Wind Power Forecasting Methods Based on Deep Learning: A Survey

Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure, temperature, roughness, and obstacles. As an effective method of high-dimensional feature extraction, deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design, such as adding noise to outputs, evolutionary learning used to optimize hidden layer weights, optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting. The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness, instantaneity and seasonal characteristics

Data-augmented sequential deep learning for wind power forecasting

Accurate wind power forecasting plays a critical role in the operation of wind parks and the dispatch of wind energy into the power grid. With excellent automatic pattern recognition and nonlinear mapping ability for big data, deep learning is increasingly employed in wind power forecasting. However, salient realities are that in-situ measured wind data are relatively expensive and inaccessible and correlation between steps is omitted in most multistep wind power forecasts. This paper is the first time that data augmentation is applied to wind power forecasting by systematically summarizing and proposing both physics-oriented and data-oriented time-series wind data augmentation approaches to considerably enlarge primary datasets, and develops deep encoder-decoder long short-term memory networks that enable sequential input and sequential output for wind power forecasting. The proposed augmentation techniques and forecasting algorithm are deployed on five turbines with diverse topographies in an Arctic wind park, and the outcomes are evaluated against benchmark models and different augmentations. The main findings reveal that on one side, the average improvement in RMSE of the proposed forecasting model over the benchmarks is 33.89%, 10.60%, 7.12%, and 4.27% before data augmentations, and increases to 40.63%, 17.67%, 11.74%, and 7.06%, respectively, after augmentations. The other side unveils that the effect of data augmentations on prediction is intricately varying, but for the proposed model with and without augmentations, all augmentation approaches boost the model outperformance from 7.87% to 13.36% in RMSE, 5.24% to 8.97% in MAE, and similarly over 12% in QR90. Finally, data-oriented augmentations, in general, are slightly better than physics-driven ones

Multiple decomposition-aided long short-term memory network for enhanced short-term wind power forecasting.

With the increasing penetration of grid-scale wind energy systems, accurate wind power forecasting is critical to optimizing their integration into the power system, ensuring operational reliability, and enabling efficient system asset utilization. Addressing this challenge, this study proposes a novel forecasting model that combines the long-short-term memory (LSTM) neural network with two signal decomposition techniques. The EMD technique effectively extracts stable, stationary, and regular patterns from the original wind power signal, while the VMD technique tackles the most challenging high-frequency component. A deep learning-based forecasting model, i.e. the LSTM neural network, is used to take advantage of its ability to learn from longer sequences of data and its robustness to noise and outliers. The developed model is evaluated against LSTM models employing various decomposition methods using real wind power data from three distinct offshore wind farms. It is shown that the two-stage decomposition significantly enhances forecasting accuracy, with the proposed model achieving R2 values up to 9.5% higher than those obtained using standard LSTM models

Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM

Offshore wind power is one of the fastest-growing energy sources worldwide, which is environmentally friendly and economically competitive. Short-term time series wind speed forecasts are extremely significant for proper and efficient offshore wind energy evaluation and in turn, benefit wind farm owner, grid operators as well as end customers. In this study, a Seasonal Auto-Regression Integrated Moving Average (SARIMA) model is proposed to predict hourly-measured wind speeds in the coastal/offshore area of Scotland. The used datasets consist of three wind speed time series collected at different elevations from a coastal met mast, which was designed to serve for a demonstration offshore wind turbine. To verify SARIMA’s performance, the developed predictive model was further compared with the newly developed deep-learning-based algorithms of Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM). Regardless of the recent development of computational power has triggered more advanced machine learning algorithms, the proposed SARIMA model has shown its outperformance in the accuracy of forecasting future lags of offshore wind speeds along with time series. The SARIMA model provided the highest accuracy and robust healthiness among all the three tested predictive models based on corresponding datasets and assessed forecasting horizons

Recurrent neural network with density-based clustering for group pattern detection in energy systems

This research explores a new direction in power system technology and develops a new framework for pattern group discovery from large power system data. The efficient combination between the recurrent neural network and the density-based clustering enables to find the group patterns in the power system. The power system data is first collected in multiple time series data and trained by the recurrent neural network to find simple patterns. The simple patterns are then studied, and analyzed with the density-based clustering algorithm to identify the group of patterns. The solution was analyzed in two case studies (pattern discovery and outlier detection) specifically for power systems. The results show the advantages of the proposed framework and a clear superiority compared to state-of-the-art approaches, where the average correlation in group pattern detection is 90% and in group outlier detection more than 80% of both true-positive and true-negative rates.publishedVersio

Recurrent neural network with density-based clustering for group pattern detection in energy systems

This research explores a new direction in power system technology and develops a new framework for pattern group discovery from large power system data. The efficient combination between the recurrent neural network and the density-based clustering enables to find the group patterns in the power system. The power system data is first collected in multiple time series data and trained by the recurrent neural network to find simple patterns. The simple patterns are then studied, and analyzed with the density-based clustering algorithm to identify the group of patterns. The solution was analyzed in two case studies (pattern discovery and outlier detection) specifically for power systems. The results show the advantages of the proposed framework and a clear superiority compared to state-of-the-art approaches, where the average correlation in group pattern detection is 90% and in group outlier detection more than 80% of both true-positive and true-negative rates.publishedVersio