Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm

Wang, Deyun; Luo, Hongyuan; Grunder, Olivier; Lin, Yanbing; Guo, Hao

doi:10.1016/j.apenergy.2016.12.134

Cited by 266 publications

(90 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Liu et al [28] built up a model which applied both a WT and EMD decomposition method. Wang et al [29] presented a hybrid model based on the FEEMD (fast ensemble empirical mode decomposition), VMD, BP and FA (firefly algorithm). The FEEMD was used to decompose the original series into several sub-series, while the VMD was used to further decompose high frequency sub-series.…”

mentioning

confidence: 99%

Short-Term Wind Speed Forecasting Based on Hybrid Variational Mode Decomposition and Least Squares Support Vector Machine Optimized by Bat Algorithm Model

Lin

2019

Sustainability

View full text Add to dashboard Cite

With the integration of wind energy into electricity grids, wind speed forecasting plays an important role in energy generation planning, power grid integration and turbine maintenance scheduling. This study proposes a hybrid wind speed forecasting model to enhance prediction performance. Variational mode decomposition (VMD) was applied to decompose the original wind speed series into different sub-series with various frequencies. A least squares support vector machine (LSSVM) model with the pertinent parameters being optimized by a bat algorithm (BA) was established to forecast those sub-series extracted from VMD. The ultimate forecast of wind speed can be obtained by accumulating the prediction values of each sub-series. The results show that: (a) VMD-BA-LSSVM displays better capacity for the prediction of ultra short-term (15 min) and short-term (1 h) wind speed forecasting; (b) the proposed forecasting model was compared with wavelet decomposition (WD) and ensemble empirical mode decomposition (EEMD), and the results indicate that VMD has stronger decomposition ability than WD and EEMD, thus, significant improvements in forecasting accuracy were obtained with the proposed forecasting models compared with other forecasting methods.The physical model relies on the information stated in the numerical weather forecast [5]. This model was established with many complicated factors such as pressure, temperature, obstacles and roughness, which are usually difficult to utilize in practical applications [6]. The traditional statistical model based on the mature statistical equations to obtain the potential evolution rule [7,8]. The most commonly used traditional statistical models for wind speed forecasting include the autoregressive model (AR) [9], autoregressive moving average (ARMA) [10] and autoregressive integrated moving average model (ARIMA) [11]. Liebl [12] proposed a new statistical perspective using a functional factor model for modeling and forecasting electricity spot prices that accounts for the merit order model. Statistical models have simple principles and high efficiencies. However, the prediction accuracy of low-order statistical models is relatively low, while high-order model parameters are tremendously difficult to obtain. As for the AI methods, artificial neural networks (ANN) [13], support vector regression (SVR) [14], regularized extreme learning machine (RELM) [15] and Least square support vector machines (LSSVM) [16] might be the most frequently used models for wind speed forecasting, and empirical analysis shows that they are superior to traditional linear models. Yeh [17] proposed a parameter-free simplified swarm optimization for ANN training for time-series prediction and demonstrated its robustness and efficiency. employed the SVR model and their results showed that the proposed model was more accurate than the persistence and auto-regressive models in medium short-term wind speed and wind power forecasting. Zhou et al. [16] built a LSSVM based model for one-step ahead wind speed forecas...

show abstract

mentioning

confidence: 99%

Short-Term Wind Speed Forecasting Based on Hybrid Variational Mode Decomposition and Least Squares Support Vector Machine Optimized by Bat Algorithm Model

Lin

2019

Sustainability

View full text Add to dashboard Cite

show abstract

“…Error backpropagation algorithm is a learning process of positive information dissemination and error backpropagation [24][25][26]. Input information passes to the output layer through a layer-by-layer process from the input layer to the hidden layer.…”

Section: Error Backpropagation Algorithmmentioning

confidence: 99%

Methanol steam reforming performance optimisation of cylindrical microreactor for hydrogen production utilising error backpropagation and genetic algorithm

Zheng

Zhou

et al. 2019

Chemical Engineering Journal

View full text Add to dashboard Cite

To optimise methanol steam reforming performance of cylindrical microreactor for hydrogen production, an error backpropagation algorithm was used to build a mathematical model for reaction performance of different microreactors for hydrogen production. Additionally, a genetic algorithm (GA) was utilised to process the computational model to obtain the optimum reaction parameters. The reliability of the optimum reaction parameters of cylindrical microreactor for hydrogen production was verified by experiments. Firstly, take plate microreactor as an example, the porosity of porous copper fiber sintered sheet (PCFSS), reaction temperature of methanol steam reforming for hydrogen production, injection velocity of the methanol and water mixture, and catalyst loading of PCFSS were considered as input data, whereas methanol conversion was used as output data. The computational model for specific testing system was gained by utilising input and output data from specific testing system to train the mathematical model for different microreactors, combining with matrix laboratory (MATLAB) neural network toolbox and designed MATLAB program. The E max of 5% for plate microreactor and E max of 3.2% for cylindrical microreactor verified the good predictive ability and reliability of the computational model for plate and cylindrical microreactor, indicating the reliability and universal applicability of the mathematical model for different microreactors. Secondly, the effects and mechanisms of PPI, reaction temperature, injection velocity, and catalyst loading on methanol conversion were studied, relying on the computational model. Finally, the optimum reaction parameters were acquired using GA, MATLAB neural network toolbox and designed MATLAB program. The validity of the optimum reaction parameters of cylindrical microreactor for hydrogen production was confirmed by experiments. This study provides a reference method for methanol steam reforming performance optimisation for hydrogen production.Highlights >Error backpropagation algorithm is used to built general mathematical model.>Computational model is established based on general mathematical model.>Computational model shows good reliability and predictive ability. >Relations between reaction parameters and performance are studied. > Optimum reaction parameters are obtained by using genetic algorithm.Graphical abstract

show abstract

“…In the area of energy engineering and energy systems, Ghorbani et al used FA for prediction of gas flow rates from gas condensate reservoirs [22], and Massan et al used FA to solve wind turbine applications [45]. Wang et al used an FA-BP neural network to forecast electricity price [69] and Rastgou and Moshtagh used FA for multi-stage transmission expansion planning [54]. In addition, Satapathy et al used a hybrid HS-FA based approach to improve the stability of PV-BESS diesel generator-based microgrid [57].…”

Section: Applications Of Fa and Its Variantsmentioning

confidence: 99%

Nature-Inspired Algorithms and Applied Optimization

Yang¹

2018

Studies in Computational Intelligence

View full text Add to dashboard Cite

show abstract

Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm

Cited by 266 publications

References 36 publications

Short-Term Wind Speed Forecasting Based on Hybrid Variational Mode Decomposition and Least Squares Support Vector Machine Optimized by Bat Algorithm Model

Short-Term Wind Speed Forecasting Based on Hybrid Variational Mode Decomposition and Least Squares Support Vector Machine Optimized by Bat Algorithm Model

Methanol steam reforming performance optimisation of cylindrical microreactor for hydrogen production utilising error backpropagation and genetic algorithm

Nature-Inspired Algorithms and Applied Optimization

Contact Info

Product

Resources

About