Time Series Decomposition Approach for Simulating Electricity Demand Profile

Sandhya Patidar; David P. Jenkins; Andrew Peacock; Peter McCallum

doi:10.26868/25222708.2019.210541

Time Series Decomposition Approach for Simulating Electricity Demand Profile

Sandhya Patidar, David P. Jenkins, Andrew Peacock, Peter McCallum

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Electricity demand profiles of dwellings are mainly composed of various known (deterministic) and unknown (stochastic) processes. Effective data processing approaches (such as time series decomposition) are mainly used to simplify underlying patterns in the complex stochastic processes by fragmenting the different layers of hidden processes (referred as components of time series). This paper will demonstrate the performance of state-of-the-art STL (a Seasonal-Trend decomposition procedure based on Loess) techniques (Cleveland, Cleveland, McRae, & Terpenning, 1990), embedded within the framework of the HMM-GP model, in simulating dynamics of high-resolution electricity demand data. The method is applied to the case studies located in the Findhorn community.

Original language	English
Title of host publication	Proceedings of the 16th IBPSA Conference
Editors	V. Corrado, E. Fabrizio, A. Gasparella, F. Patuzzi
Pages	1388-1395
ISBN (Electronic)	978-1-7750520-1-2
DOIs	https://doi.org/10.26868/25222708.2019.210541
Publication status	Published - 1 Apr 2020
Event	Building Simulation 2019: 16th IBPSA International Conference and Exhibition - Angelicum Congress Centre, Rome, Italy Duration: 2 Sept 2019 → 4 Sept 2019 http://www.ibpsa.org/proceedings/BS2019/BS2019_210541.pdf

Conference

Conference	Building Simulation 2019
Abbreviated title	BS2019
Country/Territory	Italy
City	Rome
Period	2/09/19 → 4/09/19
Internet address	http://www.ibpsa.org/proceedings/BS2019/BS2019_210541.pdf

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10.26868/25222708.2019.210541

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@inproceedings{afc5980209fc468e95b6ec6babb6d9af,

title = "Time Series Decomposition Approach for Simulating Electricity Demand Profile",

abstract = "Electricity demand profiles of dwellings are mainly composed of various known (deterministic) and unknown (stochastic) processes. Effective data processing approaches (such as time series decomposition) are mainly used to simplify underlying patterns in the complex stochastic processes by fragmenting the different layers of hidden processes (referred as components of time series). This paper will demonstrate the performance of state-of-the-art STL (a Seasonal-Trend decomposition procedure based on Loess) techniques (Cleveland, Cleveland, McRae, & Terpenning, 1990), embedded within the framework of the HMM-GP model, in simulating dynamics of high-resolution electricity demand data. The method is applied to the case studies located in the Findhorn community.",

author = "Sandhya Patidar and Jenkins, {David P.} and Andrew Peacock and Peter McCallum",

year = "2020",

month = apr,

day = "1",

doi = "10.26868/25222708.2019.210541",

language = "English",

pages = "1388--1395",

editor = "V. Corrado and E. Fabrizio and A. Gasparella and F. Patuzzi",

booktitle = "Proceedings of the 16th IBPSA Conference",

note = "Building Simulation 2019 : 16th IBPSA International Conference and Exhibition, BS2019 ; Conference date: 02-09-2019 Through 04-09-2019",

url = "http://www.ibpsa.org/proceedings/BS2019/BS2019_210541.pdf",

}

TY - GEN

T1 - Time Series Decomposition Approach for Simulating Electricity Demand Profile

AU - Patidar, Sandhya

AU - Jenkins, David P.

AU - Peacock, Andrew

AU - McCallum, Peter

PY - 2020/4/1

Y1 - 2020/4/1

N2 - Electricity demand profiles of dwellings are mainly composed of various known (deterministic) and unknown (stochastic) processes. Effective data processing approaches (such as time series decomposition) are mainly used to simplify underlying patterns in the complex stochastic processes by fragmenting the different layers of hidden processes (referred as components of time series). This paper will demonstrate the performance of state-of-the-art STL (a Seasonal-Trend decomposition procedure based on Loess) techniques (Cleveland, Cleveland, McRae, & Terpenning, 1990), embedded within the framework of the HMM-GP model, in simulating dynamics of high-resolution electricity demand data. The method is applied to the case studies located in the Findhorn community.

AB - Electricity demand profiles of dwellings are mainly composed of various known (deterministic) and unknown (stochastic) processes. Effective data processing approaches (such as time series decomposition) are mainly used to simplify underlying patterns in the complex stochastic processes by fragmenting the different layers of hidden processes (referred as components of time series). This paper will demonstrate the performance of state-of-the-art STL (a Seasonal-Trend decomposition procedure based on Loess) techniques (Cleveland, Cleveland, McRae, & Terpenning, 1990), embedded within the framework of the HMM-GP model, in simulating dynamics of high-resolution electricity demand data. The method is applied to the case studies located in the Findhorn community.

U2 - 10.26868/25222708.2019.210541

DO - 10.26868/25222708.2019.210541

M3 - Conference contribution

SP - 1388

EP - 1395

BT - Proceedings of the 16th IBPSA Conference

A2 - Corrado, V.

A2 - Fabrizio, E.

A2 - Gasparella, A.

A2 - Patuzzi, F.

T2 - Building Simulation 2019

Y2 - 2 September 2019 through 4 September 2019

ER -

Time Series Decomposition Approach for Simulating Electricity Demand Profile

Abstract

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