Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

Regional power load prediction method and system

A technology of power load and forecasting method, which is applied in the field of regional power load forecasting method and system, can solve the problems such as the accuracy needs to be improved, and achieve the effect of balancing transmission and supply and improving forecasting accuracy

Inactive Publication Date: 2019-10-18
武汉四创自动控制技术有限责任公司
View PDF4 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, due to the randomness and volatility of residential electricity consumption, the accuracy of existing medium and long-term load forecasting methods needs to be improved.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Regional power load prediction method and system
  • Regional power load prediction method and system
  • Regional power load prediction method and system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0070] Embodiment 1 provided by the present invention is an embodiment of a regional power load forecasting method provided by the present invention. This method is suitable for a network system in which a hydropower station supplies power to surrounding residential buildings. The power supply objects of the hydropower station are all residential buildings within a certain range. And the generating set of the hydropower station itself. Such as figure 2 Shown is the flow chart of the embodiment of a kind of regional power load forecasting method provided by the present invention, by figure 2 It can be seen that an embodiment of a regional power load forecasting method provided by the present invention includes:

[0071] Step 1, collecting the historical data of electric load in the corresponding collection area.

[0072] After the historical data is collected in step 1, the historical data is normalized:

[0073]

[0074] x i is the actual value of the power load at th...

Embodiment 2

[0116] Embodiment 2 provided by the present invention is an embodiment of a regional power load forecasting system provided by the present invention, such as image 3 Shown is a structural block diagram of an embodiment of a regional power load forecasting system provided by the present invention, consisting of image 3 It can be seen that an embodiment of a regional power load forecasting system provided by the present invention includes: a power data acquisition module 1 , a model forecasting module 2 and a hydropower station control module 3 .

[0117] The power data collection module 1 is used to collect historical power load data of a corresponding collection area.

[0118] Model forecasting module 2, establishes the RNN forecasting model for power load forecasting, uses the hybrid locust optimization algorithm to optimize the RNN forecasting model, and substitutes historical data into the RNN forecasting model to obtain the power load forecasting value of the collection ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention relates to a regional power load prediction method. The prediction method comprises the following steps: step 1, acquiring power load historical data of a corresponding acquisition area;step 2, establishing an RNN (Recurrent Neural Network) prediction model for power load prediction, and optimizing the RNN prediction model by using a hybrid locust optimization algorithm; and step 3,substituting the historical data into the RNN prediction model to obtain a power load prediction value of the acquisition area. The hybrid locust optimization algorithm is used to optimize the neuralnetwork model to predict the power load. Prediction precision is substantially improved. Problems of large power prediction model training samples and many network adjustable parameters established based on statistical data in a traditional scheme are solved, effective reference is provided for reasonable optimization of use of electric power resources, and electric energy transmission and supplyare balanced.

Description

technical field [0001] The invention relates to the field of power load forecasting, in particular to a regional power load forecasting method and system. Background technique [0002] With the rapid growth of population and rapid economic development, the electricity consumption of residential buildings has increased significantly. In order to balance electricity supply and demand and reduce carbon emissions, the development of smart buildings and smart grids has received more and more attention. At the same time, the intermittence and volatility of renewable energy have a certain impact on the power grid. With the increase of smart electricity terminals in residential buildings, residential electricity consumption has stronger volatility and randomness, which will affect the balance of supply and demand of electricity. Therefore, reliable and accurate load forecasting is of great significance, which helps to realize the dynamic planning and efficient management of smart ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06Q10/04G06Q50/06G06N3/04G06N3/08G06N3/00
CPCG06Q10/04G06Q50/06G06N3/08G06N3/006G06N3/045G06N3/044
Inventor 吕桂林刘辉陈启明丁坦吕在生陈馨凝万英杰
Owner 武汉四创自动控制技术有限责任公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Eureka Blog
Learn More
PatSnap group products