Forecasting Of Wind Power Generation Research Articles

Wind power forecasting with metaheuristic-based feature selection and neural networks

Accurate forecasting of wind power generation is crucial for ensuring a stable and efficient energy supply, reducing the environmental impact of energy production, and promoting a cleaner and more sustainable energy supply. Inaccurate forecasts can lead to a mismatch between wind power generation and energy demand, resulting in wasted energy, increased emissions, and reduced grid stability. Therefore, improving the accuracy of wind power generation forecasting is essential for optimizing energy storage and grid management, reducing the reliance on fossil fuels, decreasing greenhouse gas emissions, and promoting a more sustainable energy future. This study proposes an innovative approach to enhance wind power generation forecasting accuracy by leveraging the strengths of metaheuristic algorithms for feature selection and integrating them with Neural Networks (NN). Specifically, five distinct algorithms - Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Teaching-Learning-Based Optimization (TLBO), and Evolutionary Mating Algorithm (EMA) - are integrated with NN model to identify optimal feature subsets from a comprehensive dataset of 18 diverse features. The results show that the GA consistently outperforms other algorithms in selecting the most influential features, leading to improved precision in wind power predictions. Notably, the GA achieves the best root mean square error (RMSE) of 37.1837 and the best mean absolute error (MAE) of 18.6313, outperforming the other algorithms and demonstrating the importance of feature selection in improving the accuracy of wind power forecasting. This innovative framework advances the field of renewable energy forecasting and provides valuable insights into optimizing feature sets for improved predictions across diverse domains.

О влиянии неопределенностей на прогнозы ветрового ресурса и выработки электроэнергии

The annual power generation of a wind farm is one of the most important indicators determining the profitability of a wind power project. The methods used to estimate the annual power generation of a wind farm have to consider uncertainties at all stages of the project life cycle. When developing a financial model of a wind power project, it is required to consider information about uncertainties to reduce the error and increase the reliability of the project. Specialists of various countries are improving the efficiency of wind power plants, studying wind energy resources, as well as assessing the efficiency of their use. However, special studies do not pay due attention to the problem of assessing the impact of various uncertainties on the forecasts of wind resource and power generation. Two methods are proposed to calculate uncertainties. They are a deterministic method based on the assumption of independence of various uncertainties, and a Monte Carlo method that simulates the behavior of a physical system many times. The paper considers the uncertainties to be considered during the design of a wind farm and presents the variation ranges. The authors have presented the plots of power generation with various levels of probability of being reached or exceeded the total uncertainty for three variants. It is shown that considering the various uncertainties allows a power generation forecast to be made with increased accuracy. The results obtained are necessary to develop a wind measurement data processing model that allows us to forecast electricity generation by existing wind power plants with increased accuracy.

Hybrid model based on similar power extraction and improved temporal convolutional network for probabilistic wind power forecasting

Accurate wind power generation forecasting is of great significance to improve the operation of power system. Probabilistic forecasting has a higher application value in power grid because it can provide more abundant forecasting information than deterministic forecasting. In addition, multi-step forecasting can provide forecasting results in a longer time range, so that decision makers can make longer-term planning and strategic arrangements. In this paper, we propose a novel multi-step improved temporal convolutional network based on quadratic spline quantile function (MITCN-QSQF) for probabilistic wind power forecasting. First, we combine maximum information coefficient, Gaussian similarity and adaptive resample to propose an effective similar power generation feature extraction method (MGR) for power generation. Then the temporal convolutional network is improved to construct the multi-step time series forecasting model MITCN. By combining the proposed model and the powerful probabilistic forecasting method quadratic spline quantile function (QSQF), high-quality probabilistic forecasting of wind power is achieved. Through comprehensive simulations on an open-source dataset, the superiority and efficiency of the proposed method are verified. Compared with some advanced benchmarks, the proposed model can obtain more accurate deterministic and probabilistic forecasting results.

