Time Series Decomposition Approach for Simulating Electricity Demand Profile

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publicationProceedings of the 16th IBPSA Conference
EditorsV. Corrado, E. Fabrizio, A. Gasparella, F. Patuzzi
Pages1388-1395
ISBN (Electronic)978-1-7750520-1-2
DOIs
Publication statusPublished - 1 Apr 2020
EventBuilding Simulation 2019: 16th IBPSA International Conference and Exhibition - Angelicum Congress Centre, Rome, Italy
Duration: 2 Sep 20194 Sep 2019
http://www.ibpsa.org/proceedings/BS2019/BS2019_210541.pdf

Conference

ConferenceBuilding Simulation 2019
Abbreviated titleBS2019
CountryItaly
CityRome
Period2/09/194/09/19
Internet address

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