Learning Heterogeneous Features Jointly: A Deep End-to-End Framework for Multi-Step Short-Term Wind Power Prediction

Chen, Jinfu; Zhu, Qiaomu; Li, Hongyi; Zhu, Lin; Shi, Dongyuan; Li, Yinhong; Duan, Xianzhong; Liu, Yilu

doi:10.1109/tste.2019.2940590

Cited by 30 publications

(14 citation statements)

References 44 publications

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“…Recent papers addressing wind power forecasts could be broadly classified into 5 categories: papers focused on how to increase NWP accuracy [4][5][6][7][8], good-practice prediction guidelines [9][10][11], comparisons of accuracy across prediction models [12][13][14][15], hybrid and ensemble methods [16][17][18][19][20][21][22][23][24][25][26][27], and conventional methods improved by, among other things, preprocessing [28][29][30][31][32][33][34][35]. At this point, clear distinction should be made between hybrid, ensemble and improved models.…”

Section: Related Workmentioning

confidence: 99%

“…Hybridization [16][17][18][19][20][21] and parallelization [22][23][24][25][26][27] of prediction models use datarefining and error compensation, respectively, as an approach to maximize prediction accuracy. The most common bases for hybrid models in recent literature are ANNs [17][18][19]21] due to their generalization ability, while the most common hybrid add ons would be single optimization methods [16,18,20,21].…”

Section: Related Workmentioning

confidence: 99%

“…Ensemble models proposed in literature vary by the methods used and how output is aggregated. Studies pertaining to both switchable [22] and output-aggregated models [23][24][25][26][27] have been described in recent research papers. As for methods, researchers frequently use variations of regression trees [22][23][24][25].…”

Section: Related Workmentioning

confidence: 99%

“…As for methods, researchers frequently use variations of regression trees [22][23][24][25]. Another approach is proposed in [26]. The solution suggested there is based on parallelization of stacked autoencoders for advanced feature extraction.…”

Section: Related Workmentioning

confidence: 99%

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Hybrid and Ensemble Methods of Two Days Ahead Forecasts of Electric Energy Production in a Small Wind Turbine

et al. 2021

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The ability to forecast electricity generation for a small wind turbine is important both on a larger scale where there are many such turbines (because it creates problems for networks managed by distribution system operators) and for prosumers to allow current energy consumption planning. It is also important for owners of small energy systems in order to optimize the use of various energy sources and facilitate energy storage. The research presented here addresses an original, rarely predicted 48 h forecasting horizon for small wind turbines. This topic has been rather underrepresented in research, especially in comparison with forecasts for large wind farms. Wind speed forecasts with a 48 h horizon are also rarely used as input data. We have analyzed the available data to identify potentially useful explanatory variables for forecasting models. Eight sets with increasing data amounts were created to analyze the influence of the types and amounts of data on forecast quality. Hybrid, ensemble and single methods are used for predictions, including machine learning (ML) solutions like long short-term memory (LSTM), multi-layer perceptron (MLP), support vector regression (SVR) and K-nearest neighbours regression (KNNR). Original hybrid methods, developed for research of specific implementations and ensemble methods based on hybrid methods’ decreased errors of energy generation forecasts for small wind turbines in comparison with single methods. The “artificial neural network (ANN) type MLP as an integrator of ensemble based on hybrid methods” ensemble forecasting method incorporates an original combination of predictors. Predictions by this method have the lowest mean absolute error (MAE). In addition, this paper presents an original ensemble forecasting method, called “averaging ensemble based on hybrid methods without extreme forecasts”. Predictions by this method have the lowest root mean square error (RMSE) error among all tested methods. LSTM, a deep neural network, is the best single method, MLP is the second best one, while SVR, KNNR and, especially, linear regression (LR) perform less well. We prove that lagged values of forecasted time series slightly increase the accuracy of predictions. The same applies to seasonal and daily variability markers. Our studies have also demonstrated that using the full set of available input data and the best proposed hybrid and ensemble methods yield the lowest error. The proposed hybrid and ensemble methods are also applicable to other short-time generation forecasting in renewable energy sources (RES), e.g., in photovoltaic (PV) systems or hydropower.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Hybrid and Ensemble Methods of Two Days Ahead Forecasts of Electric Energy Production in a Small Wind Turbine

et al. 2021

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“…Artificial Intelligence (AI) technologies have been widely used in many areas, such as image recognition, speech recognition, self-driving cars, and fraud detection. Many researchers also propose AI-based methods for wind speed prediction [8], situational awareness [9,10], emergency control [11,12], and oscillation damping control [13]. In this paper, AI technologies provide a promising solution for adaptive RAS by mapping the operating conditions to the optimal corrective actions.…”

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confidence: 99%

Deep Learning-Based Adaptive Remedial Action Scheme with Security Margin for Renewable-Dominated Power Grids

et al. 2021

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The Remedial Action Scheme (RAS) is designed to take corrective actions after detecting predetermined conditions to maintain system transient stability in large interconnected power grids. However, since RAS is usually designed based on a few selected typical operating conditions, it is not optimal in operating conditions that are not considered in the offline design, especially under frequently and dramatically varying operating conditions due to the increasing integration of intermittent renewables. The deep learning-based RAS is proposed to enhance the adaptivity of RAS to varying operating conditions. During the training, a customized loss function is developed to penalize the negative loss and suggest corrective actions with a security margin to avoid triggering under-frequency and over-frequency relays. Simulation results of the reduced United States Western Interconnection system model demonstrate that the proposed deep learning–based RAS can provide optimal corrective actions for unseen operating conditions while maintaining a sufficient security margin.

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Deep Learning Algorithms for Wind Forecasting: An Overview

Lydia¹

2022

Artificial Intelligence for Renewable Energy Systems

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Learning Heterogeneous Features Jointly: A Deep End-to-End Framework for Multi-Step Short-Term Wind Power Prediction

Cited by 30 publications

References 44 publications

Hybrid and Ensemble Methods of Two Days Ahead Forecasts of Electric Energy Production in a Small Wind Turbine

Hybrid and Ensemble Methods of Two Days Ahead Forecasts of Electric Energy Production in a Small Wind Turbine

Deep Learning-Based Adaptive Remedial Action Scheme with Security Margin for Renewable-Dominated Power Grids

Deep Learning Algorithms for Wind Forecasting: An Overview

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