LSTM Deep Learning Techniques for Wind Power Generation Forecasting

Wind power generation forecasting is crucial for the optimal integration of renewable energy sources into power systems. Traditional forecasting methods often struggle to accurately predict wind energy production due to the complex and nonlinear relationships between wind speed, weather parameters, and power output. In recent years, deep learning techniques have emerged as promising alternatives for wind power forecasting. This conference paper provides a comprehensive review of deep learning techniques, with a specific focus on Long Short-Term Memory (LSTM) networks, for short-term wind power generation forecasting. Leveraging insights from recent research and empirical evaluations, this paper explores the effectiveness of LSTM networks in capturing temporal dependencies in wind data and improving prediction accuracy. The review highlights the potential of LSTM-based models to enhance the integration of wind energy into power systems and provides guidance for future research in this area.

BERT4ST:: Fine-tuning pre-trained large language model for wind power forecasting

Accurate forecasting of wind power generation is essential for ensuring power safety, scheduling various energy sources, and improving energy utilization. However, the elusive nature of wind, influenced by various meteorological and geographical factors, greatly complicates the wind power forecasting task. To improve the forecasting accuracy of wind power (WP), we propose a BERT-based model for spatio-temporal forecasting (BERT4ST), which is the first approach to fine-tune a large language model for the spatio-temporal modeling of WP. To deal with the inherent characteristics of WP, BERT4ST exploits the individual spatial and temporal dependency of patches and redesigns a set of spatial and temporal encodings. By well analyzing the connection between bidirectional attention networks and WP spatio-temporal data, BERT4ST employs a pre-trained BERT encoder as the backbone network to learn the individual spatial and temporal dependency of patches of WP data. Additionally, BERT4ST fine-tunes the pre-trained backbone in a multi-stage manner, i.e., first aligning the language model with the spatio-temporal data and then fine-tuning the downstream tasks while maintaining the stability of the backbone network. Experimental results demonstrate that our BERT4ST achieves desirable performance compared to some state-of-the-art methods.

Branch error reduction criterion-based signal recursive decomposition and its application to wind power generation forecasting.

Due to the ability of sidestepping mode aliasing and endpoint effects, variational mode decomposition (VMD) is usually used as the forecasting module of a hybrid model in time-series forecasting. However, the forecast accuracy of the hybrid model is sensitive to the manually set mode number of VMD; neither underdecomposition (the mode number is too small) nor over-decomposition (the mode number is too large) improves forecasting accuracy. To address this issue, a branch error reduction (BER) criterion is proposed in this study that is based on which a mode number adaptive VMD-based recursive decomposition method is used. This decomposition method is combined with commonly used single forecasting models and applied to the wind power generation forecasting task. Experimental results validate the effectiveness of the proposed combination.

Artificial-intelligence-enabled dynamic demand response system for maximizing the use of renewable electricity in production processes

AbstractThe transition towards renewable electricity provides opportunities for manufacturing companies to save electricity costs through participating in demand response programs. End-to-end implementation of demand response systems focusing on manufacturing power consumers is still challenging due to multiple stakeholders and subsystems that generate a heterogeneous and large amount of data. This work develops an approach utilizing artificial intelligence for a demand response system that optimizes industrial consumers’ and prosumers’ production-related electricity costs according to time-variable electricity tariffs. It also proposes a semantic middleware architecture that utilizes an ontology as the semantic integration model for handling heterogeneous data models between the system’s modules. This paper reports on developing and evaluating multiple machine learning models for power generation forecasting and load prediction, and also mixed-integer linear programming as well as reinforcement learning for production optimization considering dynamic electricity pricing represented as Green Electricity Index (GEI). The experiments show that the hybrid auto-regressive long-short-term-memory model performs best for solar and convolutional neural networks for wind power generation forecasting. Random forest, k-nearest neighbors, ridge, and gradient-boosting regression models perform best in load prediction in the considered use cases. Furthermore, this research found that the reinforcement-learning-based approach can provide generic and scalable solutions for complex and dynamic production environments. Additionally, this paper presents the validation of the developed system in the German industrial environment, involving a utility company and two small to medium-sized manufacturing companies. It shows that the developed system benefits the manufacturing company that implements fine-grained process scheduling most due to its flexible rescheduling capacities.

