Hourly Electricity Price Prediction for Electricity Market with High Proportion of Wind and Solar Power

Zhang, Yangrui; Tao, Peng; Wu, Xiangming; Yang, Chenguang; Hân, Gia; Zhou, Hui; Hu, Yinlong

doi:10.3390/en15041345

Cited by 12 publications

(4 citation statements)

References 27 publications

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“…This approach ensures the practicality and operability of the model [7].In addition to this, we continued to employ the LSTM algorithm to derive . Function Evaluations (2) I also employed Bayesian optimization [9], which allowed me to efficiently search for the optimal model parameters, reducing the time and computational resources required for model tuning [10].The optimization process is illustrated in Figure 9 and Figure 10.…”

Section: Figure 2 Emd Decompositionmentioning

confidence: 99%

Fusion Of SSA Mahjong Search and LSTM Model for Vegetable Sales and Pricing Research

Cheng

2024

HSET

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In supermarkets, vegetable products have a short shelf life, and their quality deteriorates over time, making it challenging to sell them the next day. To formulate rational sales and pricing decisions, this article first analyzes the relationship between sales and prices. Due to the unique nature of vegetable products, pricing may also be influenced by historical sales and pricing information, which can be analyzed through regression data.The study begins with data preprocessing using EMD decomposition and utilizes an LSTM model to predict various vegetable categories. Additionally, an intelligent optimization algorithm, the SSA Sparrow Search Algorithm, is employed to optimize sales forecasts for the upcoming week. Combining historical sales data and pricing information, a multivariate time series prediction is performed, again using the LSTM model, but leveraging Bayesian optimization to determine pricing for various vegetable categories in the upcoming week.This approach provides a pricing strategy that maximizes the benefits for supermarkets.

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Section: Figure 2 Emd Decompositionmentioning

confidence: 99%

Fusion Of SSA Mahjong Search and LSTM Model for Vegetable Sales and Pricing Research

Cheng

2024

HSET

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“…Meanwhile, the COV value is introduced for further analysis of the influence of prediction accuracy on different datasets. This value is a typical indicator of the degree of data fluctuation, with more volatile data having a higher COV value [ 44 ]. It also can be concluded that the accuracy of the proposed model prediction is inversely related to the degree of fluctuation in the original data.…”

Section: Experiments and Analysismentioning

confidence: 99%

An Adaptive Hybrid Model for Wind Power Prediction Based on the IVMD-FE-Ad-Informer

Tian

Wang

Zhou

et al. 2023

Entropy

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Accurate wind power prediction can increase the utilization rate of wind power generation and maintain the stability of the power system. At present, a large number of wind power prediction studies are based on the mean square error (MSE) loss function, which generates many errors when predicting original data with random fluctuation and non-stationarity. Therefore, a hybrid model for wind power prediction named IVMD-FE-Ad-Informer, which is based on Informer with an adaptive loss function and combines improved variational mode decomposition (IVMD) and fuzzy entropy (FE), is proposed. Firstly, the original data are decomposed into K subsequences by IVMD, which possess distinct frequency domain characteristics. Secondly, the sub-series are reconstructed into new elements using FE. Then, the adaptive and robust Ad-Informer model predicts new elements and the predicted values of each element are superimposed to obtain the final results of wind power. Finally, the model is analyzed and evaluated on two real datasets collected from wind farms in China and Spain. The results demonstrate that the proposed model is superior to other models in the performance and accuracy on different datasets, and this model can effectively meet the demand for actual wind power prediction.

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“…The adaptive nature of WPD enables the decomposition of unstable electricity price data into high-and low-frequency components, thereby facilitating forecasting tasks. The working mechanism of WPD is depicted in Figure 5, and the equation governing the wavelet packet decomposition is represented by Equation (13).…”

Section: Testing Functionsmentioning

confidence: 99%

“…The literature [12] uses the two methodologies of EEMD and VMD decomposition to increase the model's overall prediction accuracy. Although population optimization algorithms such as the sparrow search algorithm [13], whale optimization algorithm [14], and marine predator algorithm [8] have significant advantages, they are prone to local optimization and slow convergence speeds and thus require improvement to enhance optimization performance. Literature [15] proposes an improved algorithm strategy based on the combination of Cauchy variation and adaptive weight, which significantly improves the optimization accuracy and convergence speed of the whale optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

Electricity Market Price Prediction Based on Quadratic Hybrid Decomposition and THPO Algorithm

Yan

et al. 2023

Energies

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Electricity price forecasting is a crucial aspect of spot trading in the electricity market and optimal scheduling of microgrids. However, the stochastic and periodic nature of electricity price sequences often results in low accuracy in electricity price forecasting. To address this issue, this study proposes a quadratic hybrid decomposition method based on ensemble empirical modal decomposition (EEMD) and wavelet packet decomposition (WPD), along with a deep extreme learning machine (DELM) optimized by a THPO algorithm to enhance the accuracy of electricity price prediction. To overcome the problem of the optimization algorithm falling into local optima, an improved optimization algorithm strategy is proposed to enhance the optimization-seeking ability of HPO. The electricity price series is decomposed into a series of components using EEMD decomposition and WPD decomposition, and the DELM model optimized by the THPO algorithm is built for each component separately. The predicted values of all the series are then superimposed to obtain the final electricity price prediction. The proposed prediction model is evaluated using electricity price data from an Australian electricity market. The results demonstrate that the proposed improved algorithm strategy significantly improves the convergence performance of the algorithm, and the proposed prediction model effectively enhances the accuracy and stability of electricity price prediction, as compared to several other prediction models.

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Hourly Electricity Price Prediction for Electricity Market with High Proportion of Wind and Solar Power

Cited by 12 publications

References 27 publications

Fusion Of SSA Mahjong Search and LSTM Model for Vegetable Sales and Pricing Research

Fusion Of SSA Mahjong Search and LSTM Model for Vegetable Sales and Pricing Research

An Adaptive Hybrid Model for Wind Power Prediction Based on the IVMD-FE-Ad-Informer

Electricity Market Price Prediction Based on Quadratic Hybrid Decomposition and THPO Algorithm

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