Towards Flexibility Detection in Device-Level Energy Consumption

Bijay Neupane; Torben Bach Pedersen; Bo Thiesson

doi:10.1007/978-3-319-13290-7 1

Towards Flexibility Detection in Device-Level Energy Consumption

Bijay Neupane, Torben Bach Pedersen, Bo Thiesson

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

20 Citations (Scopus)

Abstract

The increasing drive towards green energy has boosted the installation of Renewable Energy Sources (RES). Increasing the share of RES in the power grid requires demand management by flexibility in the consumption. In this paper, we perform a state-of-the-art analysis on the flexibility and operation patterns of the devices in a set of real households. We propose a number of specific pre-processing steps such as operation stage segmentation, and aberrant operation duration removal to clean device level data. Further, we demonstrate various device operation properties such as hourly and daily regularities and patterns and the correlation between operating different devices. Subsequently, we show the existence of detectable time and energy flexibility in device operations. Finally, we provide various results providing a foundation for load- and flexibility-detection and -prediction at the device level.

Original language	English
Title of host publication	Data Analytics for Renewable Energy Integration : Proceedings of the Second ECML PKDD Workshop, DARE 2014
Editors	Wei Lee Woon, Zeyar Aung, Stuart Madnick
Volume	8817
Publisher	Springer Publishing Company
Publication date	2014
Pages	1-16
ISBN (Print)	978-3-3191-3289-1
DOIs	https://doi.org/10.1007/978-3-319-13290-7 1
Publication status	Published - 2014
Event	Data Analytics for Renewable Energy Integration: Second ECML PKDD Workshop, DARE 2014 - Centre Prouvé, Nancy, France Duration: 19 Sep 2014 → … Conference number: Second

Workshop

Workshop	Data Analytics for Renewable Energy Integration
Number	Second
Location	Centre Prouvé
Country/Territory	France
City	Nancy
Period	19/09/2014 → …

Series	Lecture Notes in Computer Science
ISSN	0302-9743

Access to Document

10.1007/978-3-319-13290-7 1

Cite this

Neupane, B., Pedersen, T. B., & Thiesson, B. (2014). Towards Flexibility Detection in Device-Level Energy Consumption. In W. L. Woon, Z. Aung, & S. Madnick (Eds.), Data Analytics for Renewable Energy Integration: Proceedings of the Second ECML PKDD Workshop, DARE 2014 (Vol. 8817, pp. 1-16). Springer Publishing Company. Lecture Notes in Computer Science https://doi.org/10.1007/978-3-319-13290-7 1

Neupane, Bijay ; Pedersen, Torben Bach ; Thiesson, Bo. / Towards Flexibility Detection in Device-Level Energy Consumption. Data Analytics for Renewable Energy Integration: Proceedings of the Second ECML PKDD Workshop, DARE 2014. editor / Wei Lee Woon ; Zeyar Aung ; Stuart Madnick. Vol. 8817 Springer Publishing Company, 2014. pp. 1-16 (Lecture Notes in Computer Science).

@inproceedings{fa25c44d9a0d4c5eb1faf99beebdc3cf,

title = "Towards Flexibility Detection in Device-Level Energy Consumption",

abstract = "The increasing drive towards green energy has boosted the installation of Renewable Energy Sources (RES). Increasing the share of RES in the power grid requires demand management by flexibility in the consumption. In this paper, we perform a state-of-the-art analysis on the flexibility and operation patterns of the devices in a set of real households. We propose a number of specific pre-processing steps such as operation stage segmentation, and aberrant operation duration removal to clean device level data. Further, we demonstrate various device operation properties such as hourly and daily regularities and patterns and the correlation between operating different devices. Subsequently, we show the existence of detectable time and energy flexibility in device operations. Finally, we provide various results providing a foundation for load- and flexibility-detection and -prediction at the device level.",

author = "Bijay Neupane and Pedersen, {Torben Bach} and Bo Thiesson",

year = "2014",

doi = "10.1007/978-3-319-13290-7 1",

language = "English",

isbn = "978-3-3191-3289-1",

volume = "8817",

series = "Lecture Notes in Computer Science",

publisher = "Springer Publishing Company",

pages = "1--16",

editor = "Woon, {Wei Lee} and { Aung}, Zeyar and Stuart Madnick",

booktitle = "Data Analytics for Renewable Energy Integration",

address = "United States",

note = "Data Analytics for Renewable Energy Integration : Second ECML PKDD Workshop, DARE 2014, DARE 2014 ; Conference date: 19-09-2014",