Comparative study of time-series forecasting models for wind power generation in Gujarat, India

The rapid rate of transformation in the power sector of India has placed a significant emphasis on robust grids and distributed generation units. The observable shift in the energy sector, especially in wind and solar energy, also requires smooth integration of Distributed Generation units with the existing power grid. Precise wind power generation forecast, therefore, becomes an important and complex task for the strategic planning and management of the systems. We, thus, aim towards a system that can actually provide precise wind power forecasts by applying machine learning techniques. This work proposes a comparative and comprehensive analysis of Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Autoregressive Integrated Moving Average (ARIMA) model. The experimentations and modelling are performed considering meteorological and historical power generation data. The study is concentrated in Kutch, Gujarat and is validated on the data collected from the Central Electricity Authority (CEA), India for power generation data and weather data collected from regional weather centres. The findings show that ARIMA outperforms the other models for non-linear data in multivariate analysis, with a MAPE score of less than 5.5 on the prediction dataset.

Machine Learning-Based Probabilistic Forecasting of Wind Power Generation: A Combined Bootstrap and Cumulant Method

Probabilistic forecasting provides complete probability information of renewable generation and load, which assists the diverse decision-making tasks in power systems under uncertainties. Conventional machine learning-based probabilistic forecasting methods usually consider the predictive uncertainty following prior distributional assumptions. This paper develops a novel combined bootstrap and cumulant (CBC) method to generate nonparametric predictive distribution using higher order statistics for probabilistic forecasting. The CBC method successfully integrates machine learning with conditional moments and cumulants to describe the overall predictive uncertainty. A bootstrap-based conditional moment estimation method is proposed to quantify both the epistemic and aleatory uncertainties involved in machine learning. Higher order cumulants are utilized for overall uncertainty quantification based on the estimated conditional moments with its unique additivity. Three types of series expansions including Gram-Charlier, Edgeworth, and Cornish-Fisher expansions are adopted to improve the overall performance and the generalization ability. Comprehensive numerical studies using the actual wind power data validate the effectiveness of the proposed CBC method.

Least Squares-based Optimal Reconciliation Method for Hierarchical Forecasts of Wind Power Generation

Wind power generation is hierarchically organized and can be aggregated/disaggregated based on geography and electrical network structure. Forecasts are required at all levels of such a hierarchy for different operational requirements, which are also known as “hierarchical forecasts”. In this paper, we propose a new method to produce hierarchical wind power forecasts which performs better than the conventional top-down or bottom-up method. At first, wind power generation at all levels of the hierarchy is independently forecasted using state-of-the-art techniques. Then, a least squares regression model is used to optimally reconcile these base forecasts. Adjusted forecasts are as close as possible to base forecasts but also satisfy the requirement of the aggregate consistency (i.e., the lower-level forecasts add up exactly to the higher-level forecasts). Case studies validate the effectiveness of the proposed approach for very short-term wind power forecasts. The accuracy improvement of reconciled forecasts over benchmarks is confirmed at all levels of the hierarchy, regardless of forecast horizons and hierarchical structures.

Powerformer: A temporal-based transformer model for wind power forecasting

Wind Power Forecasting has emerged as a critical and dynamic research area in response to the growing demand for renewable energy. The unpredictable and stochastic nature of wind conditions, encompassing factors such as wind speed, wind direction, air temperature, and barometric pressure, poses unique challenges for accurate forecasting of wind power generation. Reliable wind power generation forecasts are essential for optimizing energy grid management, ensuring grid stability, and facilitating the integration of wind energy with existing power systems. To address these challenges, this research introduces Powerformer, a Transformer-based model designed to improve the accuracy of wind power prediction. Powerformer utilizes the infrastructure of the Transformer with innovative modifications to address the complexity of wind power prediction, enhancing temporal feature extraction capabilities while reducing complexity. The research in this study includes a comprehensive set of experiments, revealing that Powerformer achieves superior results among all models. Furthermore, the model exhibits stronger robustness, as confirmed through a series of ablation experiments validating the reasonableness of the model design.

Mind the (spectral) gap: how the temporal resolution of wind data affects multi-decadal wind power forecasts

To forecast wind power generation in the scale of years to decades, outputs from climate models are often used. However, one major limitation of the data projected by these models is their coarse temporal resolution—usually not finer than three hours and sometimes as coarse as one month. Due to the non-linear relationship between wind speed and wind power, and the long forecast horizon considered, small changes in wind speed can result in big changes in projected wind power generation. Our study indicates that the distribution of observed 10 min wind speed data is relatively well preserved using three- or six-hourly instantaneous values. In contrast, daily or monthly values, as well as any averages, including three-hourly averages, are almost never capable of preserving the distribution of the underlying higher resolution data. Assuming that climate models behave in a similar manner to observations, our results indicate that output at three-hourly or six-hourly temporal resolution is high enough for multi-decadal wind power generation forecasting. In contrast, wind speed projections of lower temporal resolution, or averages over any time range, should be handled with care.

