Abstract: 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.

Abstract: 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.

Abstract: An information processing apparatus according to an embodiment includes a first estimator configured to estimate height information of a cloud based on data obtained by sensing the cloud, a divider configured to divide the cloud into a plurality of portions based on the height information, and a second estimator configured to estimate states of the cloud at target time for each of the portions.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

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.

Abstract: A bid-price determination apparatus includes an acquirer, an extractor, a predictor, a determiner, and an output device. The acquirer acquires a price history of a product before a current time in an electric power continuous session. The extractor extracts, from the acquired price history, a first feature quantity which is a feature quantity of the price history of the product. The predictor predicts, based on the extracted first feature quantity, a second feature quantity which is a feature quantity of price changes of the product after the current time. The determiner determines, based on the predicted second feature quantity, a transaction standard value which is a standard for a transaction of the product after the current time. The output device outputs the determined transaction standard value.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A weather forecasting apparatus includes a first feature quantity calculator, a first error calculator, a first model generator, a first coefficient calculator, and a first predicted value calculator. The first feature quantity calculator calculates a first feature quantity from a sky image. The first error calculator calculates a first error between a predicted numerical value and a measured value of a weather parameter. The first model generator generates a first model indicating a relation between the first feature quantity and the first error. The first coefficient calculator calculates a first coefficient from the first model and the first feature quantity. The first predicted value calculator calculates a first predicted value of the weather parameter at the prediction object day and time based on the first coefficient at the prediction object day and time and the predicted numerical value at the prediction object day and time.