The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Dec. 11, 2018

Filed:

Dec. 30, 2015
Applicant:

Enernoc, Inc., Boston, MA (US);

Inventors:

Husain Al-Mohssen, Dedham, MA (US);

Richard R. Paradis, Franklin, MA (US);

Angela S. Bassa, Stoneham, MA (US);

Assignee:

ENERNOC, INC., Boston, MA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G05D 3/12 (2006.01); H02J 3/00 (2006.01); G05B 13/02 (2006.01);
U.S. Cl.
CPC ...
H02J 3/00 (2013.01); G05B 13/026 (2013.01); H02J 2003/003 (2013.01); Y04S 10/54 (2013.01);
Abstract

A method for predicting when energy consumption on a grid will exceed normal production capacity for buildings within the grid including retrieving a plurality of baseline energy use data sets for the buildings from a baseline data stores; generating data sets for each of the buildings, each set comprising energy consumption values along with corresponding time and outside temperature values, where the energy consumption values within each set are shifted by one of a plurality of lag values relative to the corresponding time and outside temperature values, and where each lag value is different; performing a regression analysis on each set to yield corresponding regression model parameters and a corresponding residual; determining a least valued residual indicating a corresponding energy lag for each of the buildings; and using outside temperatures, regression model parameters, and energy lags for all of the buildings to estimate a cumulative energy consumption for the buildings, and to predict the time when energy consumption on the grid will exceed normal production capacity.


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