Energy load prediction system based on big data

Abstract

The invention discloses an energy load prediction system based on big data. The energy load prediction system comprises the following steps: (1) receiving an energy use condition in a to-be-detected area through big data; (2) storing all the collected data in a Kafka cache cluster, and forwarding a message of storing the data in the Kafka cache cluster; (3) carrying out time, temperature and load capacity task classification, wherein the task classification comprises the step of feeding back a task receiving request to the big database according to the working state of each area; (4) acquiring a to-be-predicted moment in the to-be-predicted area, an estimated temperature of the to-be-predicted moment and a plurality of historical load capacities of to-be-predicted energy; (5) carrying out task initialization and information analysis; (6) inputting the data into an energy load prediction model to obtain the predicted load capacity of the to-be-predicted energy. The system has the beneficial effects that the energy load can be effectively predicted through big data processing, and the use effect is good.

Description

Technical field [0001] Technical Field The present invention relates to a power, in particular a large energy load forecasting system based on data. Background technique [0002] With the production and living needs of the increasing demand for energy load also increased, in order to ensure adequate energy and maximum possible to avoid waste of energy for the company's staff can usually predict the energy future time periods load, and according to a load prediction of the amount of energy, the energy load adjustment period in the future. [0003] Currently, the process of forecasting energy load, the energy supply company's staff are generally based on historical load cases a particular kind of energy, energy load forecasting the next phase of the case; and the actual production, living in different Energy load between affect each other. For example, residents of an area accustomed to using heat energy, corresponding to the load of the residents of this area will be energy-year d...

AI Technical Summary

Problems solved by technology

For example, if the residents in a certain area are accustomed to using heat energy, correspondingly, the electric energy load of the residents in this area will be lower than that of other areas in the same period. The prediction accuracy is low, and it is impossible to accurately predict the load of specific types of energy in the future time period

[0004] Based on this, there is an urgent need for a prediction method of energy load in a region, which is used to solve the problem of low accuracy in predicting a specific type of energy load in a region in the prior art