Abstract: A computer-Implemented method, system, and computer program product for optimizing production of an industrial facility. The industrial facility is designed to produce a predefinable quantity of at least one product. A model trained by machine learning is provided at a first time and the trained model is executed at a second time following the first time to generate a rolling forecast for a predefinable time interval. The predefinable time interval begins after the second time and the rolling forecast forecasts for any time within the time interval a quantity of the at least one product to be produced at this time. The rolling forecast is further processed by means of a further model to calculate a reforecast on the basis of the rolling forecast.

Abstract: Computer-assisted analysis of a data record from observations is provided. The data record contains, for each observation, a data vector that includes values of input variables and a value of a target variable. A neuron network structure is learned from differently initialized neuron networks based on the data record. The neuron networks respectively include an input layer, one or more hidden layers, and an output layer. The input layer includes at least a portion of the input variables, and the output layer includes the target variable. The neuron network structure outputs the mean value of the target variables of the output layers of the neuron networks. Sensitivity values are determined by the neuron network structure and stored. Each sensitivity value is assigned an observation and an input variable. The sensitivity value includes the derivative of the target variable of the assigned observation with respect to the assigned input variable.

Abstract: A method for computer-assisted determination of usage of electrical energy produced by a power generation plant such as a renewable power generation plant is provided. The method uses a plurality of neural networks having a different structure or being learned differently for calculating future energy amounts produced by a power generation plant. To do so, the energy outputs of the power generation plant forecasted by the plurality of the neural networks are used to build histograms. Based on the histograms, energy amounts for different confidence levels describing the likelihood of the availability of the energy amount are determined, and different uses are assigned to different energy amounts. Energy amounts having a higher likelihood of availability in the future are sold at higher prices than other energy amounts.

Abstract: Disclosed is a method for the computer-assisted modeling of a technical system. One or more output vectors are modeled dependent on one or more input vectors by the learning process of a neural network on the basis of training data of known input vectors and output vectors. Each output vector comprises one or more operating variables of the technical system, and each input vector comprises one or more input variables that influence the operating variable(s). The neural network is a feedforward network with an input layer, a plurality of hidden layers, and an output layer. The output layer comprises a plurality of output clusters, each of which consists of one or more output neurons, the plurality of output clusters corresponding to the plurality of hidden layers. Each output cluster describes the same output vector and is connected to another hidden layer.

Abstract: A biochemical analytical device and a biochemical analytical method for determining an analyte in a test sample are provided. In the technique, the biochemical analytical device includes a sample port to receive the test sample, a sensor to probe the test sample and to generate sensor data, and a processor. The sensor data corresponds to the analyte in the test sample. The processor receives the sensor data from the sensor and selects a non-linear function for the received sensor data. The processor fits the selected non-linear function to the sensor data. Additionally, the processor compares the fitted non-linear function to a reference data to determine the analyte in the test sample.

Abstract: A method and a device for controlling an energy-generating system are operated with a renewable energy source. In the method, a prediction about an energy yield of the energy-generating system is made for a predefined prediction time period, and a predefined area, using a learning system with an input vector and an output vector. The output vector includes operating variables for a multiplicity of successive future times of the time period. The input vector includes variables, influencing the operating variables, for a point in time from a multiplicity of points in time of a predefined observation time period. The input variables include at least three items of information for the observation time period and the predefined area. The energy-generating system is controlled on the basis of the generated prediction such that weather-conditioned fluctuations in the energy yield of the energy-generating system are reduced.

Abstract: A method for improving the usability of photovoltaic installations (PV installations) by taking account of shading information of adjacent PV installations for forecasting the power output by a relevant PV installation is provided. In particular, cloud movements and cloud shapes are taken into account. This improves the accuracy of the forecast. Here, it is advantageous that short-term forecasts in relation to e.g. the next 15 minutes are possible and a substitute energy source can be activated accordingly, in good time, prior to a dip in the power output by the PV installation. The invention can be used e.g. in the field of renewable energies, PV installations or smart grids.

Abstract: Computer-assisted analysis of a data record from observations is provided. The data record contains, for each observation, a data vector that includes values of input variables and a value of a target variable. A neuron network structure is learned from differently initialized neuron networks based on the data record. The neuron networks respectively include an input layer, one or more hidden layers, and an output layer. The input layer includes at least a portion of the input variables, and the output layer includes the target variable. The neuron network structure outputs the mean value of the target variables of the output layers of the neuron networks. Sensitivity values are determined by the neuron network structure and stored. Each sensitivity value is assigned an observation and an input variable. The sensitivity value includes the derivative of the target variable of the assigned observation with respect to the assigned input variable.

