Kabushiki Kaisha Toshiba

Toshiba Energy Systems & Solutions Corporation

Hiromasa Shin

Mitsuru Kakimoto

Yoshiaki Shiga

Yusuke Endoh

Yuki Hayashi

Hideyuki Aisu

Masaaki Takada

Mikito Iwamasa

Mutsumi Fujihara

Takufumi Yoshida

Yoshiaki Hasegawa

Shigeta Kuninobu

Yasuhiro Ogura

Ryoichi Ichikawa

Akinori Ohta

Kenji Kawamata

Fang Ni

Kotaro Kimura

Ichiro Toyoshima

According to one embodiment, an information processing device includes a first processor configured to calculate, based on first information indicating a target time point at which weather prediction data is to be acquired and which is a relative time point with respect to a first time point, second information indicating the target time point being a relative time point with respect to a second time point at which weather prediction is started by a prediction device, the prediction device being configured to generate weather prediction data through weather prediction.

Information processing device, information processing method, and non-transitory computer readable medium

According to one embodiment, a prediction device includes: first processing circuitry and second processing circuitry. The first processing circuitry corrects a demand result value on a particular date on a basis of an adjustment coefficient depending on the particular date; and generates a prediction model of a demand on a basis of the corrected demand result value on the particular date and a demand result value on a non-particular date. The second processing circuitry calculates a demand predicted value on a date of a prediction target on a basis of demand result values on one or more dates, an adjustment coefficient depending on a particular date among the one or more dates, and the prediction model. The second processing circuitry inversely corrects the demand predicted value using an adjustment coefficient depending on the date of the prediction target if the date of the prediction target is a particular date.

Prediction device, prediction method, and non-transitory computer readable medium

According to one embodiment, an information processing device includes processing circuitry configured to calculate a first index based on a plurality of pieces of first prediction data on an amount of electric power generated by a first electric power generation resource and a penalty imposed when an amount of electric power supplied to an electric power system is insufficient or excessive for a first amount of electric power which corresponds to a planned value of electric power generation amount, the first index being an index related to a yield obtained in a case that the first amount of electric power is tentatively bid in a first market; and determine a second amount of electric power to be bid in the first market based on the first index.

According to one embodiment, an information processing apparatus includes: a processor configured to select a first case based on subject data including at least one feature, and acquire a first prediction value that is a value of an objective variable included in the first case; a first estimator configured to estimate frequency data indicating frequencies of observation values of the objective variable, based on a history of observation values of the objective variable; a second estimator configured to estimate first frequency data indicating frequencies of first prediction values, based on a history of first prediction values acquired before the first prediction value is acquired; and a corrector configured to correct the first prediction value acquired by the processor, based on the frequency data and the first frequency data.

Information processing apparatus, information processing method, and non-transitory computer readable medium

A prediction model generation device has a first storage unit that stores a plurality of explanatory variables, a second storage unit that stores a plurality of objective variables, an input unit that inputs instruction information on classification, a class generation unit that generates a plurality of classes based on the instruction information, and a prediction model calculation unit that calculates a plurality of prediction models corresponding to the plurality of classes. The prediction model calculation unit has a learning data set extraction unit that extracts a learning data set corresponding to each of the plurality of classes from among the plurality of explanatory variables and the plurality of objective variables.

Prediction model generation device, prediction model generation method, and recording medium

A solar irradiance intensity estimation apparatus has an estimation model generation unit that generates estimation models of solar radiation intensities at a plurality of observation points based on observed cloud state data and solar radiation intensities observed at the plurality of observation points, an estimation model interpolation unit that generates an estimation model of a solar irradiance intensity at a target point based on the estimation models of solar radiation intensities at the plurality of observation points, and a solar irradiance intensity estimation unit that estimates a solar irradiance intensity at the target point based on a reflection intensity at the target point obtained from the cloud state data and the estimation model of a solar irradiance intensity at the target point.

Solar irradiance intensity estimation apparatus, solar irradiance intensity estimation system, and solar irradiance intensity estimation method

According to one embodiment, a probabilistic weather forecasting device includes processing circuitry configured to generate, based on a plurality of ensembles each including predicted values of the first meteorological variable and a plurality of pieces of observed data each including a observed value of the second meteorological variable, a simultaneous probability density distribution of a predicted value of the first meteorological variable and an observed value of the second meteorological variable; and configured to generate, based on the simultaneous probability density distribution, first probability density distributions each being a probability density distribution of an observed value of the second meteorological variable obtained under a condition that each of a plurality of predicted values of the first meteorological variable are given at a prediction target time, and synthesize the first probability density distributions to generate a second probability density distribution.