Hybrid deep learning and quantum-inspired neural network for day-ahead spatiotemporal wind speed forecasting

Wind is an essential, clean and sustainable renewable source of energy; however, wind speed is stochastic and intermittent. Accurate wind power generation forecasts are required to ensure that power generation can be scheduled economically and securely. This work proposes a hybrid deep learning technique that incorporates a quantum-inspired neural network to predict wind speeds 24 h in advance. An innovative neural network technique is implemented by cascading parallel convolutional neural networks (CNNs) with a long short-term memory (LSTM) and a quantum-inspired neural network (QINN). The proposed hybrid model is optimized using two iterative loops: the outer loop is implemented by using quantum particle swarm optimization (QPSO) to tune the structure of model and other hyperparameters/parameters. The inner loop uses an Adam optimizer to tune the weights and biases of the proposed model. Spatiotemporal wind speeds at various locations provide the 2D input data. Simulation results reveal that the proposed method outperforms other methods for 24 h-ahead wind speed forecasting.

A dual spatio-temporal network for short-term wind power forecasting

The intricate spatial and temporal complexities inherent in wind speed data necessitate the utilization of spatio-temporal networks for accurate wind power generation forecasting. But these complexities are made worse by the constant presence of high-frequency disturbances and clear outliers in the spatial and temporal correlations of wind speed, which makes forecasting wind power very difficult. Additionally, the intricate topological architectures that underpin current models often result in superfluous computational operations and susceptibility to overfitting. In response to these formidable challenges, we introduce an innovative solution in the form of the dual spatio-temporal (DST) network. This paradigm, characterized by meticulous design, excels in acquiring precise and resilient spatio-temporal representations essential for wind power forecasting. This is possible because of the careful development of two types of spatial correlation layers: synchronous layers and asynchronous layers. Each type is fine-tuned to recognize different spatial patterns. Recognizing that spatial and temporal correlations change over time, our method explores spatio-temporal representations at many different scales. This makes the system more adaptable and robust. Notably, for effective noise mitigation over extended forecasting horizons, we judiciously integrate wavelet decomposition. Empirical validation, rigorously conducted across twelve wind power stations in Anhui Province, China, firmly attests to the efficacy of our proposed DST network. In summary, our innovation presents a promising avenue for advancing wind power forecasting, characterized by adept noise reduction and improved precision while concurrently optimizing computational efficiency in modeling intricate spatial–temporal dependencies.

Day-Ahead Parametric Probabilistic Forecasting of Wind and Solar Power Generation Using Bounded Probability Distributions and Hybrid Neural Networks

Day-Ahead Parametric Probabilistic Forecasting of Wind and Solar Power Generation Using Bounded Probability Distributions and Hybrid Neural Networks

Deep Learning for Variable Renewable Energy: A Systematic Review

In recent years, both fields, AI and VRE, have received increasing attention in scientific research. Thus, this article’s purpose is to investigate the potential of DL-based applications on VRE and as such provide an introduction to and structured overview of the field. First, we conduct a systematic literature review of the application of Artificial Intelligence (AI), especially Deep Learning (DL), on the integration of Variable Renewable Energy (VRE). Subsequently, we provide a comprehensive overview of specific DL-based solution approaches and evaluate their applicability, including a survey of the most applied and best suited DL architectures. We identify ten DL-based approaches to support the integration of VRE in modern power systems. We find (I) solar PV and wind power generation forecasting, (II) system scheduling and grid management, and (III) intelligent condition monitoring as three high potential application areas.

