DEMAND RESPONSE LOAD FORECASTER

• US 20140122181A1
• Filed: 01/07/2014
• Published: 05/01/2014
• Est. Priority Date: 09/15/2012
• Status: Abandoned Application

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Fisera, Radek, Rojicek, Jiri, Berka, Jan
• Application Number:
  US14/149,301
• Publication Number:
  US 20140122181A1
• Time in Patent Office: Days
• US Class Current: 705/7.31
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: Y04S 50/14 Marketing, i.e. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards

Abstract

A demand response system having an improved load forecaster connected to a decision engine. A basis of the improved forecaster may be an introduction of an explanatory variable which is a time-based shaping function that allows capturing a demand response (DR) lead and DR rebound effect, and the like, capturing a shape of load reduction, given by an applied DR action. The engine may receive information from the forecaster and utility relative to behavior of a DR customer, market price, renewable energy generation, grid status, and so on. The engine may provide optimal timing, selection of resources, and so forth, to a DR automation server, which in turn may provide DR signals to customers. The customers may provide data consumption data to a database. Electricity generation data may also be provided to the database. Selected relevant data from the database and weather information may go to the load forecaster.

Claims

1. A method of demand response (DR) load forecasting comprising:

providing a computer;

collecting and entering into the computer historical data pertaining to power consumption, outside temperature, humidity, calendar variables and/or information about DR events;

defining a non decreasing cyclic time-based shaping function from information about DR events;

bounding the time-based shaping function; and

altering an output of the time-based shaping function relative to different phases of a demand response event; and

wherein the defining the time-based shaping function, the bounding the time-based shaping function, and the altering of the time-based shaping function are performed, at least in part, by the computer.

2. The method of claim 1, wherein:

the time-based shaping function permits capturing a DR lead, a DR rebound effect, and a shape of load reduction; and

the shape of load reduction is given by an applied DR action.

3. The method of claim 2, wherein a shaping function represents relative time within a DR event incorporating a DR lead and a DR rebound.

4. The method of claim 3, wherein:

relative time can be non-linear with respect to real time;

relative time moves faster when more details to capture occur; and

relative time moves slower when fewer details to capture occur.

5. The method of claim 3, wherein energy demand data from one or more facilities participating in a demand response program determine a shape of a variable capturing a relative time within a DR event incorporating rebound and lead effects.

6. The method of claim 5, wherein the shape indicates how a statistical typical facility, determined to be statistically average of the two or more facilities, behaves immediately before the DR event, during the DR event, and immediately after the DR event.

7. A system for supporting a demand response (DR) decision engine, comprising:

a computer comprising a decision engine; and

a forecaster connected to the decision engine; and

wherein;

the decision engine provides an optimum timing and selection of DR resources based on a result from the forecaster;

variables are provided to the forecasters;

the variables comprise information based on time, DR signals and/or weather; and

a DR shaping function is developed from the variables based on DR signals.

8. The system of claim 7, wherein:

variables based on time comprise time of day, day and/or type of day; and

variables based on DR signals comprise a DR event start time, DR event stop time, DR event mode, DR lead effect, DR rebound effect and/or demand baseline.

9. The system of claim 8, wherein variables based on weather comprise outdoor air temperature, humidity, solar radiation, wind, past data, current data and/or forecast data.

10. The system of claim 8, wherein changes in electricity demand occur almost immediately after the DR event start time and DR event stop time.

11. The system of claim 7, wherein the electricity demand becomes more or less steady fixed after a transient, caused by devices turning on and off on consumed electricity, vanishes.

12. The system of claim 11, wherein the electricity demand, becoming more or less steady fixed, results in the DR shaping function having a slope approaching zero.

13. The system of claim 9, wherein a result from the forecaster is obtained by one or more defined regressors that provide a regression based on the variables.

14. A mechanism for demand response (DR) load forecasting, comprising:

a computer comprising a DR automation server;

a decision engine connected to the DR automation server; and

a demand forecaster connected to the decision engine; and

wherein the demand forecaster comprises a module for providing a DR time-based shaping function that captures a DR lead, a DR rebound effect, and a shape of a load reduction given by an applied DR action despite a duration of a DR event and a time of an occurrence of the DR event during a day of the DR event.

15. The mechanism of claim 14, further comprising an energy consumption database connected to the demand forecaster.

16. The mechanism of claim 15, further comprising:

a weather information module connected to the demand forecaster; and

wherein;

the DR automation server has an output for DR signals to one or more customers; and

the energy consumption database has an input for electricity consumption data from one or more customers.

17. The mechanism of claim 16, wherein the demand forecaster comprises:

a combiner connected to the decision engine;

a first predictor connected to the combiner; and

a second predictor connected to the combiner; and

wherein;

the energy consumption database is connected to the first and second predictors; and

the weather information module is connected to the first and second predictors.

18. The mechanism of claim 17, wherein:

the outputs of the first and second predictors are combined according to weights of the first and second predictors, respectively; and

the weights are computed according to preceding accuracies of the first and second predictors.

19. The mechanism of claim 14, wherein an output of the demand forecaster comprises a measure incorporating a distance of a currently estimated point from the middle of a DR event.

20. The mechanism of claim 16, wherein:

the electricity consumption data is collected concerning demand during one or more DR events from one or more customers having received DR signals from the automation server; and

the energy consumption data to the demand forecaster contribute to predicting behavior of a customer in response to one or more DR signals.