A tracking control method for electricity-carbon emission forecasting
Abstrak
This paper introduces a novel carbon emission prediction method based on tracking control, leveraging historical CO2 emission prediction errors and feed-forward integration of electricity consumption data to enhance forecasting accuracy and minimize lag. Comparative analysis with pre-trained models such as LSTM and ARDL using Python showcases the proposed method's substantial reduction in prediction errors compared to singular reliance on electricity data, while also significantly reducing computational time in contrast to LSTM models. The findings establish a valuable reference for policymakers and researchers in refining carbon emission prediction methodologies and formulating effective carbon reduction policies.
Topik & Kata Kunci
Penulis (8)
Hongyin Chen
Songcen Wang
Jianfeng Li
Yaoxian Yu
Dezhi Li
Lu Jin
Yi Guo
Xiaorui Cui
Akses Cepat
PDF tidak tersedia langsung
Cek di sumber asli →- Tahun Terbit
- 2024
- Sumber Database
- DOAJ
- DOI
- 10.1016/j.heliyon.2024.e36576
- Akses
- Open Access ✓