Probabilistic weather forecasting device, probabilistic weather forecasting method, and non-transitory computer readable medium

An information processing apparatus has an input data acquisition unit that acquires a plurality of input data, a fault tolerance requirement acquisition unit that acquires a fault tolerance requirement for the plurality of input data, a training data definition unit that defines an output which satisfies the fault tolerance requirement, a fault pattern generation unit that generates a plurality of fault patterns which include at least one of the plurality of input data based on the plurality of input data and the fault tolerance requirement, a model update unit that updates the information processing model so as to tolerate a fault of the input data satisfying the fault tolerance requirement, and an execution control unit that applies the plurality of input data to the information processing model updated by the model update unit and executes the information processing model.

Information processing apparatus, information processing method, and recording medium

An object retrieval apparatus includes a storage and a retrieval. The storage stores first to N-th space index information relating to Xobjects to Xobjects which are selected in a descending order of an object size from among a plurality of objects, and in which a value of X(where T is a value from 1 to N) decreases as a value of T increases. The retrieval processor divides a sight range into a plurality of pieces according to distance from a sight point, assign a different one of the first to N-th space index information to each divided range such that a smaller index number of the space index information is assigned to the divided range closer to the sight point, and specifies an object having a size visible from the sight point among objects existing in each divided range by retrieving the assigned space index information.

Object retrieval apparatus and object retrieval method

A power control apparatus has a peak cut prediction unit that predicts a period of time to impose a restriction on power supply from a power system to a consumer based on a predetermined condition, a threshold setting unit that sets a predetermined threshold such that a power supply amount from the power system to the consumer does not exceed the threshold in the period of time predicted by the peak cut prediction unit, and a charge/discharge control command unit that stops power supply from the power system to the consumer and causes a power storage device to be discharged by an amount of demand power exceeding the threshold when demand power of the consumer reaches the threshold in the period of time predicted by the peak cut prediction unit.

Power control apparatus, power control method, and recording medium

According to one embodiment, an aggregation management apparatus includes processing circuitry configured to acquire position information and performance information of each photovoltaic power generation facility in a set of photovoltaic power generation facilities from a storage; and calculate an evaluation value of a predictive error of a total generated energy of the set of photovoltaic power generation facilities, based on the position information, the performance information, and a correlation level of the predictive errors of solar radiation information depending on a distance between two locations.

Aggregation management apparatus and aggregation management method

According to one embodiment, an object retrieval apparatus includes a query acceptance unit and a collision decision unit. The query acceptance unit is configured to accept a retrieval query indicating (N&#x2212;1)-dimensional surface in N-dimensional space (N is an integral number larger than or equal to three). The collision decision unit is configured to decide whether the (N&#x2212;1)-dimensional surface intersects N-dimensional cuboid positioned in the N-dimensional space. The collision decision unit decides by using a plurality of decision functions. The plurality of decision functions includes zero-th&#x2dc;(N&#x2212;1)-th decision functions to decide whether at least a part of at least one of X-dimensional face (X is all integral numbers larger than or equal to zero, and smaller than or equal to (N&#x2212;1)) of the N-dimensional cuboid is included in the (N&#x2212;1)-dimensional surface.

Apparatus and a method for retrieving an object

A state-transition system includes a plurality of states and a first transition representing a state-change based on an external event occurred into a system to be checked. A plurality of requirement items includes a precondition and a postcondition in correspondence with the external event. By partially selecting the precondition and the postcondition from the plurality of requirement items, a state map to map a state onto truth values of the precondition and the postcondition is generated. By contracting the state-transition system with the state map, a quotient state-transition system is generated. The quotient state-transition system includes a plurality of second transitions among a plurality of groups each having states. By tracing each second transition in the quotient state-transition system, a representative transition path is generated. The representative transition path is regenerated as a transition path in the state-transition system. The transition path is output as a test case.

Apparatus and a method for generating a test case

A method of the present invention includes: inputting a state transition diagram that represents first transitions between a plurality of states; inputting a mapping function that maps a given state to a value within a certain range; mapping each of the states with the mapping function to obtain mapping values, and perform grouping of the states into a plurality of groups based on the mapping values; for each of pairs of two groups obtained by combining two of the groups, setting a second transition from one group of the two groups to the other group when there is at least one first transition between therein; generating a representative transition path which is a sequence of the second transitions by tracing sequentially the second transitions; converting the representative transition path to a transition path in the state transition diagram; outputting the transition path as the test case.

Growing community of inventors

Yokohama, Japan

Hiromasa Shin