Metered residential cooling loads: comparison of three models

Author

Eto, Joseph ; Moezzi, Mithra

Author_Institution

Lawrence Berkeley Lab., CA, USA

Volume

12

Issue

2

fYear

1997

fDate

5/1/1997 12:00:00 AM

Firstpage

858

Lastpage

868

Abstract

End-use metered data collected for five years from 350 California residences are used to compare three types of models for allocating estimates of annual residential central air conditioning energy use to hours of the year. We assess how well the model fits the data for daily energy, peak demand, and demand coincident with system peak. A model which couples regression-based functions for daily load estimation with hourly estimation according to a library of load profiles is judged to have a slightly better fit to the data than a model that estimates hourly loads directly from hourly functions derived from linear regressions. Concerns regarding the applicability of end-use metered data for long-term resource planning are described

Keywords

air conditioning; power consumption; statistical analysis; California; annual energy use estimation; central air conditioning energy use; daily energy; daily load estimation; end-use metered data; hourly estimation; linear regressions; load profiles; long-term resource planning; metered residential cooling loads; peak demand; regression-based functions; Central air conditioning; Cooling; Demography; Economic forecasting; Laboratories; Load forecasting; Predictive models; Shape; Uncertainty; Weather forecasting;

fLanguage

English

Journal_Title

Power Systems, IEEE Transactions on

Publisher

ieee

ISSN

0885-8950

Type

jour

DOI

10.1109/59.589727

Filename

589727