Ms. Rong Dou Profile

Rong Dou

at Harbin Engineering Univ

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 8 December 2022 Paper

Prophet-LSTM combination model carbon trading price prediction research

Rong Dou, Fansheng Meng

Proceedings Volume 12474, 124741W (2022) https://doi.org/10.1117/12.2653785

KEYWORDS: Carbon, Data modeling, Performance modeling, Neural networks, Statistical modeling, Atmospheric modeling, Composites, Motion models, Artificial intelligence, Optimization (mathematics)

Read Abstract +

Driven by the dual carbon goals, China's carbon trading plays an increasingly significant role in carbon emission reduction. The price of carbon trading is the core issue of carbon trading, and predicting changes in the price of carbon trading is of great significance to the long-term low carbon development of enterprises. Based on an in-depth analysis of the properties of Prophet model and Long Short-Term Memory (LSTM) neural network, a combined Prophet-LSTM model for short-term prediction of carbon trading price is proposed in this paper. Prophet and LSTM prediction models are built separately, then a combination model is built by optimizing the weight coefficients for carbon trading price prediction. The data of Hubei carbon trading market from 2019 to 2022 were used as an arithmetic example for validation. The experimental results show that the combined Prophet-LSTM forecasting model has stronger stability and higher accuracy than the standard Prophet model forecasting method, LSTM model forecasting method, ARIMA model forecasting method and SARIMAX model forecasting method in carbon trading price time series analysis. The combined forecasting method proposed in this paper can effectively improve the prediction accuracy of carbon trading price, thus helping enterprises to reasonably assess their carbon assets and control the cost of carbon in production to achieve low carbon and high quality development.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years