Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods

Zhang, Yang; Li, Ce; Li, Lian

doi:10.1016/j.apenergy.2016.12.130

Cited by 317 publications

(139 citation statements)

References 46 publications

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“…As reported in the previous literature 38,41,49,50 , the ELM outperforms the conventional computational intelligence algorithms. However, the connection weights between the input and hidden layers of the ELM algorithm are randomly determined and it leads to random fluctuation of the predicted results.…”

Section: Resultssupporting

confidence: 69%

“…41 . Considering that the performance of KELM is affected by its parameters 38,41 , it is significant to select appropriate parameters. The artificial bee colony (ABC) is a swarm intelligence-based global optimization algorithm, and has been utilized to search the optimal parameters of KELM due to its high accuracy and fast convergence characteristic 42 .…”

Section: Introductionmentioning

confidence: 99%

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A novel method for landslide displacement prediction by integrating advanced computational intelligence algorithms

Zhou

Yin

Cao

et al. 2018

Sci Rep

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Landslide displacement prediction is considered as an essential component for developing early warning systems. The modelling of conventional forecast methods requires enormous monitoring data that limit its application. To conduct accurate displacement prediction with limited data, a novel method is proposed and applied by integrating three computational intelligence algorithms namely: the wavelet transform (WT), the artificial bees colony (ABC), and the kernel-based extreme learning machine (KELM). At first, the total displacement was decomposed into several sub-sequences with different frequencies using the WT. Next each sub-sequence was predicted separately by the KELM whose parameters were optimized by the ABC. Finally the predicted total displacement was obtained by adding all the predicted sub-sequences. The Shuping landslide in the Three Gorges Reservoir area in China was taken as a case study. The performance of the new method was compared with the WT-ELM, ABC-KELM, ELM, and the support vector machine (SVM) methods. Results show that the prediction accuracy can be improved by decomposing the total displacement into sub-sequences with various frequencies and by predicting them separately. The ABC-KELM algorithm shows the highest prediction capacity followed by the ELM and SVM. Overall, the proposed method achieved excellent performance both in terms of accuracy and stability.

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Section: Resultssupporting

confidence: 69%

Section: Introductionmentioning

confidence: 99%

A novel method for landslide displacement prediction by integrating advanced computational intelligence algorithms

Zhou

Yin

Cao

et al. 2018

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Year Methods and analysis Country Darbellay 34 2017 Combined wavelet transform (WT), autoregressive moving average (ARMA), kernel-based extreme learning machine (KELM), and self-adapting particle swarm optimization (SAPSO) to predict hourly electricity prices using daily and weekly input data…”

Section: Authorsmentioning

confidence: 99%

Forecast of long‐term EUA price probability using momentum strategy and GBM simulation

2020

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CO2 storage projects are financially intensive and, without the European Union emission trading scheme that encourages CO2 emission reduction projects, they will not be cost effective, that is, feasible. Similar to the huge uncertainties involved in long‐term energy price prediction, CO2 market price is volatile. In this paper, indicators for key CO2 price changes are detected by moving average analysis. To prove the validity of the methods, geometric Brownian motion (GBM) simulations were performed for different drift and volatility values. Drift, the standard deviation of CO2 price changes and CO2 volatility were used on a daily, weekly and monthly basis for short term simulations and on a weekly basis for long term simulations. GBM simulation can help determine the probability of a European emission allowances (EUA) price, which is featured by great variations, depending on the period. Signals for periods were determined by momentum strategy, with 60 days for short and 365 days for the long moving average. They show correlation with natural gas prices, that is, EUA signals appear 13–17 months after the signal in natural gas price. Among the vast number of proposed hybrid prediction models, this correlation allows the prediction of a long‐term price trend. It seems that back‐loading measures obstructed risk estimates because they resulted in extreme price drift in some periods, as the consequence of rebalancing in supply and demand. © 2020 Society of Chemical Industry and John Wiley & Sons, Ltd.

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“…Based on the above theory, an efficient way to help improving the forecasting accuracy under non-stationary weather statuses with high volatility data is to decompose the original data series into several stable parts and fluctuant parts. These decomposed subseries have better behaviors (e.g., more stable variances and fewer outliers) in terms of regularity than the original data series, and thus can be forecasted more accurately using multiple well-directed models [25][26][27][28][29][30].…”

Section: Wavelet Decomposition Based Irradiance Forecastingmentioning

confidence: 99%

Time-Section Fusion Pattern Classification Based Day-Ahead Solar Irradiance Ensemble Forecasting Model Using Mutual Iterative Optimization

et al. 2018

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Accurate solar PV power forecasting can provide expected future PV output power so as to help the system operator to dispatch traditional power plants to maintain the balance between supply and demand sides. However, under non-stationary weather conditions, such as cloudy or partly cloudy days, the variability of solar irradiance makes the accurate PV power forecasting a very hard task. Ensemble forecasting based on multiple models established by different theory has been proved as an effective means on improving forecasting accuracy. Classification modeling according to different patterns could reduce the complexity and difficulty of intro-class data fitting so as to improve the forecasting accuracy as well. When combining the two above points and focusing on the different fusion pattern specifically in terms of hourly time dimension, a time-section fusion pattern classification based day-ahead solar irradiance ensemble forecasting model using mutual iterative optimization is proposed, which contains multiple forecasting models based on wavelet decomposition (WD), fusion pattern classification model, and fusion models corresponding to each fusion pattern. First, the solar irradiance is forecasted using WD based models at different WD level. Second, the fusion pattern classification recognition model is trained and then applied to recognize the different fusion pattern at each hourly time section. At last, the final forecasting result is obtained using the optimal fusion model corresponding to the data fusion pattern. In addition, a mutual iterative optimization framework for the pattern classification and data fusion models is also proposed to improve the model's performance. Simulations show that the mutual iterative optimization framework can effectively enhance the performance and coordination of pattern classification and data fusion models. The accuracy of the proposed solar irradiance day-ahead ensemble forecasting model is verified when compared with a standard Artificial Neural Network (ANN) forecasting model, five WD based models and a single ensemble forecasting model without time-section fusion classification.

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Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods

Cited by 317 publications

References 46 publications

A novel method for landslide displacement prediction by integrating advanced computational intelligence algorithms

A novel method for landslide displacement prediction by integrating advanced computational intelligence algorithms

Forecast of long‐term EUA price probability using momentum strategy and GBM simulation

Time-Section Fusion Pattern Classification Based Day-Ahead Solar Irradiance Ensemble Forecasting Model Using Mutual Iterative Optimization

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