Forecasting Of Wind Power Generation Research Articles

Solar and wind power generation forecasts using elastic net in time-varying forecast combinations

Precise renewable energy feed-in forecasts are essential for an effective and efficient integration of renewables into energy systems, and research contributions that help to reduce the uncertainty related to renewables are in high demand. This importance will increase in the future, as renewable energies are the world’s fastest growing electricity generation capacities. Forecast combinations have been empirically proven to outperform individual forecasting models in many disciplines. Our work uses an elastic net method, with cross-validation and rolling window estimation, in the context of renewable energy forecasts. Namely, the forecast combinations are obtained using regional data from Germany for both solar photovoltaic and wind feed-in during the period 2010–2018, with quarter-hourly frequency. The dynamic elastic net estimation, preceded by dynamic data pre-processing, improves forecasting accuracy for both photovoltaic and wind power feed-in forecasts. Moreover, our forecasting framework outperforms benchmarks such as simple average and individual forecasts. Our forecasting framework can be applied widely to estimate renewable power in other countries, systems, or individual power plants.

A novel asexual-reproduction evolutionary neural network for wind power prediction based on generative adversarial networks

Accurate forecasts of wind power generation are essential for the operation of wind farms. But for the newly developed stations, it is difficult to make accurate prediction because there are no sufficient historical data available. It will thus be interesting to explore new data augmentation and prediction modeling approach adaptive to such new-built wind farms. In this regard, a novel asexual-reproduction evolutionary neural network (ARENN) for short-term wind power prediction based on Wasserstein generative adversarial network with gradient penalty (WGANGP) and ensemble empirical mode decomposition (EEMD) is presented in this paper. To solve the dilemma that new-built wind farms lack sufficient wind power data, the WGANGP is first applied to generate realistic data with a similar distribution of real data to augment the training dataset, which is further decomposed into a series of more stable subsequences by the EEMD so as to reduce the prediction difficulty of the machine learning model. In this study, a novel ARENN prediction model is developed to make the short-term wind power prediction, in which an asexual-reproduction evolutionary approach is first proposed to optimize the neural network based on a set of different loss functions that facilitate the population of network parameters approximating to the global optimum along different error surfaces in the evolutionary process. The proposed approach is validated on the data collected from the wind farm located in Spain and the predicted results demonstrate the advantage of our proposed approach over other methods involved in this study.

Forecasting the wind power generation in China by seasonal grey forecasting model based on collaborative optimization

Renewable energy represented by wind energy plays an increasingly important role in China’s national energy system. The accurate prediction of wind power generation is of great significance to China’s energy planning and power grid dispatch. However, due to the late development of the wind power industry in China and the lag of power enterprise information, there are little historical data available at present. Therefore, the traditional large sample prediction method is difficult to be applied to the forecasting of wind power generation in China. For this kind of small sample and poor information problem, the grey prediction method can give a good solution. Thus, given the seasonal and long memory characteristics of the seasonal wind power generation, this paper constructs a seasonal discrete grey prediction model based on collaborative optimization. On the one hand, the model is based on moving average filtering algorithm to realize the recognition of seasonal and trend features. On the other hand, based on the optimization of fractional order and initial value, the collaborative optimization of trend and season is realized. To verify the practicability and accuracy of the proposed model, this paper uses the model to predict the quarterly wind power generation of China from 2012Q1 to 2020Q1, and compares the prediction results with the prediction results of the traditional GM(1,1) model, SGM(1,1) model and Holt-Winters model. The results are shown that the proposed model has a strong ability to capture the trend and seasonal fluctuation characteristics of wind power generation. And the long-term forecasts are valid if the existing wind power expansion capacity policy is maintained in the next four years. Based on the forecast of China’s wind power generation from 2021Q2 to 2024Q2 in the future, it is predicted that China’s wind power generation will reach 239.09 TWh in the future, which will be beneficial to the realization of China’s energy-saving and emission reduction targets.

Medium and long term wind power generation forecast based on OWA combined model and Markov chain

Wind energy, as one of the renewable energies with the most potential for development, has been widely concerned. At the same time, the medium and long term wind power prediction is easily affected by many factors. In order to avoid the instability of a single model, this paper first builds a self-adaptive filtering model and a gray model with parameter optimized by teaching learning-based optimization (TLBO), then uses ordered weighted averaging (OWA) to assign weights to two single models, and finally uses Markov chain to modify the prediction results to further improve the prediction accuracy.

