Demand forecasting device, demand forecasting method and non-transitory computer readable medium

Patent metadata

Field	Value
Publication number	US-2019139059-A1
Application number	US-201815918373-A
Country	US
Kind code	A1
Filing date	Mar 12, 2018
Priority date	Nov 7, 2017
Publication date	May 9, 2019
Grant date	—

Abstract

According to one embodiment, a demand forecasting device includes a location model generator and an overall model generator. The location models generate each providing a forecasted value of demand for a geographical region including a plurality of locations, based on weather data for either of the plurality of locations and previous value of demand for the geographical region. The overall model generator generates an overall model based on the location models and coefficients that are determined based on size of impact to the forecasted value of demand, by the weather data of the locations.

First claim

1 . A demand forecasting device comprising:
a location model generator generating location models each providing a forecasted value of demand for a geographical region including a plurality of locations, based on weather data for either of the plurality of locations and previous value of demand for the geographical region; and an overall model generator generating an overall model based on the location models and coefficients that are determined based on size of impact to the forecasted value of demand, by the weather data of the locations.

2 . The demand forecasting device according to claim 1 , wherein
the weather data includes more than one item for observed values of weather or predicted values of weather.

3 . The demand forecasting device according to claim 2 , wherein
the overall model generated by the overall model generator includes a plurality of location models generated by the local model generator.

4 . The demand forecasting device according to claim 2 , wherein
the overall model generated by the overall model generator includes a linear combination formed by multiplying the location model with the coefficient.

5 . The demand forecasting device according to claim 2 , wherein
the overall model generator uses sparse regularization to calculate the coefficients and to select the location models that are included in the overall model, based on the values of the coefficients.

6 . The demand forecasting device according to claim 2 , further comprising
a forecasting unit which calculates the forecasted value of demand in the geographical region by entering the predicted value of weather and the previous value of demand into the overall model.

7 . The demand forecasting device according to claim 6 , wherein
the predicted value of weather entered into the overall model by the forecasting unit is the predicted value of weather for a target date time of demand forecast.

8 . The demand forecasting device according to claim 7 , wherein
the weather data and the previous value of demand used for generation of the location model is the weather data and the previous value of demand for a plurality of previous date times which satisfy specific conditions.

9 . The demand forecasting device according to claim 8 , wherein
the overall model generator generates the overall model which can provide the forecasted value of demand for the specific conditions.

10 . The demand forecasting device according to claim 9 , wherein
the forecasting unit selects the overall model to be used based on the specific conditions of the target date time of demand forecast.

11 . The demand forecasting device according to claim 2 , further comprising
a visualizing unit which displays a first graph which is a scattered plot diagram showing a relation between the previous value of demand and the weather data.

12 . The demand forecasting device according to claim 11 , wherein
the visualizing unit displays a regression line or a approximated curve overlapping the scattered plot diagram.

13 . The demand forecasting device according to claim 11 , wherein
the visualizing unit displays a map which shows size of the coefficients for each location corresponding to the predicted value of weather or the observed value of weather used in generation of the location model.

14 . The demand forecasting device according to claim 11 , wherein
the visualizing unit displays a second graph which shows the forecasted value of demand and the previous value of demand for a plurality of date times.

15 . The demand forecasting device according to claim 2 , further comprising
a weather forecasting unit which calculates the predicted value of weather for the location belonging to the geographical region based on the weather data.

16 . The demand forecasting device according to claim 15 , wherein
the weather forecasting unit calculates the predicted value of weather for a specific date time in future.

17 . The demand forecasting device according to claim 1 , wherein
the location model generator generates the location model using a Generalized Additive Model.

18 . The demand forecasting device according to claim 1 , wherein
the previous value of demand is a previous value of demand for electricity and the forecasted value of demand is demand of electricity.

19 . A demand forecasting method comprising the steps of:
generating location models each providing a forecasted value of demand for a geographical region including a plurality of locations, based on predicted values of weather or observed values of weather for either of the plurality of locations and previous values of demand for the geographical region; generating an overall model based on the location models and coefficients that are determined based on size of impact to the forecasted value of demand, by the predicted value of weather or the observed value of weather in the location; calculating the forecasted value of demand in the geographical region by entering the predicted value of weather and the previous value of demand into the overall model.

20 . A non-transitory computer readable medium having a computer program stored therein which causes a computer to execute processes comprising:
generating location models each providing a forecasted value of demand for a geographical region including a plurality of locations, based on a predicted value of weather or a observed value of weather for either of the plurality of locations and previous value of demand for the geographical region; generating an overall model based on the location models and coefficients that are determined based on size of impact to the forecasted value of demand, by the predicted value of weather or the observed value of weather in the location; calculating the forecasted value of demand in the geographical region by entering the predicted value of weather and the previous value of demand into the overall model.

Assignees

• Toshiba Kk

Inventors

• Shiga Yoshiaki
• Shin Hiromasa
• Kakimoto Mitsuru
• Takada Masaaki

Classifications

• G06N20/00

  Machine learning · CPC title

• G06N3/08

  Learning methods · CPC title

• G06Q30/0202 Primary

  Market predictions or forecasting for commercial activities · CPC title

• G06N20/10

  using kernel methods, e.g. support vector machines [SVM] · CPC title

• G06N99/005

  Physics · mapped topic