Prediction of the Growth of Renewable Energies in the European Union using Time Series Analysis-Reference-Cited by-同舟云学术

Prediction of the Growth of Renewable Energies in the European Union using Time Series Analysis

Published:2023-11-20 Issue: Volume:22 Page:225-232
ISSN:2224-2872
Container-title:WSEAS TRANSACTIONS ON COMPUTERS
language:en
Short-container-title:

Author:

Kraenzle Holger¹,Rampp Maximilian¹,Werner Daniel¹,Seitz Jürgen¹,Sharma Neha²

Affiliation:

1. Duale Hochschule Baden-Württemberg, Wirtschaftsinformatik, Heidenheim 89518, GERMANY

2. AI.Cloud, Tata Consultancy Services, INDIA

Abstract

The whole world is affected by climate change and renewable energy plays an important role in combating climate change. To add to the existing precarious situation, the current political events such as the war in Ukraine mean that fossil raw materials such as oil and gas are becoming more and more expensive in the raw material markets. This paper presents the current state of renewable energies in Germany and Europe. Using data from the past 56 years, the predictive models ARIMA and Prophet are used to find out if the conversion to renewable energies and the elimination of fossil raw materials in the energy sector can be achieved in the EU. The results are compared with the target of the EU in 2030 and a long-term outlook until 2050 will be provided.

Publisher

World Scientific and Engineering Academy and Society (WSEAS)

Subject

General Computer Science

Reference25 articles.

1. Sharma N., Ghosh S., Saha M. (2021). Open Data for Sustainable Community. Advances in Sustainability Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-33-4312- 2_10.

2. Sharma, N., De, P.K. (2023). Towards NetZero Targets: Usage of Data Science for Long-Term Sustainability Pathways. Advances in Sustainability Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-19-5244-9.

3. K. U. R. Khan, G. H. Bisen, P. K. De and N. Sharma, "Multivariate Regression Analysis of Investments by Banks in Fossil Fuel Sectors to Predict Climate Change Consequences," 2021 IEEE 2nd International Conference on Technology, Engineering, Management for Societal impact using Marketing, Entrepreneurship and Talent (TEMSMET), 2021, pp. 1-6, doi: 10.1109/TEMSMET53515.2021.9768677.

4. Abhay Despande, Tanmay Belsare, Neha Sharma, Prithwis De, “Univariate Time Series Forecasting of Indian Agriculture Emissions”, In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2022. Communications in Computer and Information Science, vol.1614. Springer, Cham. https://doi.org/10.1007/978-3-031- 12641-3_28.

5. Chadha, A.S., Shinde, Y., Sharma, N., De, P.K. (2023). Predicting CO2 Emissions by Vehicles Using Machine Learning. In: Goswami, S., Barara, I.S., Goje, A., Mohan, C., Bruckstein, A.M. (eds) Data Management, Analytics and Innovation. ICDMAI 2022. Lecture Notes on Data Engineering and Communications Technologies, vol.137. Springer, Singapore. https://doi.org/10.1007/978-981-19-2600- 6_14.

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