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<p>With the increasing scarcity of conventional energy and environmental degradation, countries around the world are increasing their investment in renewable energy development. In order to make a scientific investment evaluation of renewable energy projects, this paper examines the analysis and control of their financial data. The intelligent analysis system of financial data is constructed based on OLAP. Logistic regression model and decision tree algorithm model are selected as the operation algorithm of the system to complete the intelligent analysis of data. Combining random forest algorithm and autoregressive moving average model, under the guidance of Bagging idea, the financial status of renewable energy projects after investment is judged in order to achieve the purpose of dynamic control. According to the results of analysis and control of financial data of renewable energy projects, it is known that the correct probability of intelligent analysis of financial data reached 94.5%, 83.1%, and 92.7% for different sample sizes of data sets, respectively. There were significant improvements in the efficiency of capital usage and asset quality, with an increase in capital concentration of 30.42%, an increase in inventory turnover from 10.68% to 13.04%, and an increase in the recovery rate of overdue accounts receivable from 60.31% to 67.83%. It has been proven that the method can help investors to better utilize uncertainty to improve the investment value of project, providing investors with a new way of thinking about decision-making.</p>
</abstract>

With the increasing scarcity of conventional energy and environmental degradation, countries around the world are increasing their investment in renewable energy development. In order to make a scientific investment evaluation of renewable energy projects, this paper examines the analysis and control of their financial data. The intelligent analysis system of financial data is constructed based on OLAP. Logistic regression model and decision tree algorithm model are selected as the operation algorithm of the system to complete the intelligent analysis of data. Combining random forest algorithm and autoregressive moving average model, under the guidance of Bagging idea, the financial status of renewable energy projects after investment is judged in order to achieve the purpose of dynamic control. According to the results of analysis and control of financial data of renewable energy projects, it is known that the correct probability of intelligent analysis of financial data reached 94.5%, 83.1%, and 92.7% for different sample sizes of data sets, respectively. There were significant improvements in the efficiency of capital usage and asset quality, with an increase in capital concentration of 30.42%, an increase in inventory turnover from 10.68% to 13.04%, and an increase in the recovery rate of overdue accounts receivable from 60.31% to 67.83%. It has been proven that the method can help investors to better utilize uncertainty to improve the investment value of project, providing investors with a new way of thinking about decision-making.

Financial big data control and intelligent analysis method for investment decision of renewable energy projects

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

With the increasing scarcity of conventional energy and environmental degradation, countries around the world are increasing their investment in renewable...

Financial big data control and intelligent analysis method for investment decision of renewable energy projects

Publié en ligne: 02 juin 2023

Pages: -

Reçu: 20 avr. 2022

Accepté: 17 oct. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00163

Mots clés
Renewable energy projects, Intelligent analysis, Random forest algorithms, Decision tree algorithms, Data control

© 2023 Dongyun Li, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Financial big data control and intelligent analysis method for investment decision of renewable energy projects

Publié en ligne: 02 juin 2023

Pages: -

Reçu: 20 avr. 2022

Accepté: 17 oct. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00163

Mots clésRenewable energy projects, Intelligent analysis, Random forest algorithms, Decision tree algorithms, Data control

© 2023 Dongyun Li, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Renewable energy projects, Intelligent analysis, Random forest algorithms, Decision tree algorithms, Data control