2020
DOI: 10.1016/j.apenergy.2020.115135
|View full text |Cite
|
Sign up to set email alerts
|

Occupant-centric miscellaneous electric loads prediction in buildings using state-of-the-art deep learning methods

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1

Citation Types

0
8
0

Year Published

2020
2020
2024
2024

Publication Types

Select...
7
1
1

Relationship

0
9

Authors

Journals

citations
Cited by 56 publications
(8 citation statements)
references
References 30 publications
0
8
0
Order By: Relevance
“…[32] proposes a simple and effective method to detect events. The event detection is summarised by (1). Stride is represented by R in (1), and R is set to 1 s. The aggregated apparent power at t s is P t .…”
Section: Event Detectionmentioning
confidence: 99%
See 1 more Smart Citation
“…[32] proposes a simple and effective method to detect events. The event detection is summarised by (1). Stride is represented by R in (1), and R is set to 1 s. The aggregated apparent power at t s is P t .…”
Section: Event Detectionmentioning
confidence: 99%
“…Electricity is one of the most widely used energy sources in current society. With the continuous improvement of people's living standards, the rapid increase in electricity consumption by urban and rural residents has become an important component of the peak load and even peak load of smart grids [1]. And with the development of smart grids, appliance-level data information plays a vital role in smart power consumption.…”
Section: Introductionmentioning
confidence: 99%
“…ETH analysis and predictions utilizing deep learning (Zoumpekas et al, 2020). Occupant-centric miscellaneous electric loads prediction in buildings using state-of-the-art deep learning methods (Das et al, 2020).…”
Section: Introductionmentioning
confidence: 99%
“…The overall energy requirement of a building results from both its thermic setting and its inhabitants, which have a greater complication in the assessment and quantification than the building envelope and its thermic setting [7]. Therefore, the management of energy demands in buildings requires critical consideration of the behavior of occupants [8,9]. In this research, the amount of cooling load in the power grid peak and its reduction potential by observing behavioral issues in the form of codified scenarios are investigated by studying the behavioral details of household energy consumption using the summertimeuse survey of the Statistics Center of Iran in 2015 and by agent-based modeling of the residential sector in the Tehran.…”
Section: Introductionmentioning
confidence: 99%