Abstract: A method for the computer-aided learning of a recurrent neural network for modeling a dynamic system which is characterized at respective times by an observable vector with one or more observables as entries is provided. The neural network includes both a causal network with a flow of information that is directed forwards in time and a retro-causal network with a flow of information which is directed backwards in time. The states of the dynamic system are characterized by first state vectors in the causal network and by second state vectors in the retro-causal network, wherein the state vectors each contain observables for the dynamic system and also hidden states of the dynamic system. Both networks are linked to one another by a combination of the observables from the relevant first and second state vectors and are learned on the basis of training date including known observables vectors.

Abstract: A method and a device for controlling an energy-generating system are operated with a renewable energy source. In the method, a prediction about an energy yield of the energy-generating system is made for a predefined prediction time period, and a predefined area, using a learning system with an input vector and an output vector. The output vector includes operating variables for a multiplicity of successive future times of the time period. The input vector includes variables, influencing the operating variables, for a point in time from a multiplicity of points in time of a predefined observation time period. The input variables include at least three items of information for the observation time period and the predefined area. The energy-generating system is controlled on the basis of the generated prediction such that weather-conditioned fluctuations in the energy yield of the energy-generating system are reduced.

Abstract: Modeling effectiveness of a verum includes dividing a group of patients into a placebo group and a verum group, defining a plurality of characteristics of the group of patients, and generating a model for the placebo group based on the plurality of characteristics. The method also includes generating a model for the verum group based on the plurality of characteristics, and isolating a placebo effect in the verum group in order to determine a pure verum effect.

Abstract: A method for computer-assisted determination of usage of electrical energy produced by a power generation plant such as a renewable power generation plant is provided. The method uses a plurality of neural networks having a different structure or being learned differently for calculating future energy amounts produced by a power generation plant. To do so, the energy outputs of the power generation plant forecasted by the plurality of the neural networks are used to build histograms. Based on the histograms, energy amounts for different confidence levels describing the likelihood of the availability of the energy amount are determined, and different uses are assigned to different energy amounts. Energy amounts having a higher likelihood of availability in the future are sold at higher prices than other energy amounts.

Abstract: Disclosed is a method for the computer-assisted modeling of a technical system. One or more output vectors are modeled dependent on one or more input vectors by the learning process of a neural network on the basis of training data of known input vectors and output vectors. Each output vector comprises one or more operating variables of the technical system, and each input vector comprises one or more input variables that influence the operating variable(s). The neural network is a feedforward network with an input layer, a plurality of hidden layers, and an output layer. The output layer comprises a plurality of output clusters, each of which consists of one or more output neurons, the plurality of output clusters corresponding to the plurality of hidden layers. Each output cluster describes the same output vector and is connected to another hidden layer.

Abstract: A method for improving the usability of photovoltaic installations (PV installations) by taking account of shading information of adjacent PV installations for forecasting the power output by a relevant PV installation is provided. In particular, cloud movements and cloud shapes are taken into account. This improves the accuracy of the forecast. Here, it is advantageous that short-term forecasts in relation to e.g. the next 15 minutes are possible and a substitute energy source can be activated accordingly, in good time, prior to a dip in the power output by the PV installation. The invention can be used e.g. in the field of renewable energies, PV installations or smart grids.

Abstract: A method for the computer-aided learning of a recurrent neural network for modeling a dynamic system which is characterized at respective times by an observable vector with one or more observables as entries is provided. The neural network includes both a causal network with a flow of information that is directed forwards in time and a retro-causal network with a flow of information which is directed backwards in time. The states of the dynamic system are characterized by first state vectors in the causal network and by second state vectors in the retro-causal network, wherein the state vectors each contain observables for the dynamic system and also hidden states of the dynamic system. Both networks are linked to one another by a combination of the observables from the relevant first and second state vectors and are learned on the basis of training date including known observables vectors.

Abstract: An approximation is determined for the future system behavior by a similarity analysis using a previously known behavior of the dynamic system, whereupon the future system behavior is determined by using the approximation for the future behavior of the dynamic system as well as a neuronal network structure, especially a causal retro-causal network (causality analysis).

Abstract: An approximation is determined for the future system behavior by a similarity analysis using a previously known behavior of the dynamic system, whereupon the future system behavior is determined by using the approximation for the future behavior of the dynamic system as well as a neuronal network structure, especially a causal retro-causal network (causality analysis).