Cloud computation-based smart grid load prediction management platform

Abstract

The invention discloses a cloud computation-based smart grid load prediction management platform. The cloud computation-based smart grid load prediction management platform comprises (1) an infrastructure layer, (2) a load data management, processing and analysis platform layer, (3) a four-type intelligent load service software layer and (4) a cloud interface management layer. By adopting the technical scheme, the cloud computation-based smart grid load prediction management platform has the following advantages and characteristics that 1, by adopting the smart grid load prediction novel technology, the requirement on prediction of the smart grid load can be effectively met; 2, by effectively utilizing the general characteristics of the cloud computation, the resource can be optimally configured, the information such as the load prediction data is shared, and different users can conveniently and effectively acquire the load service at any time; 3, the additional hardware upgrading expense caused by the upgrading of the load prediction platform along the development of the smart grid can be avoided, the device such as different servers is not needed, and the primary development cost and the maintenance upgrading cost can be reduced. By adopting the open-source development environment, the development cost is low.

Description

technical field [0001] The invention relates to the field of power system grid dispatching, and more specifically, relates to a smart grid load forecasting management platform based on cloud computing. Background technique [0002] Load forecasting is a traditional research problem in the field of power systems, which plays an important role in the safe operation of power systems, and is the basic work of energy-saving scheduling, power generation planning, power grid planning, and stability calculations. Numerous examples and data show that accurate load forecasting is very important to rationally design power supply structure, formulate fuel plan, implement energy saving and emission reduction measures, and accelerate the pace of regional economic development. [0003] In the past 20 to 30 years, many experts and scholars at home and abroad have done a lot of research work on the theory and method of load forecasting, and achieved a lot of results. From the perspectives o...

AI Technical Summary

Problems solved by technology

Therefore, load forecasting mainly faces the following problems: First, traditional load data analysis methods cannot meet the massive data analysis and processing requirements of smart grids.

Second, the prediction model under multiple influencing factors is not adaptive

Third, the accuracy of random and intermittent load forecasting is unstable

Fourth, the phenomenon of heterogeneity in the majority of load forecasting systems is prominent, and resources cannot be effectively shared

Fifth, the CPU utilization rate of the load forecasting server is not high, resulting in a serious waste of resources

Load forecasting The CPU utilization of the network, system, and database servers is low, and they are idle or unsaturated most of the time, which seriously wastes resources.

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