A Bibliometric Analysis on Artificial Intelligence in the Energy Forecasting

The daily price in the energy market reflects the point at which supply and demand meet; however, this point is subject to significant variations due to the high volatility of both demand and supply. Therefore, the objective of this paper is to analyze, from a bibliometric perspective, how artificial intelligence can assist in forecasting energy demand.

Authors

M.M. Braggio
Matteo Lo Russo

Institutions

University of Genoa (IT)

Publication Details

Published: 2024-11-04
DOI: https://doi.org/10.1109/iceccme62383.2024.10796773
Citations: 1
Primary Topic: Energy Load and Power Forecasting
Type: article
Field-Weighted Citation Impact: 0.21

Controls

|||

ALL TIME

JAN

FEB

MAR

APR

MAY

JUN

JUL

AUG

SEP

OCT

article

A Bibliometric Analysis on Artificial Intelligence in the Energy Forecasting

M.M. Braggio, Matteo Lo Russo

Nov 4, 2024

1 citations

Energy Load and Power Forecasting

0.21

article

A Bibliometric Analysis on Artificial Intelligence in the Energy Forecasting

M.M. Braggio, Matteo Lo Russo

November 4, 2024article en

1 citations

Abstract

University of Genoa (IT)

Affordable and clean energy

Openalex Percentile: Top 45%

Energy Load and Power Forecasting

0.21

DOI Source OpenAlex

AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

A Bibliometric Analysis on Artificial Intelligence in the Energy Forecasting — M.M. Braggio, Matteo Lo Russo · (2024) | TGRS Research Map | TGRS