, and these factors are the key challenges when it comes to integrating wind power into the electric power grid. Wind power forecasting (WPF) is an important tool to help efficiently address this challenge, and significant efforts have been invested in developing more accurate wind power forecasts. In this report, we document our work on the use of wind power forecasting in operational decisions.

As the global economy rapidly develops, energy consumption and carbon dioxide emissions have increased annually, prompting countries to strive for carbon neutrality by 2050. Accurate wind power forecasting can aid power system dispatch departments to obtain wind farms’ output and improve the power system’s new energy absorption capacity by coordinating multiple power generation resources. To this end, this study proposes a novel method for wind power forecasting: the Generative Adversarial Network method-based Deep Q Neural Network (GDQN). Wind power is a nonlinear model with random characteristics like dynamics and uncertainty. The GDQN generates wind power data similar to historical wind power data, solving the problem of insufficient wind power data samples by developing adversarial networks. The deep Q-learning network is then utilized to predict future wind power data. The experimental results based on the actual test of the total power generated by all wind turbines in a complete wind farm indicate that the proposed GDQN method can significantly reduce the Mean Absolute Percentage Error (MAPE %) of wind power forecasting, as compared to other commonly used methods in wind power forecasting.

With the improvement of penetration rate of wind power in the power system, its volatility and intermittence bring new problems to the power grid. The accurate forecasting of wind power is an effective way to alleviate the impact on the power grid. In this paper, a novel wind power forecasting method is proposed. Firstly, a wind power forecasting model based on the bidirectional long short-term memory (BiLSTM) neural network is established to forecast the wind power according to the wind speed of numerical weather forecasting (NWF). Secondly, a wind power forecasting error time series model based on empirical mode decomposition (EMD) is established to decrease the forecasting error. Finally, this paper uses the real data to simulate and verify the proposed method. Evaluating by the root mean square error (RMSE), symmetric mean absolute percentage error (SMAPE), and Theil inequality coefficient (TIC), the simulation results show that the forecasting accuracy of the BiLSTM neural network model are 10.25%, 6.71% and 12.18% higher than LSTM model respectively. After correcting wind power forecasting error using the proposed time series model based on EMD, the accuracy of wind power forecasting is further improved.