A short-term load forecasting platform based on big data

Abstract

The invention discloses a short-term load predication platform based on large data. A Hadoop cluster serves as architecture of the short-term load predication platform based on the large data. Locally-weighed linear regression parallel load predication is achieved on the platform through Mapreduce. The platform comprises a data integration module, a load predication module, a result visualization module and a user management module. The data integration module completes the complete data processing flow from data acquisition and loading relevant to loads and data storage to the final multi-factor large data integration fusion technology. The load predication module achieves parallel load prediction through Mapreduce of a locally-weighed linear regression algorithm, and self-learning and self-adapting load prediction is achieved through parameter adjustment. The result visualization module is a specific showing layer of a platform prediction result, and a real-time analysis technology is dynamically shown. The user management module is a safety mechanism layer of the platform, and achieves safe, reliable and efficient running of the load prediction platform.

Description

technical field [0001] The invention relates to a big data-based short-term load forecasting platform with Hadoop cluster as the framework. Background technique [0002] Power system load forecasting is one of the important tasks of power system dispatching, power consumption, planning and planning and other management departments. Accurate load forecasting is conducive to economically and rationally arranging the start and stop of power generation units inside the power grid and maintaining the safety and stability of power grid operation. , reduce unnecessary rotating reserve capacity; it is beneficial to electricity management, reasonable arrangement of power grid operation mode and unit maintenance plan, to ensure the normal production and life of the society. [0003] In recent years, with the rapid development of science and technology, academia, and social economy, big data technology has become a global research hotspot, and the rapid development of corresponding sen...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a scalable load forecasting process that can process a variety of massive power consumption data at high speed and can realize the integration, parallelization, and self-adaptation of data loading, data processing, load forecasting, parameter control, and visualization. Load forecasting platform to solve the problems of limited data processing quantity, low forecasting speed and single processing data structure in the current load forecasting system

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