Predictive analytics for building power demand : day-ahead forecasting and anomaly prediction
department or school: Applied Science, Faculty of
department or school: Engineering, School of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Lin, Jing
issued: 2021-03-10T17:53:54Z
issued: 2021
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: The rising energy demand of buildings contributes to global resource consumption and greenhouse gas emissions. Understanding the energy consumption habits of a building is the first and most crucial step in order to achieve energy efficiency. Forecasting and anomaly prediction of power demand play an essential role in the electric industry, as it provides the basis for making decisions in power system planning and operation. However, there are several challenges in predictive analytics to improve energy efficiency. For instance, the information in the forecast needs to be interpreted by a person with domain expertise. Moreover, automated interpretation of upcoming abnormal behaviors needs ground-truth labeling, but labels are not always available from power meter data. To address those problems, a novel predictive power demand analytics methodology (PPDAM) is proposed in this thesis, based on long short-term memory neural networks and symbolic aggregate approximation. The main target of PPDAM is to identify upcoming normal and anomalous demand patterns, through mining historical power demand and temperature data, and day-ahead power demand forecast. The PPDAM consists of two modules: power demand forecasting and anomaly prediction. The power demand forecasting module is utilized to predict day-ahead power profiles (time sequences), as a baseline for pattern generation and anomaly prediction. In the module of anomaly prediction, the historical and predicted time-series profiles are first transformed into patterns and later retrieved from a system of labeled repositories. The patterns are classified as normal or anomalous based on their frequency of appearance in the pattern repositories. The power demand data cover the total electrical power consumption in seven buildings at the University of British Columbia. The experimental results indicate that a power forecast could be mapped as different foreseeable demand patterns, each with a specific probability of occurrenc…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/77497
note: PREDICTIVE ANALYTICS FOR BUILDING POWER DEMAND: DAY-AHEADFORECASTING AND ANOMALY PREDICTIONbyJing LinB. Eng., Jiangnan University, 2018A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF APPLIED SCIENCEinTHE COLLEGE OF GRADUATE STUDIES(Electrical Engineering)The University of British Columbia(Okanagan)February 2021c© Jing Lin, 2021The following individuals certify that they have read, and recommend to the College of GraduateStudies for acceptance, the thesis entitled:Predictive Analytics for Building Power Demand: Day-Ahead Forecasting and Anomaly Predic-tionsubmitted by Jing Lin in partial fulfillment of the requirements forthe degree of Master of Applied Science.Examining Committee:Zheng Liu, School of EngineeringsupervisorKasun Hewage, School of EngineeringSupervisory Committee MemberChen Feng, School of EngineeringSupervisory Committee MemberJian Liu, School of EngineeringUniversity ExamineriiAbstractThe rising energy demand of buildings contributes to global resource consumption and greenhousegas emissions. Understanding the energy consumption habits of a building is the first and mostcrucial step in order to achieve energy efficiency. Forecasting and anomaly prediction of powerdemand play an essential role in the electric industry, as it provides the basis for making deci-sions in power system planning and operation. However, there are several challenges in predictiveanalytics to improve energy efficiency. For instance, the information in the forecast needs to beinterpreted by a person with domain expertise. Moreover, automated interpretation of upcomingabnormal behaviors needs ground-truth labeling, but labels are not always available from powermeter data.To address those problems, a novel Predictive Power Demand Analytics methodology (PPDAM)is proposed in this thesis, based on long short-term memory neural networks and symbolic aggre-gate approximation. The main target of PPDAM is to identify upcoming normal and anomalousd…
has type: Thesis/Dissertation
date issued: 2021-05
is shown at: 10.14288/1.0396087
language: eng
degree discipline: Electrical Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/77497