A novel approach to multi-horizon wind power forecasting based on deep neural architecture

In recent years, renewable energy sources have been installed in large numbers. Wind power in particular, a technology with very high potential, has become a significant source of energy in most power grids. However, wind power generation forecasting and scheduling remain very difficult tasks due to the uncertainty and stochastic behaviour of wind speed. This work provides a novel, powerful tool for wind power forecasting based on neural expansion analysis for time series forecasting (N-BEATS), a deep neural network approach. N-BEATS was designed as an easy-to-implement approach to solving non-linear stochastic time series forecasting problems. Additionally, a loss function is tailored to wind power forecasting to confront the issue of forecast bias. The results are compared with established models, such as statistical and machine learning approaches as well as hybrid models, using the real-world wind power data from 15 different European countries as input. Comprehensive and accurate results are obtained during this work, showing that this methodology can easily compete with other approaches and even outperform them in terms of accuracy in most cases. Additionally, the tailored loss function reduces the error significantly. The N-BEATS architecture is further customized to deliver decomposed components such as trend and seasonality, yielding interpretable outputs. These findings constitute considerable progress and provide support for decision makers.

Sub-seasonal forecasts of demand and wind power and solar power generation for 28 European countries

Abstract. Electricity systems are becoming increasingly exposed to weather. The need for high-quality meteorological forecasts for managing risk across all timescales has therefore never been greater. This paper seeks to extend the uptake of meteorological data in the power systems modelling community to include probabilistic meteorological forecasts at sub-seasonal lead times. Such forecasts are growing in skill and are receiving considerable attention in power system risk management and energy trading. Despite this interest, these forecasts are rarely evaluated in power system terms, and technical barriers frequently prohibit use by non-meteorological specialists. This paper therefore presents data produced through a new EU climate services programme Subseasonal-to-seasonal forecasting for Energy (S2S4E). The data correspond to a suite of well-documented, easy-to-use, self-consistent daily and nationally aggregated time series for wind power, solar power and electricity demand across 28 European countries. The data are accessible from https://doi.org/10.17864/1947.275 (Gonzalez et al., 2020). The data include a set of daily ensemble reforecasts from two leading forecast systems spanning 20 years (ECMWF, an 11-member ensemble, with twice-weekly starts for 1996–2016, totalling 22 880 forecasts) and 11 years (NCEP, a 12-member lagged-ensemble, constructed to match the start dates from the ECMWF forecast from 1999–2010, totalling 14 976 forecasts). The reforecasts contain multiple plausible realisations of daily weather and power data for up to 6 weeks in the future. To the authors’ knowledge, this is the first time a fully calibrated and post-processed daily power system forecast set has been published, and this is the primary purpose of this paper. A brief review of forecast skill in each of the individual primary power system properties and a composite property is presented, focusing on the winter season. The forecast systems contain additional skill over climatological expectation for weekly-average forecasts at extended lead times, though this skill depends on the nature of the forecast metric considered. This highlights the need for greater collaboration between the energy and meteorological research communities to develop applications, and it is hoped that publishing these data and tools will support this.

Integrative Density Forecast and Uncertainty Quantification of Wind Power Generation

The volatile nature of wind power generation creates challenges in achieving secure power grid operations. It is, therefore, necessary to accurately predict wind power and its uncertainty quantification. Wind power forecasting usually depends on wind speed prediction and the wind-to-power conversion process. However, most current wind power prediction models only consider portions of the uncertainty. This paper develops an integrative framework for predicting wind power density, considering uncertainties arising from both wind speed prediction and the wind-to-power conversion process. Specifically, we model wind speed using the inhomogeneous Geometric Brownian Motion and convert the wind speed prediction density into the wind power density in a closed-form. The resulting wind power density allows quantifying prediction uncertainties through prediction intervals. To forecast the power output, we minimize the expected prediction cost with (unequal) penalties on the overestimation and underestimation. We show the predictive power of the proposed approach using data from multiple operating wind farms located at different sites.

