2022
DOI: 10.1002/ese3.1122
|View full text |Cite
|
Sign up to set email alerts
|

A hybrid deep learning approach by integrating extreme gradient boosting‐long short‐term memory with generalized autoregressive conditional heteroscedasticity family models for natural gas load volatility prediction

Abstract: Natural gas load forecasting provides decision-making support for natural gas dispatch and management, pipeline network construction, pricing, and sustainable energy development. To explain the uncertainty and volatility in natural gas load forecasting, this study predicts the natural gas load volatility. As the natural gas load volatility has the time-series features, along with long-term memory, volatility aggregation, asymmetry, and nonnormality, this study proposes a natural gas load volatility prediction … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
6
1

Relationship

1
6

Authors

Journals

citations
Cited by 11 publications
(2 citation statements)
references
References 33 publications
0
2
0
Order By: Relevance
“…The data normalization process will result in values ranging from 0 to 1. According to [ 28 ], the equation used in data normalization is as follows: and the denormalization [ 37 ] of the data is where x norm is a normalised value, x max is the maximum value of the entire data, and x min is the threshold of the entire data.…”
Section: Methodsmentioning
confidence: 99%
“…The data normalization process will result in values ranging from 0 to 1. According to [ 28 ], the equation used in data normalization is as follows: and the denormalization [ 37 ] of the data is where x norm is a normalised value, x max is the maximum value of the entire data, and x min is the threshold of the entire data.…”
Section: Methodsmentioning
confidence: 99%
“…To fully evaluate the performance of the LUR-GBM model, a ten-fold cross-validation (10-CV) based on samples, sites and time was used, and the computed results were compared with BPNN, DNN, RF, XGBoost and LightGBM. Three indicators, coefficient of determination (R 2 ), root mean square error (RMSE), mean prediction error (MAE) and mean absolute percentage error (MAPE), were calculated separately from the model prediction results to test the model performance [42]. R 2 is a measure of the degree of linear correlation between variables and reflects the proportion of the variation in the dependent variable that can be explained by the independent variable.…”
Section: Accuracy Evaluationmentioning
confidence: 99%