Electricity Consumption Forecasting is a computational modeling task that specifically involves predicting future levels of electrical energy usage by end-users over defined temporal intervals through the analysis of historical consumption patterns, behavioral trends, and contextual variables.
2.8K
Publications
183.2K
Citations
8.3K
Authors
2.2K
Institutions
Publications per year
2017–2026
1.8K
Variants
Electricity Demand Forecasting
Parents
Publications
2.8K
Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks
Guokun Lai, Wei-Cheng Chang, Yiming Yang et al. · 2018 · 2.1K citations
Predicting residential energy consumption using CNN-LSTM neural networks
Tae Young Kim, Sung-Bae Cho · Energy · 2019 · 1.4K citations
1–5 of 2.8K
Authors
8.3K
- SR
16
h-index
- SW
16
h-index
- AA
15
h-index
- EH
13
h-index
- YG
13
h-index
1–5 of 8.3K
Institutions
2.2K
North China Electric Power University
Beijing, China
28 h-index
204 publications
11.6K citations
University of Tehran
Tehran, Iran
21 h-index
88 publications
6.6K citations
COMSATS University Islamabad
Islamabad, Pakistan
21 h-index
60 publications
4.6K citations
Sejong University
Seoul, South Korea
20 h-index
82 publications
8K citations
National University of Singapore
Singapore, Singapore
18 h-index
46 publications
1.8K citations
King Fahd University of Petroleum and Minerals
Dhahran, Saudi Arabia
17 h-index
32 publications
2K citations
Politecnico di Milano
Milan, Italy
17 h-index
73 publications
2.5K citations
University of California, Berkeley
Berkeley, United States
16 h-index
29 publications
15.5K citations
1–8 of 2.2K
Venues
- Energy
211 publications · 23.3K citations
85
h-index
- Applied Energy
110 publications · 11.8K citations
58
h-index
- Energies
192 publications · 11K citations
53
h-index
- Energy Policy
57 publications · 6.2K citations
44
h-index
- Energy and Buildings
78 publications · 7.5K citations
43
h-index
1–5