Very short-term forecasting of wind power generation using hybrid deep learning model

Accurate forecasting of wind power generation plays a key role in improving the operation and management of a power system network and thereby its reliability and security. However, predicting wind power is complex due to the existence of high non-linearity in wind speed that eventually relies on prevailing weather conditions. In this paper, a novel hybrid deep learning model is proposed to improve the prediction accuracy of very short-term wind power generation for the Bodangora wind farm located in New South Wales, Australia. The hybrid model consists of convolutional layers, gated recurrent unit (GRU) layers and a fully connected neural network. The convolutional layers have the ability to automatically learn complex features from raw data while the GRU layers are capable of directly learning multiple parallel sequences of input data. The data sets of 5-min intervals from the wind farm are used in case studies to demonstrate the effectiveness of the proposed model against other advanced existing models, including long short-term memory, GRU, autoregressive integrated moving average and support vector machine, which are tuned to optimise outcome. To further evaluate the efficacy of the proposed model, another data set taken from the Capital wind farm in Australia is used. It is observed that the hybrid deep learning model exhibits superior performance in both the data sets over other forecasting models to improve the accuracy of wind power forecasting, numerically for the Bodangora wind farm, up to 1.59 per cent in mean absolute error, 3.73 per cent in root mean square error and 8.13 per cent in mean absolute percentage error.

An improved deep belief network based hybrid forecasting method for wind power

The stochastic nature of wind speed hinders the forecasting of wind power generation. To improve the accuracy of wind power forecasting and effectively utilize the capability of principal component analysis (PCA) to process high-dimensional data, and take the advantages of deep belief network (DBN) to process complex data and spatial correlation (SC) in considering geographical position and terrain, a hybrid forecasting method using numerical weather prediction (NWP) is presented in this paper. First, an improved DBN is proposed by introducing Gaussian-Bernoulli restricted Boltzmann machine, and an adaptive learning step technique is applied to improve the convergence speed. Furthermore, the principal components are extracted from high-dimension original data by PCA, which are further input to the improved DBN. Then, a wind speed correction model is established to address the inaccuracy of NWP. Moreover, the output of the target site is forecasted using the data of its neighboring observation sites. Finally, the advantages of the above methods are combined, and the sliding window strategy is utilized to adaptively update the training data. The simulation results verify the effectiveness of the proposed improved DBN and the hybrid method compared to the traditional DBN, the corresponding average increase of forecasting accuracy is 15.8975% and 29.3725%, respectively.

Wind power generation forecast by coupling numerical weather prediction model and gradient boosting machines in Yahyalı wind power plant

Forecasting of the wind speed and power generation for a wind farm has always been quite challenging and has importance in terms of balancing the electricity grid and preventing energy imbalance penalties. This study focuses on creating a hybrid model that uses both numerical weather prediction model and gradient boosting machines (GBM) for wind power generation forecast. Weather Research and Forecasting (WRF) model with a low spatial resolution is used to increase temporal resolutions of the computed new or existing variables whereas GBM is used for downscaling purposes. The results of the hybrid model have been compared with the outputs of a stand-alone WRF which is well configured in terms of physical schemes and has a high spatial resolution for Yahyalı wind farm over a complex terrain located in Turkey. Consequently, the superiority of the hybrid model in terms of both performance indicators and computational expense in detail is shown.

Optimal uncertainty-guided neural network training

The neural network (NN)-based direct uncertainty quantification (UQ) methods have achieved the state of the art performance since the first inauguration, known as the lower–upper-bound estimation (LUBE) method. However, currently-available cost functions for uncertainty guided NN training are not always converging, and all converged NNs do not generate optimized prediction intervals (PIs). In recent years researchers have proposed different quality criteria for PIs that raise a question about their relative effectiveness. Most of the existing cost functions of uncertainty guided NN training are not customizable, and the convergence of the NN training is uncertain. Therefore, in this paper, we propose a highly customizable smooth cost function for developing NNs to construct optimal PIs. The method computes the optimized average width of PIs, PI-failure distances, and the PI coverage probability (PICP) for the test dataset. We examine the performance of the proposed method for wind power generation, electricity demand, and temperature forecast datasets. Results show that the proposed method reduces variation in the quality of PIs, accelerates the training, and improves convergence probability from 99.2% to 99.8%.

Short-term Power Forecast of Wind Power Generation Based on Genetic Algorithm Optimized Neural Network

With the rapid development of economy, human consumption of fossil energy has caused a global energy crisis. Nowadays, countries all over the world are making great efforts to develop clean energy. Wind energy as one of the pollution-free, renewable and high-quality clean energy, has attracted widespread attention. However, wind power generation is easily affected by natural factors and is instable. In order to improve the quality of wind power, this paper analyzes the influencing factors of wind power, studies the prediction method of wind power forecasting, and uses genetic algorithm optimization neural network to forecast the wind power of a wind farm in Northwest China, which may provide some reference for the power generation and grid connection of wind power plants.

Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend

To sustain a clean environment by reducing fossil fuels-based energies and increasing the integration of renewable-based energy sources, i.e., wind and solar power, have become the national policy for many countries. The increasing demand for renewable energy sources, such as wind, has created interest in the economic and technical issues related to the integration into the power grids. Having an intermittent nature and wind generation forecasting is a crucial aspect of ensuring the optimum grid control and design in power plants. Accurate forecasting provides essential information to empower grid operators and system designers in generating an optimal wind power plant, and to balance the power supply and demand. In this paper, we present an extensive review of wind forecasting methods and the artificial neural network (ANN) prolific in this regard. The instrument used to measure wind assimilation is analyzed and discussed, accurately, in studies that were published from May 1st, 2014 to May 1st, 2018. The results of the review demonstrate the increased application of ANN into wind power generation forecasting. Considering the component limitation of other systems, the trend of deploying the ANN and its hybrid systems are more attractive than other individual methods. The review further revealed that high forecasting accuracy could be achieved through proper handling and calibration of the wind-forecasting instrument and method.

A new short-term wind speed forecasting method based on fine-tuned LSTM neural network and optimal input sets

In recent years, clean energies, such as wind power have been developed rapidly. Especially, wind power generation becomes a significant source of energy in some power grids. On the other hand, based on the uncertain and non-convex behavior of wind speed, wind power generation forecasting and scheduling may be very difficult. In this paper, to improve the accuracy of forecasting the short-term wind speed, a hybrid wind speed forecasting model has been proposed based on four modules: crow search algorithm (CSA), wavelet transform (WT), Feature selection (FS) based on entropy and mutual information (MI), and deep learning time series prediction based on Long Short Term Memory neural networks (LSTM). The proposed wind speed forecasting strategy is applied to real-life data from Sotavento that is located in the south-west of Europe, in Galicia, Spain, and Kerman that is located in the Middle East, in the southeast of Iran. The presented numerical results demonstrate the efficiency of the proposed method, compared to some other existing wind speed forecasting methods.

A hybrid model of generalized regression neural network and radial basis function neural network for wind power forecasting in Indian wind farms

Wind power generation is a major part of renewable energy but it suffers from uncertainty and variable nature of wind speed. In conquering the uncertainty, forecasting of wind power generation emerges as best solution. Accordingly, wind power forecasting assists the power grid to increase the grid reliability. In this paper, a generalized regression neural network (GRNN), a neural network of radial basis function (RBFN) and a hybrid of GRNN and RBFN are applied for estimation of wind power and their performance is compared in respect to short-term wind power forecasting. In comparison to RBFN, GRNN has simple and static architecture. Historical wind power and meteorological wind speed data of the year 2014 from Indian wind farms are employed for training and testing the neural networks. The research work carried in this article interprets that GRNN has consistently performed better than RBFN. The hybrid GRNN-RBFN has also depicted good accuracy in a week ahead wind power forecasting. In such cases, either GRNN or RBFN predicts with high errors the hybrid GRNN-RBFN is helpful in accurate wind power forecasting. The errors are calculated in terms of MAPE & RMSE. For GRNN approach MAPE ranges from 0.48 % to 10.53 % and RMSE ranges from 0.01 KW to 3.31 KW. Whereas the maximum MAPE is 25.6 % for RBFN and the maximum RMSE is much higher i.e 11.61KW. In fact, for any forecasting approach, reliability is a major concern. The work carried also includes reliability analysis of all three forecasting models proposed. To ensure the best performance of GRNN, confidence intervals of MAPE are computed for all three models implemented. Among all, GRNN is well perceived to be the best one with narrowest confidence intervals.

An Efficient Method for Wind Power Generation Forecasting by LSTM in Consideration of Overfitting Prevention

This paper proposes an efficient method for wind power generation forecasting by Long Short Term Memory (LSTM) of Deep Neural Network (DNN). It is one of recurrent neural networks that make use of past output of the network, but replaces hidden layers of the conventional networks with the LSTM Block with memory and three gates of input, output and forget. Artificial and Deep Neural Networks are inclined to overfit leaning data in learning process. This paper proposes a modified LSTM that considers to prevent LSTM from overfitting with two strategies. One is Dropout to exclude some nodes randomly and change network topology while the other is Weight Decay that evaluates smaller weights between neurons. The effectiveness of the proposed method is demonstrated for real data of wind power generation.

