Siemens Aktiengesellschaft

Ralph Grothmann

Hans-Georg Zimmermann

Christoph Tietz

Kai Heesche

Terrence Chen

Per Egedal

Joachim Bamberger

Volkmar Sterzing

Michael Metzger

Thomas A Runkler

Martin Bischoff

Oliver Hennig

Ariane Sutor

Johann Kim

Eberhard Ritzhaupt-Kleissl

Jochen Cleve

Clemens Hoffmann

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.

Computer-assisted analysis of a data record from observations

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.

Method of computer-assisted determination of the usage of electrical energy produced by a power generation plant, particularly a renewable power generation plant

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.

Method for the computer-assisted modeling of a wind power installation or a photovoltaic installation with a feed forward neural network

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.

Method and device for controlling an energy-generating system which can be operated with a renewable energy source

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.

Method and device for determining the power output by a photovoltaic installation

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.

Method for the computer-aided learning of a recurrent neural network for modeling a dynamic system

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).

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Munich, Germany

Ralph Grothmann