Back to results
US 2007/0185823 A1Published application
Load prediction based on-line and off-line training of neural networks
AssigneeSiemens Energy, Inc.
InventorDingguo Chen
Life of this patent
Estimating the term…
Abstract
A method and system is provided for predicting loads within a power system through the training of on-line and an off-line neural networks. Load data and load increments are used with an on-line load prediction scheme to generate predicted load values to optimize power generation and minimize costs. This objective is achieved by employing a method and system which predicts short term load trends through the use of historical load data and short term load forecast data.
Claims21
A method of energy management in a power system comprising the steps of:
- Training an off-line neural network with load data;
- Training the off-line neural network with a first set of on-line predicted load values;
- Using the first set of on-line predicted values to predict a second set of predicted load values;
- Training an on-line neural network with load data;
- Training the on-line neural network with a third set of on-line predicted load values
- Using the third set of on-line predicted load values to predict a fourth set of on-line predicted load values
- Comparing the second set of predicted load values with actual load values and incrementing a first counter if the difference between the second set of predicted load values and the actual load values are within an acceptable range;
- Comparing the fourth set of predicted load values with actual load values and incrementing a second counter if the difference between the fourth set of predicted load values and the actual load values are within an acceptable range;
- and
- Calculating final predicted load values based on the values of the first and second counter.
7 more elements
A computer-readable medium having stored thereon instructions which when executed by a processor, cause the processor to perform the steps of:
- Training an off-line neural network with load data;
- Training the off-line neural network with a first set of on-line predicted load values;
- Using the first set of on-line predicted values to predict a second set of predicted load values;
- Training an on-line neural network with load data;
- Training the on-line neural network with a third set of on-line predicted load values
- Using the third set of on-line predicted load values to predict a fourth set of on-line predicted load values
- Comparing the second set of predicted load values with actual load values and incrementing a first counter if the difference between the second set of predicted load values and the actual load values are within an acceptable range;
- Comparing the fourth set of predicted load values with actual load values and incrementing a second counter if the difference between the fourth set of predicted load values and the actual load values are within an acceptable range;
- and
- Calculating final predicted load values based on the values of the first and second counter.
7 more elements
A system for predicting loads within an energy management system of a power system, comprising:
- a processor for executing energy management applications;
- an off-line neural network in communication with the processor;
- an on-line neural network in communication with the processor and the off-line neural network;
- a load database for storing current and historical load values and predicted load values and accessible by the processor, the off-line neural network;
- an on-line load prediction module in communication with the processor, the off-line neural network and load database;
- a neural network training module in communication with the on-line neural network and off-line neural network;
- a decision algorithm module for selecting a load prediction scheme;
- a memory in communication with the processor for storing energy related data;
- and
- an energy management module for executing energy management functions comprising,
- Training the off-line neural network with a first set of on-line predicted load values;
- Using the first set of on-line predicted values to predict a second set of predicted load values;
- Training an on-line neural network with load data;
- Training the on-line neural network with a third set of on-line predicted load values
- Using the third set of on-line predicted load values to predict a fourth set of on-line predicted load values
- Comparing the second set of predicted load values with actual load values and incrementing a first counter if the difference between the second set of predicted load values and the actual load values are within an acceptable range;
- Comparing the fourth set of predicted load values with actual load values and incrementing a second counter if the difference between the fourth set of predicted load values and the actual load values are within an acceptable range;
- and
- Calculating final predicted load values based on the values of the first and second counter.
16 more elements
Dependencies are inferred from the claim text.