A novel wind power probabilistic forecasting approach based on joint quantile regression and multi-objective optimization

Probabilistic forecasts of wind power generation play a significant role in operation management and decision-making for wind power producers and power system operators. This study introduces a novel joint quantile model based on the use of Mercer’s kernels in the vector-valued RKHSs (Reproducing Kernel Hilbert Spaces) for the nonparametric probabilistic forecasts of wind power. The proposed approach takes the autocorrelation of the (nonlinearly) transformed input and output into consideration and improves the model flexibility by constructing composite kernels. Besides, the model is optimized by a new meta-heuristics algorithm named Multi-Objective Salp Swarm Optimization Algorithm (MSSA), which mathematically models and mimics the behavior of salp swarms for solving multiple objective optimization problems. Under this model framework, several conditional quantiles of wind power can be estimated and predicted at the same time. Moreover, due to the joint estimation process, the problem of quantile curve crossing can be effectively weakened during the process of model training. The qualitative and quantitative performances of the proposed method are tested and verified on a set of real case studies. The results show that the proposed algorithm provides superior outputs compared to the well-known and recent algorithms in the literature.

Optimising the use of ensemble information in numerical weather forecasts of wind power generation

Electricity generation output forecasts for wind farms across Europe use numerical weather prediction (NWP) models. These forecasts influence decisions in the energy market, some of which help determine daily energy prices or the usage of thermal power generation plants. The predictive skill of power generation forecasts has an impact on the profitability of energy trading strategies and the ability to decrease carbon emissions. Probabilistic ensemble forecasts contain valuable information about the uncertainties in a forecast. The energy market typically takes basic approaches to using ensemble data to obtain more skilful forecasts. There is, however, evidence that more sophisticated approaches could yield significant further improvements in forecast skill and utility. In this letter, the application of ensemble forecasting methods to the aggregated electricity generation output for wind farms across Germany is investigated using historical ensemble forecasts from the European Centre for Medium-Range Weather Forecasting (ECMWF). Multiple methods for producing a single forecast from the ensemble are tried and tested against traditional deterministic methods. All the methods exhibit positive skill, relative to a climatological forecast, out to a lead time of at least seven days. A wind energy trading strategy involving ensemble data is implemented and produces significantly more profit than trading strategies based on single forecasts. It is thus found that ensemble spread is a good predictor for wind electricity generation output forecast uncertainty and is extremely valuable at informing wind energy trading strategy.

Analysis of forecasted meteorological data (NWP) for efficient spatial forecasting of wind power generation

This paper presents a first step towards developing a method and an IT system for spatial forecasting of energy generation from renewable energy sources. Spatial forecasting is understood here as simultaneous forecasting for both large (e.g. country) and small (e.g. district) areas. Such approach is very useful to different stakeholders of the power industry, and especially to system operators. The first step mentioned above was an analysis of Numerical Weather Prediction (NWP) data to find out to what extent one can reduce the quantity of NWP data. The impact of reduced data on the quality of predictions was also investigated. The paper comprises three main parts: a literature review, statistical analysis of NWP data and tests of the possible reduction of NWP data employing different methods. Multiple linear regression models and artificial neural networks were the methods analysed for compensating of the reduced data. The authors have succeeded in identifying a way to process NWP data that provides an acceptable quality of wind power generation forecasts. This paper ends with conclusions.

A novel composite neural network based method for wind and solar power forecasting in microgrids

Nowadays, wind and solar power generation have a major impact in many microgrid hybrid energy systems based on their cost and pollution. On the other hand, accurate forecasting of wind and solar power generation is very important for energy management in microgrids. Therefore, a novel prediction interval model, consisting of several sections (wavelet transform, hybrid feature selection, Group Method of Data Handling neural network, and modified multi-objective fruit fly optimization algorithm), has been developed to short-term predict wind speed and solar irradiation and to investigate the energy consumption of microgrids. The renewables prediction and the energy consumption analysis have been applied to the Favignana island microgrid, in the south of Italy, using the new proposed point forecast model (Group Method of Data Handling neural network and modified fruit fly optimization algorithm – GMDHMFOA) and a Pareto analysis. The results show that the proposed interval prediction model has a good performance in different confidence levels (95%, 90%, and 85%) to predict wind speed and solar irradiation than other already existing methods. In addition, the proposed point forecast model (GMDHMFOA) has an acceptable error and better performance than the other ones commonly used in predicting energy consumption. Lastly, the monthly energy consumption in different stations of the microgrid can be predicted by using the proposed model and provides suitable solutions for energy management of the microgrid.