Abstract: A CPU performs multiple regression analysis using a first regression model to calculate a tentative degree of contribution of each target facility to the total power consumption. The CPU calculates tentative power consumption of the target facility using the tentative degree of contribution of the target facility. The CPU calculates power consumption of a non-monitored facility by subtracting the total value of the tentative power consumption of the target facility from the total power consumption. The CPU classifies time-series data of the power consumption of the non-monitored facility into a plurality of clusters. The CPU performs multiple regression analysis using a second regression model to determine the degree of contribution of each of the target facilities. The CPU determines the power consumption of the target facility using the determined degree of contribution.

Abstract: An area evaluation unit (51) acquires for each area of a plurality of areas, a predictive value of each area which is predicted to be measured when a device operates in a space that includes the plurality of areas. A building evaluation unit (52) acquires an overall predictive value which is a predictive value of the entire space calculated using a plurality of predictive values of the plurality of areas. Further, the area evaluation unit (51) evaluates the predictive value of each area, using a partial evaluation criterion which is an evaluation criterion applied to each area. Further, the building evaluation unit (52) evaluates the overall predictive value, using an overall evaluation criterion which is an evaluation criterion applied to the entire space.

Abstract: An occupancy model selection apparatus (100) includes an occupancy model selection unit (103). The occupancy model selection unit (103) evaluates each of a plurality of occupancy models which predict occupancy situation in a simulation target area during a simulation period, based on a power consumption value corresponding to each of the plurality of occupancy models and on a past input condition; and based on a result of evaluation of each of the plurality of occupancy models, selects an occupancy model that generates a simulation result relatively close to an actual power consumption value in the simulation period corresponding to the past input condition in the simulation target area.

Abstract: A number-of-allowable-users calculation unit (212) calculates, as the number of allowable users of a room, the number of persons with which a distance equal to or greater than a limit distance can be secured in the room, based on limit distance data indicating the limit distance defined as a distance to be kept between persons and room data indicating information on the room to be used by one or more persons.

Abstract: An image acquisition unit (211) acquires a photographed image obtained by photographing an air-conditioning indoor unit including a plurality of air outlets. An image collation unit (212) collates a template image wherein an air-conditioning indoor unit of a same type as the air-conditioning indoor unit is shown with the photographed image. An air outlet identification unit (213) determines an air outlet identifier of each air outlet in the photographed image based on a collation result, and on air outlet identification data indicating an air outlet identifier to be associated with each air outlet in the template image. An identification result display unit (214) displays the photographed image by superimposing the air outlet identifier on each air outlet in the photographed image.

Abstract: A power consumption estimation device (10) which estimates power consumption of each of one object facility (100) or more whose operation statuses can be monitored includes: a total power vector generation unit (32) to acquire time-series data of total power consumption; a status matrix generation unit (34) to acquire time-series data of an operation parameter of the one object facility (100) or more; a reference signal generation unit (36) to generate one reference signal or more; a contribution degree estimation unit (38) to perform multiple regression analysis by taking the total power consumption as an objective variable, and the operation parameter of the one object facility (100) or more and a component value of the one reference signal or more, as explanatory variables, to thereby calculate a contribution degree of each of the one object facility (100) or more; and a breakdown calculation unit (42) to multiply the contribution degree of the object facility (100) and the operation parameter of the objec

Abstract: An apparatus control unit (54) executes baseline judgment control of canceling power consumption limitation for a management target apparatus (20) throughout a predetermined judgement period within a demand term. A demand control execution judgment unit (48) judges whether or not a value of a baseline BL, which is an estimated value based on an integrated value of power consumption per building in the judgment period, exceeds a demand control execution value P_Dmd by a demand-term end point. When it is judged that the value of the baseline BL exceeds the demand control execution value P_Dmd by the demand-term end point, the apparatus control unit (54) executes demand control of limiting power consumption of the management target apparatus (20), after the judgment period.

Abstract: A monitoring system (10) includes an operation record information storage unit (21) to accumulate operation record information about an operating state of equipment (2) collected by an operation record information collection unit (11); an operation model generation unit (12) to generate, for each piece of the equipment, an operation model indicating an operating state of each piece of the equipment (2) based on the operation record information; and an equipment selection processing unit (13) to cause a user interface unit (14) to display operation models, and select the equipment (2) corresponding to an operation model selected by an administrator from the displayed operation models, as control target equipment to be a target of demand control.

Abstract: A power consumption estimation device (10) which estimates power consumption of each of one object facility (100) or more whose operation statuses can be monitored includes: a total power vector generation unit (32) to acquire time-series data of total power consumption; a status matrix generation unit (34) to acquire time-series data of an operation parameter of the one object facility (100) or more; a reference signal generation unit (36) to generate one reference signal or more; a contribution degree estimation unit (38) to perform multiple regression analysis by taking the total power consumption as an objective variable, and the operation parameter of the one object facility (100) or more and a component value of the one reference signal or more, as explanatory variables, to thereby calculate a contribution degree of each of the one object facility (100) or more; and a breakdown calculation unit (42) to multiply the contribution degree of the object facility (100) and the operation parameter of the objec

Abstract: In a method to detect a stationary person by a tracking method using images, image feature quantities of objects, other than persons, are registered in advance, and when the image feature quantity of a stationary region of an image does not resemble the preregistered ones, the stationary region is recognized as a person. Hence, there has been a problem that it requires a large volume of image feature quantities to be collected and registered in advance. A person detecting device and a person detecting method which relate to the present invention determine a stationary region on the basis of the distance between the position of a first moving-object region extracted from an image and the position of a second moving-object region extracted from an image previous to the image of the first moving-object region. The presence or absence of a person is determined by using the image feature variation of the stationary region.

Abstract: In a method to detect a stationary person by a tracking method using images, image feature quantities of objects, other than persons, are registered in advance, and when the image feature quantity of a stationary region of an image does not resemble the preregistered ones, the stationary region is recognized as a person. Hence, there has been a problem that it requires a large volume of image feature quantities to be collected and registered in advance. A person detecting device and a person detecting method which relate to the present invention determine a stationary region on the basis of the distance between the position of a first moving-object region extracted from an image and the position of a second moving-object region extracted from an image previous to the image of the first moving-object region. The presence or absence of a person is determined by using the image feature variation of the stationary region.