A Review of Modern Wind Power Generation Forecasting Technologies

The prediction of wind power output is part of the basic work of power grid dispatching and energy distribution. At present, the output power prediction is mainly obtained by fitting and regressing the historical data. The medium- and long-term power prediction results exhibit large deviations due to the uncertainty of wind power generation. In order to meet the demand for accessing large-scale wind power into the electricity grid and to further improve the accuracy of short-term wind power prediction, it is necessary to develop models for accurate and precise short-term wind power prediction based on advanced algorithms for studying the output power of a wind power generation system. This paper summarizes the contribution of the current advanced wind power forecasting technology and delineates the key advantages and disadvantages of various wind power forecasting models. These models have different forecasting capabilities, update the weights of each model in real time, improve the comprehensive forecasting capability of the model, and have good application prospects in wind power generation forecasting. Furthermore, the case studies and examples in the literature for accurately predicting ultra-short-term and short-term wind power generation with uncertainty and randomness are reviewed and analyzed. Finally, we present prospects for future studies that can serve as useful directions for other researchers planning to conduct similar experiments and investigations.

EMD-based gray combined forecasting model - Application to long-term forecasting of wind power generation

Wind power is the most promising renewable energy source after hydropower because of its mature technology and low price, and has great potential for carbon emission reduction. Long-term forecasts of its power generation can help power companies to develop operational plans, grid configuration and power dispatch, and can also provide a basis for the government to formulate energy and environmental policies. However, due to the characteristics of China's monsoon climate and wind power industry development, wind power generation data are characterized by nonlinear cycles and small data volume, which makes accurate prediction more difficult. To this end, this paper develops a new prediction model and applies it to the long-term prediction of wind power generation in China, and proposes some targeted policy recommendations based on the prediction results to promote the development of China's wind power industry.

Short-term forecasting of wind power generation using artificial intelligence

As global warming is increasing due to conventional sources the government and the private sectors introduce policies to minimize it, renewable energy has been developed and deployed because of these strategies. Among the various renewable energy sources, wind energy is the fastest-growing and cleanest energy resource in the world. However, predicting wind power is not easy due to the nonlinearity in wind speed that eventually depends on weather conditions. To reduce these issues improved forecasting models have been used to get the correct results and improve the performance and stability of the power system and thereby its reliability and security.In this work, two models are used to predict the “Output of Wind Turbine” to improve the prediction accuracy of short-term wind power generation. The two models namely the Gated Recurrent Unit (GRU) from the deep learning model and Autoregressive Integrated Moving Average (ARIMA) from Statistical Learning. The data used in this research is collected from the wind power plant, Located in Jhimpir Pakistan. This study compares the accuracy metrics of deep learning models and statistical models to determine which model is the most effective for producing wind power.The results are obtained by using python programming in Jupyter Notebook software and the accuracy metrics of each algorithm are compared with each other as a result Gated recurrent unit (GRU) is the best model among others with the least possible errors and high accuracy. i.e., up to 0.047 root mean square error, 0.89 coefficient of metrics, and 0.03 mean absolute error. Hence, due to its advanced features, then other deep learning, and statistical models the Gated recurrent unit (GRU) Model is suitable for the prediction of wind turbine output power.

Machine learning based renewable energy generation and energy consumption forecasting

ABSTRACT Renewable energy generation sources are the need of the hour to mitigate the challenges of fossil fuels, environmental impacts, large transmission and distribution losses, and variable consumption pattern. Renewable energy such as solar PV generation or wind generation is most unpredicted generation and depends upon weather constraints. A promising prediction of generation is a suitable solution for this problem. A machine learning-based conventional neural network algorithm is proposed to forecast the generation and calculate the errors in generation forecast. The generated energy is supplied to consumers, but the consumers consume electricity by their choice and an energy management model is required at the consumer end to supply the available predicted generation to develop a self-sustainable microgrid. The energy consumption at consumer end is essential to predict, and based on the predicted consumption at hourly bases, the cost model for the consumers is developed. The consumption profile is forecasted using long short-term memory as it is a good tool for time-series forecasting using available sequential load consumption data. The demand is forecasted and analyzed for selected load categories (i.e. fixed, non-shift-able, and shift-able loads). The evaluation matrix mean square error (MSE), mean absolute error, and root mean square error (RMSE) are used for the error calculations. The RMSE for solar and wind power generation forecasting have been 2.472 and 3.034, respectively. The RMSE for fixed, non-shiftable, and shiftable demand forecasting have been 0.027, 0.067, and 0.21, respectively. The results show that the proposed methods for predictions of solar, wind power generation and energy consumption forecast achieve better accuracy. The presented methodology can be implemented with energy storage for renewable energy production and demand management for energy from sustainable energy resources.