A unified inverse modeling framework for whole-building energy interval data: Daily and hourly baseline modeling and short-term load forecasting

Saurabh Jalori, T Agami Reddy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)

Abstract

A considerable amount of literature on the application of inverse methods to building energy data has been published in the last three decades. These inverse models serve a variety of purpose such as baseline modeling for monitoring and verification (M&V) projects at monthly, daily, and hourly time scales; condition monitoring; fault detection and diagnosis; supervisory control; and load forecasting, to name a few. Usually, these models have distinct model structures, and separate models are developed depending on the specified need. This paper proposes a novel inverse modeling framework that attempts to unify some of the above application-specific models by allowing a single model to be incrementally enhanced. Specifically, we begin by clustering the energy interval data of a building, identifying scheduling day types and removing any outliers. This aspect of the analysis is described in the companion paper by Jalori and Reddy (2015). Subsequently, the first level is to identify models for each day type using daily average values of energy use and climatic variables; this is adequate in many M&V projects. These models are then extended to hourly time scales by including additional terms in the model that capture diurnal variations of the climatic variables and the building hourly scheduling about the daily mean value; this level is appropriate for M & V and for condition monitoring. Finally, periodic autoregressive terms are added to the model to enhance prediction accuracy for short-term load forecasting, useful for demand response programs, or for short-term supervisory control. The application of the proposed methodology is illustrated with year-long data from two different buildings, one synthetic (the Department of Energy medium-office prototype) building and an actual office building.

Original languageEnglish (US)
Title of host publicationASHRAE Transactions
PublisherAmer. Soc. Heating, Ref. Air-Conditoning Eng. Inc.
Pages156-169
Number of pages14
Volume121
ISBN (Print)9781939200013
StatePublished - 2015
Event2015 ASHRAE Annual Conference - Atlanta, United States
Duration: Jun 27 2015Jul 1 2015

Other

Other2015 ASHRAE Annual Conference
CountryUnited States
CityAtlanta
Period6/27/157/1/15

Fingerprint

Office buildings
Condition monitoring
Scheduling
Model structures
Fault detection
Failure analysis
Monitoring

ASJC Scopus subject areas

  • Mechanical Engineering
  • Building and Construction

Cite this

Jalori, S., & Reddy, T. A. (2015). A unified inverse modeling framework for whole-building energy interval data: Daily and hourly baseline modeling and short-term load forecasting. In ASHRAE Transactions (Vol. 121, pp. 156-169). Amer. Soc. Heating, Ref. Air-Conditoning Eng. Inc..

A unified inverse modeling framework for whole-building energy interval data : Daily and hourly baseline modeling and short-term load forecasting. / Jalori, Saurabh; Reddy, T Agami.

ASHRAE Transactions. Vol. 121 Amer. Soc. Heating, Ref. Air-Conditoning Eng. Inc., 2015. p. 156-169.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Jalori, S & Reddy, TA 2015, A unified inverse modeling framework for whole-building energy interval data: Daily and hourly baseline modeling and short-term load forecasting. in ASHRAE Transactions. vol. 121, Amer. Soc. Heating, Ref. Air-Conditoning Eng. Inc., pp. 156-169, 2015 ASHRAE Annual Conference, Atlanta, United States, 6/27/15.
Jalori S, Reddy TA. A unified inverse modeling framework for whole-building energy interval data: Daily and hourly baseline modeling and short-term load forecasting. In ASHRAE Transactions. Vol. 121. Amer. Soc. Heating, Ref. Air-Conditoning Eng. Inc. 2015. p. 156-169
Jalori, Saurabh ; Reddy, T Agami. / A unified inverse modeling framework for whole-building energy interval data : Daily and hourly baseline modeling and short-term load forecasting. ASHRAE Transactions. Vol. 121 Amer. Soc. Heating, Ref. Air-Conditoning Eng. Inc., 2015. pp. 156-169
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