}

Neupane, B , Pedersen, TB & Thiesson, B 2014, Towards Flexibility Detection in Device-Level Energy Consumption. in WL Woon, Z Aung & S Madnick (eds), Data Analytics for Renewable Energy Integration: Proceedings of the Second ECML PKDD Workshop, DARE 2014. vol. 8817, Springer Publishing Company, Lecture Notes in Computer Science, pp. 1-16, Data Analytics for Renewable Energy Integration, Nancy, France, 19/09/2014. https://doi.org/10.1007/978-3-319-13290-7 1

Towards Flexibility Detection in Device-Level Energy Consumption. / Neupane, Bijay ; Pedersen, Torben Bach; Thiesson, Bo.

Data Analytics for Renewable Energy Integration: Proceedings of the Second ECML PKDD Workshop, DARE 2014. ed. / Wei Lee Woon; Zeyar Aung; Stuart Madnick. Vol. 8817 Springer Publishing Company, 2014. p. 1-16 (Lecture Notes in Computer Science).

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

TY - GEN

T1 - Towards Flexibility Detection in Device-Level Energy Consumption

AU - Neupane, Bijay

AU - Pedersen, Torben Bach

AU - Thiesson, Bo

N1 - Conference code: Second

PY - 2014

Y1 - 2014

N2 - The increasing drive towards green energy has boosted the installation of Renewable Energy Sources (RES). Increasing the share of RES in the power grid requires demand management by flexibility in the consumption. In this paper, we perform a state-of-the-art analysis on the flexibility and operation patterns of the devices in a set of real households. We propose a number of specific pre-processing steps such as operation stage segmentation, and aberrant operation duration removal to clean device level data. Further, we demonstrate various device operation properties such as hourly and daily regularities and patterns and the correlation between operating different devices. Subsequently, we show the existence of detectable time and energy flexibility in device operations. Finally, we provide various results providing a foundation for load- and flexibility-detection and -prediction at the device level.

AB - The increasing drive towards green energy has boosted the installation of Renewable Energy Sources (RES). Increasing the share of RES in the power grid requires demand management by flexibility in the consumption. In this paper, we perform a state-of-the-art analysis on the flexibility and operation patterns of the devices in a set of real households. We propose a number of specific pre-processing steps such as operation stage segmentation, and aberrant operation duration removal to clean device level data. Further, we demonstrate various device operation properties such as hourly and daily regularities and patterns and the correlation between operating different devices. Subsequently, we show the existence of detectable time and energy flexibility in device operations. Finally, we provide various results providing a foundation for load- and flexibility-detection and -prediction at the device level.

U2 - 10.1007/978-3-319-13290-7 1

DO - 10.1007/978-3-319-13290-7 1

M3 - Article in proceeding

SN - 978-3-3191-3289-1

VL - 8817

T3 - Lecture Notes in Computer Science

SP - 1

EP - 16

BT - Data Analytics for Renewable Energy Integration

A2 - Woon, Wei Lee

A2 - Aung, Zeyar

A2 - Madnick, Stuart

PB - Springer Publishing Company

T2 - Data Analytics for Renewable Energy Integration

Y2 - 19 September 2014

ER -

Neupane B , Pedersen TB, Thiesson B. Towards Flexibility Detection in Device-Level Energy Consumption. In Woon WL, Aung Z, Madnick S, editors, Data Analytics for Renewable Energy Integration: Proceedings of the Second ECML PKDD Workshop, DARE 2014. Vol. 8817. Springer Publishing Company. 2014. p. 1-16. (Lecture Notes in Computer Science). doi: 10.1007/978-3-319-13290-7 1

Towards Flexibility Detection in Device-Level Energy Consumption

Abstract

Workshop

Access to Document

Fingerprint

Cite this