A Hybrid Wind Speed Forecasting System Based on a ‘Decomposition and Ensemble’ Strategy and Fuzzy Time Series

Yang, Hufang; Jiang, Zaiping; Lu, Hanqing

doi:10.3390/en10091422

Cited by 42 publications

(21 citation statements)

References 71 publications

(67 reference statements)

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“…Time series forecasting has fundamental importance in numerous practical engineering fields such as energy, finance, geology, and information technology [7][8][9][10][11][12]. For instance, forecasting of electricity consumption is of great importance in deregulated electricity markets for all of the stakeholders: energy wholesalers, traders, retailers, and consumers [10].…”

Section: Introductionmentioning

confidence: 99%

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Combining LSTM Network Ensemble via Adaptive Weighting for Improved Time Series Forecasting

Choi

Lee

2018

Mathematical Problems in Engineering

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Time series forecasting is essential for various engineering applications in finance, geology, and information technology, etc. Long Short-Term Memory (LSTM) networks are nowadays gaining renewed interest and they are replacing many practical implementations of the time series forecasting systems. This paper presents a novel LSTM ensemble forecasting algorithm that effectively combines multiple forecast (prediction) results from a set of individual LSTM networks. The main advantages of our LSTM ensemble method over other state-of-the-art ensemble techniques are summarized as follows: (1) we develop a novel way of dynamically adjusting the combining weights that are used for combining multiple LSTM models to produce the composite prediction output; for this, our method is devised for updating combining weights at each time step in an adaptive and recursive way by using both past prediction errors and forgetting weight factor; (2) our method is capable of well capturing nonlinear statistical properties in the time series, which considerably improves the forecasting accuracy; (3) our method is straightforward to implement and computationally efficient when it comes to runtime performance because it does not require the complex optimization in the process of finding combining weights. Comparative experiments demonstrate that our proposed LSTM ensemble method achieves state-of-the-art forecasting performance on four real-life time series datasets publicly available.

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Section: Introductionmentioning

confidence: 99%

“…Moreover, energy-related time series forecasting plays an important role in the planning and working of the power grid system [7,8]; for instance, accurate and stable wind speed forecast has primary importance in the wind power industry and make an influence on power-system management and the stability of market economics [11].…”

Section: Introductionmentioning

confidence: 99%

Combining LSTM Network Ensemble via Adaptive Weighting for Improved Time Series Forecasting

Choi

Lee

2018

Mathematical Problems in Engineering

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“…Statistical models used to forecast wind speed usually are fuzzy methods [12], autoregressive (AR), moving average (MA), autoregressive moving average (ARMA) [13], and autoregressive integrated moving average (ARIMA) [14] models. These required that the time series data be stable, or stable after differentiation, and they can only capture the linear relationship, but not the non-linear relationship.…”

Section: Introductionmentioning

confidence: 99%

Artificial Combined Model Based on Hybrid Nonlinear Neural Network Models and Statistics Linear Models—Research and Application for Wind Speed Forecasting

et al. 2018

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The use of wind power is rapidly increasing as an important part of power systems, but because of the intermittent and random nature of wind speed, system operators and researchers urgently need to find more reliable methods to forecast wind speed. Through research, it is found that the time series of wind speed demonstrate not only linear features but also nonlinear features. Hence, a combined forecasting model based on an improved cuckoo search algorithm optimizes weight, and several single models—linear model, hybrid nonlinear neural network, and fuzzy forecasting model—are developed in this paper to provide more trend change for time series of wind speed forecasting besides improving the forecasting accuracy. Furthermore, the effectiveness of the proposed model is proved by wind speed data from four wind farm sites and the results are more reliable and accurate than comparison models.

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“…Two key aspects in wind energy have been studied in this special issue: Wind speed and wind power generation. On the one hand, a hybrid wind speed forecasting system based on a decomposition and ensemble strategy and fuzzy time series can be found in [7]. One the other hand, wind power forecasting based on echo state networks and long short-term memory was analyzed in [8].…”

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confidence: 99%

Data Science and Big Data in Energy Forecasting

2018

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This editorial summarizes the performance of the special issue entitled Data Science and Big Data in Energy Forecasting, which was published at MDPI's Energies journal. The special issue took place in 2017 and accepted a total of 13 papers from 7 different countries. Electrical, solar and wind energy forecasting were the most analyzed topics, introducing new methods with applications of utmost relevance.

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A Hybrid Wind Speed Forecasting System Based on a ‘Decomposition and Ensemble’ Strategy and Fuzzy Time Series

Cited by 42 publications

References 71 publications

Combining LSTM Network Ensemble via Adaptive Weighting for Improved Time Series Forecasting

Combining LSTM Network Ensemble via Adaptive Weighting for Improved Time Series Forecasting

Artificial Combined Model Based on Hybrid Nonlinear Neural Network Models and Statistics Linear Models—Research and Application for Wind Speed Forecasting

Data Science and Big Data in Energy Forecasting

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