Demand response ability evaluation based on seasonal and trend decomposition using LOESS and S–G filtering algorithms

Wu, Di; Wang, Yunchu; Li, Lei; Lu, Pengfei; Liu, Shengyuan; Dai, C.J.; Pan, Yizhou; Zhi, Zhang; Lin, Zhenzhi; Yang, Li

doi:10.1016/j.egyr.2022.02.139

Cited by 11 publications

(4 citation statements)

References 17 publications

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“…Therefore, the time series data decomposition method Seasonal and Trend decomposition using Loess (STL) using locally weighted regression for periodic trend decomposition is used to decompose time series data of gas emission (Apaydin et al, 2021). Since the overall timing diagram of the analyzed gas emission data is in a relatively stable state, the additive STL time series is selected to decompose the data (Wu et al, 2022). Each part of the decomposition is shown in Eq.…”

Section: Data Processingmentioning

confidence: 99%

“…At present, there are various methods for predicting the gas emission quantity, and they have achieved good results. Scholars such as Wang Qianyu have studied the application model of gas emission quantity prediction based on support vector machines (Wang et al, 2022). Solubility algorithm gas emission quantity prediction model, Cheng Xiaoyu and other scholars have studied the gas emission quantity prediction method of mining face based on random forest and support vector machine (Cheng et al, 2022), but there are still problems that the prediction results are not accurate enough to meet the needs of coal mine safety management.…”

Section: Introductionmentioning

confidence: 99%

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WITHDRAWN: Research on gas emission quantity prediction model based on EDA-IGA

Peng

Shi

Shi³

2023

Preprint

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In order to accurately predict the possible gas emission quantity in coal mines, it is proposed to use the multi-thread calculation of the Immune Genetic Algorithm (IGA) and injection of vaccines to improve the accuracy of prediction and combine the Estimation of Distribution Algorithm (EDA) to the distribution probability of excellent populations. Calculating, and selecting excellent populations for iteration, optimize the population generation process of the Immune Genetic Algorithm, so that the population quality is continuously optimized and improved, and the optimal solution is obtained, thereby establishing a gas emission quantity prediction model based on the Immune Genetic Algorithm and Estimation of Distribution Algorithm. Using the 9136 mining face with gas emission hazards in a coal mine from Shandong Province in China as the prediction object, the absolute gas emission quantity is used to scale the gas emission quantity, and it is found that the model can accurately predict the gas emission quantity, which is consistent with the on-site emission unanimous. In the prediction comparison with IGA, it is found that the accuracy of the prediction results has increased by 9.51%, and the number of iterations to achieve the required goal has been reduced by 67%, indicating that the EDA has a better role in optimizing the population update process such as genetic selection of the IGA. Comparing the prediction results of other models, it is found that the prediction accuracy of the EDA-IGA is 94.93%, which is the highest prediction accuracy, indicating that this prediction model can be used as a new method for the prediction of coal mine gas emission.

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Section: Data Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Research on gas emission quantity prediction model based on EDA-IGA

Peng

Shi

Shi³

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Therefore, the time series data decomposition method Seasonal and Trend decomposition using Loess (STL) using locally weighted regression for periodic trend decomposition is used to decompose time series data of gas emission [ 27 ]. Since the overall timing diagram of the analyzed gas emission data is in a relatively stable state, the additive STL time series is selected to decompose the data [ 28 ]. Each part of the decomposition is shown in Eq.…”

Section: Establishment Of Prediction Model Based On Eda-igamentioning

confidence: 99%

Research on gas emission quantity prediction model based on EDA-IGA

Peng

Shi

2023

Heliyon

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“…The economic incentive is able to induce the customer to sign the IDR contract with the electric power company. Based on the contract, the user can actively adjust the power consumption pattern to reduce the electricity consumption when load peaks emerge in the power system, whilst gaining a proper cost compensation (Wu et al, 2022). However, because of the unique features of the IDR contraction for example the strong timeliness, it puts forward higher requirements for the characteristics of the power consumption behaviors of the candidate participating users.…”

Section: Introductionmentioning

confidence: 99%

A residential user classification approach based on the graded portrait with considering the IDR adaptability and potential

Huang

Liu

et al. 2022

Front. Energy Res.

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In the current modern power system, extreme load peaks and valleys frequently occur due to the complicated electricity consumption behaviors. This point severely impacts the security, stability, and economy of the power system. Demand response (DR) has been proved to be one of the most effective ways to shift load to relieve the intensity of the power system. Although DR is mainly applied on the commercial and industrial loads traditionally, in recent years, the residential load has gradually attracted attentions of DR researches, especially incentive demand response (IDR) research because of its remarkable stability and flexibility in terms of load shifting. However, the difficulty of measuring the IDR adaptability and potential of a residential user according to the load curve significantly prevents the IDR from being conveniently implemented. And further, the power company is tremendously difficult to efficiently and effectively select the users with high IDR adaptabilities and potentials to participate in IDR. Therefore, to address the aforementioned issues, this paper presents a residential user classification approach based on the graded user portrait with considering the IDR adaptability and potential. Based on the portrait approach, the residential users with high IDR adaptabilities can be preliminarily selected. And then, based on the selected users, the portrait approach to delineate the users with high IDR potentials is further presented. Afterward, the achieved residential users with high adaptabilities and potentials are labeled, which are employed to train the presented variational auto encoder based deep belief network (VAE-DBN) load classification model. The experimental results show the effectiveness of the presented user portrait approaches as well as the presented load classification model. The results suggest that the presented approaches could be potential tools for power company to identify the suitable residential users for participating in the IDR tasks.

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Demand response ability evaluation based on seasonal and trend decomposition using LOESS and S–G filtering algorithms

Cited by 11 publications

References 17 publications

WITHDRAWN: Research on gas emission quantity prediction model based on EDA-IGA

WITHDRAWN: Research on gas emission quantity prediction model based on EDA-IGA

Research on gas emission quantity prediction model based on EDA-IGA

A residential user classification approach based on the graded portrait with considering the IDR adaptability and potential

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