Electrical load forecasting to plan the increase in renewable energy sources and electricity demand : a CNN-QR-RTCF and deep learning approach

Jiotsop-Foze, Wellcome Peujio; Hernández-del-Valle, Adrián; Venegas-Martínez, Francisco

Please use this identifier to cite or link to this item: https://hdl.handle.net/11159/701062

Title:

Electrical load forecasting to plan the increase in renewable energy sources and electricity demand : a CNN-QR-RTCF and deep learning approach

Journal:

International Journal of Energy Economics and Policy

Authors:

Jiotsop-Foze, Wellcome Peujio
Hernández-del-Valle, Adrián
Venegas-Martínez, Francisco

e-ISSN:

2146-4553

Document Type:

Article

Year of Publication:

2024

Open Content License:

Original URL:

https://www.econjournals.com/index.php/ijeep/article/download/15773/7979/37878

Persistent Identifier of the first edition:

doi:10.32479/ijeep.15773

Language:

English (eng)

Citation:

Jiotsop-Foze, Wellcome Peujio/Hernández-del-Valle, Adrián et. al. (2024). Electrical load forecasting to plan the increase in renewable energy sources and electricity demand : a CNN-QR-RTCF and deep learning approach. In: International Journal of Energy Economics and Policy 14 (4), S. 186 - 194.
https://www.econjournals.com/index.php/ijeep/article/download/15773/7979/37878.
doi:10.32479/ijeep.15773.

Appears in Collections:

International journal of energy economics and policy (IJEEP)

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