Wind Power Short-Term Prediction Based on LSTM and Discrete Wavelet Transform

Liu, Yao; Guan, Lin; Hou, Chen; Han, Hua; Liu, Zhangjie; Sun, Yao; Zheng, Minghui

doi:10.3390/app9061108

Cited by 174 publications

(67 citation statements)

References 35 publications

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“…Examples include support vector regression (SVR) [10][11][12][13], classification and regression tree (CART) [14], Gaussian process regression (GPR) [15], and ensemble learning [16]. The learning approaches are mainly based on the artificial neural network (ANN) [17], which uses meteorological observations provided by the wind farm to predict the wind power; examples include back propagation neural network (BPNN) [18,19], radial basis function neural network (RBFNN) [20], deep belief network (DBN) [21], recurrent neural network (RNN) [22], long short-term memory (LSTM) [23], and other neural networks [24][25][26]. For ANN, the adjustment of parameters may have a great influence on the prediction results.…”

Section: Introductionmentioning

confidence: 99%

A XGBoost Model with Weather Similarity Analysis and Feature Engineering for Short-Term Wind Power Forecasting

Zheng

2019

Applied Sciences

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Large-scale wind power access may cause a series of safety and stability problems. Wind power forecasting (WPF) is beneficial to dispatch in advance. In this paper, a new extreme gradient boosting (XGBoost) model with weather similarity analysis and feature engineering is proposed for short-term wind power forecasting. Based on the similarity among historical days’ weather, k-means clustering algorithm is used to divide the samples into several categories. Additionally, we also create some time features and drop unimportant features through feature engineering. For each category, we make predictions using XGBoost. The results of the proposed model are compared with the back propagation neural network (BPNN) and classification and regression tree (CART), random forests (RF), support vector regression (SVR), and a single XGBoost model. It is shown that the proposed model produces the highest forecasting accuracy among all these models.

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Section: Introductionmentioning

confidence: 99%

A XGBoost Model with Weather Similarity Analysis and Feature Engineering for Short-Term Wind Power Forecasting

Zheng

2019

Applied Sciences

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“…Over the past few decades, recurrent neural networks (RNNs) [6,7] have been used particularly for time series prediction because this type of network includes states that can capture historical information from an arbitrarily long context window. In our study, we use long short-term memory (LSTM) cells for sequence learning of financial market predictions.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Short-Term Stock Price Movement Prediction using Transfer Learning

Nguyen

Yoon

2019

Applied Sciences

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Stock price prediction has always been an important application in time series predictions. Recently, deep neural networks have been employed extensively for financial time series tasks. The network typically requires a large amount of training samples to achieve high accuracy. However, in the stock market, the number of data points collected on a daily basis is limited in one year, which leads to insufficient training samples and accordingly results in an overfitting problem. Moreover, predicting stock price movement is affected by various factors in the stock market. Therefore, choosing appropriate input features for prediction models should be taken into account. To address these problems, this paper proposes a novel framework, named deep transfer with related stock information (DTRSI), which takes advantage of a deep neural network and transfer learning. First, a base model using long short-term memory (LSTM) cells is pre-trained based on a large amount of data, which are obtained from a number of different stocks, to optimize initial training parameters. Second, the base model is fine-tuned by using a small amount data from a target stock and different types of input features (constructed based on the relationship between stocks) in order to enhance performance. Experiments are conducted with data from top-five companies in the Korean market and the United States (US) market from 2012 to 2018 in terms of the highest market capitalization. Experimental results demonstrate the effectiveness of transfer learning and using stock relationship information in helping to improve model performance, and the proposed approach shows remarkable performance (compared to other baselines) in terms of prediction accuracy.

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“…Liu et al designed a wind power short-term forecasting method based on discrete wavelet transform and LSTM, and reported an increased prediction accuracy compared to five different benchmarks [29].…”

Section: Related Workmentioning

confidence: 99%

Multi-Sequence LSTM-RNN Deep Learning and Metaheuristics for Electric Load Forecasting

et al. 2020

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Short term electric load forecasting plays a crucial role for utility companies, as it allows for the efficient operation and management of power grid networks, optimal balancing between production and demand, as well as reduced production costs. As the volume and variety of energy data provided by building automation systems, smart meters, and other sources are continuously increasing, long short-term memory (LSTM) deep learning models have become an attractive approach for energy load forecasting. These models are characterized by their capabilities of learning long-term dependencies in collected electric data, which lead to accurate prediction results that outperform several alternative statistical and machine learning approaches. Unfortunately, applying LSTM models may not produce acceptable forecasting results, not only because of the noisy electric data but also due to the naive selection of its hyperparameter values. Therefore, an optimal configuration of an LSTM model is necessary to describe the electric consumption patterns and discover the time-series dynamics in the energy domain. Finding such an optimal configuration is, on the one hand, a combinatorial problem where selection is done from a very large space of choices; on the other hand, it is a learning problem where the hyperparameters should reflect the energy consumption domain knowledge, such as the influential time lags, seasonality, periodicity, and other temporal attributes. To handle this problem, we use in this paper metaheuristic-search-based algorithms, known by their ability to alleviate search complexity as well as their capacity to learn from the domain where they are applied, to find optimal or near-optimal values for the set of tunable LSTM hyperparameters in the electrical energy consumption domain. We tailor both a genetic algorithm (GA) and particle swarm optimization (PSO) to learn hyperparameters for load forecasting in the context of energy consumption of big data. The statistical analysis of the obtained result shows that the multi-sequence deep learning model tuned by the metaheuristic search algorithms provides more accurate results than the benchmark machine learning models and the LSTM model whose inputs and hyperparameters were established through limited experience and a discounted number of experimentations.

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Wind Power Short-Term Prediction Based on LSTM and Discrete Wavelet Transform

Cited by 174 publications

References 35 publications

A XGBoost Model with Weather Similarity Analysis and Feature Engineering for Short-Term Wind Power Forecasting

A XGBoost Model with Weather Similarity Analysis and Feature Engineering for Short-Term Wind Power Forecasting

A Novel Approach to Short-Term Stock Price Movement Prediction using Transfer Learning

Multi-Sequence LSTM-RNN Deep Learning and Metaheuristics for Electric Load Forecasting

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