Electricity price prediction based on hybrid model of adam optimized LSTM neural network and wavelet transform

Chang, Zihan; Zhang, Yang; Chen, Wenbo

doi:10.1016/j.energy.2019.07.134

Cited by 301 publications

(137 citation statements)

References 27 publications

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“…16 Ma et al 17 combined the Grid concept and LSTM (G-LSTM) for the forecasting of fuel cell degradation. More than that, the latest researches applied LSTM to more hot areas of prediction, for example, electricity price forecasting, 18 flood forecasting, 19 wind speed forecasting, 20 air pollution forecasting, 21 voltages forecasting, 22 demand forecasting, 23 photovoltaic power forecasting, 24 and so forth. 25,26 The LSTM was wielded to alleviate the vanished gradient in a multi-layer network architecture.…”

Section: Overview Of Studies On Carbon Price Forecasting and Other mentioning

confidence: 99%

“…Additionally, the values of A MAE , A MAPE , A RMSE , and A R2 are closer to 0, the smaller amelioration the proposed model renders. The definition of, A MAE , A MAPE , A RMSE , and A R2 is described as Equations (16)(17)(18)(19). where the subscript 1 and 2 denote the statistic index of the proposed model and other benchmark models.…”

Section: Performance Evaluation Criteriamentioning

confidence: 99%

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An ensemble‐driven long short‐term memory model based on mode decomposition for carbon price forecasting of all eight carbon trading pilots in China

Sun

2020

Energy Science & Engineering

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Global climate change, which was triggered by the excessive emissions of greenhouse gases, has become an obstacle in sustainable development. Among all greenhouse gases, carbon dioxide is the most prominent and is artificially controllable. China ranks as the largest emitter of carbon dioxide worldwide and is a pioneer in economic growth. 1 As the international community is advocating in reducing carbon emissions, Chinese carbon emissions and its corresponding

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Section: Overview Of Studies On Carbon Price Forecasting and Other mentioning

confidence: 99%

Section: Performance Evaluation Criteriamentioning

confidence: 99%

An ensemble‐driven long short‐term memory model based on mode decomposition for carbon price forecasting of all eight carbon trading pilots in China

Sun

2020

Energy Science & Engineering

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“…In order to reduce the impact of extreme values in the process data, the Hampel filter [21] is used to process the training set before feeding into the GRU network. During the training of GRU network, the mean square error loss function and 'Adam' optimizer are used [22]. The length of moving window n is set as 99 by trial and error.…”

Section: Case 1: Hanging Faultmentioning

confidence: 99%

Fault Detection and Identification of Blast Furnace Ironmaking Process Using the Gated Recurrent Unit Network

Ouyang

Zeng

et al. 2020

Processes

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It is of critical importance to keep a steady operation in the blast furnace to facilitate the production of high quality hot metal. In order to monitor the state of blast furnace, this article proposes a fault detection and identification method based on the multidimensional Gated Recurrent Unit (GRU) network, which is a kind of recurrent neural network and is highly effective in handling process dynamics. Comparing to conventional recurrent neural networks, GRU has a simpler structure and involves fewer parameters. In fault detection, a moving window approach is applied and a GRU model is constructed for each process variable to generate a series of residuals, which is further monitored using the support vector data description (SVDD) method. Once a fault is detected, fault identification is performed using the contribution analysis. Application to a real blast furnace fault shows that the proposed method is effective.

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“…Surprisingly, despite its crucial role, research on how to select the appropriate data window for model estimation is an under-researched topic in forecasting. Whilst we have seen time series methods increase in complexity to capture the distinctive features of power price formation, going from ARIMA and its variants [2][3][4], neural network and other AI approaches [5][6][7][8][9][10] to wavelets [11][12][13] and various combinations, these procedures all rely on the presumption that the time series model, as estimated, can be projected forward, which may not be so appropriate in the more evolving power systems of today. In one of the few research papers to look at this aspect, the sensitivity of forecast errors to the estimation window has been analysed in [14] and based upon this, the research in [15] presented a pragmatic averaging of forecasts of individual ARX models estimated upon different data calibration windows.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Electricity Price Forecasting with Recurrent Regimes and Structural Breaks

et al. 2020

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This paper develops a new approach to short-term electricity forecasting by focusing upon the dynamic specification of an appropriate calibration dataset prior to model specification. It challenges the conventional forecasting principles which argue that adaptive methods should place most emphasis upon recent data and that regime-switching should likewise model transitions from the latest regime. The approach in this paper recognises that the most relevant dataset in the episodic, recurrent nature of electricity dynamics may not be the most recent. This methodology provides a dynamic calibration dataset approach that is based on cluster analysis applied to fundamental market regime indicators, as well as structural time series breakpoint analyses. Forecasting is based upon applying a hybrid fundamental optimisation model with a neural network to the appropriate calibration data. The results outperform other benchmark models in backtesting on data from the Iberian electricity market of 2017, which presents a considerable number of market structural breaks and evolving market price drivers.

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Electricity price prediction based on hybrid model of adam optimized LSTM neural network and wavelet transform

Cited by 301 publications

References 27 publications

An ensemble‐driven long short‐term memory model based on mode decomposition for carbon price forecasting of all eight carbon trading pilots in China

An ensemble‐driven long short‐term memory model based on mode decomposition for carbon price forecasting of all eight carbon trading pilots in China

Fault Detection and Identification of Blast Furnace Ironmaking Process Using the Gated Recurrent Unit Network

Short-Term Electricity Price Forecasting with Recurrent Regimes and Structural Breaks

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