Energy supply and demand forecasting method based on support vector machine

Abstract

The invention relates to an energy supply and demand forecasting method for a tobacco enterprise. At first, historical data of energy supply and demand is pre-processed, preprocessing comprises rejecting abnormal data, special point analysis and data denoising, and then all data indicators are used to build a support vector machine model, and then the support vector machine model is conducted solving. Tobacco energy supply and demand forecasting historic data is firstly obtained, and then the data is screened, obvious abnormal data is rejected, and the abnormal data mainly refers to energy waste caused by manual work and abnormal tobacco quality caused by abnormal equipment, and then obtained input and output data are utilized to carry out simple preprocessing, and a model is built through the support vector machine and a least square support vector machine. People choose two kinds of kernel functions in a parallel mode, namely a polynomial function and a radial basis function (RBF), wherein the RBF is used as a main body to build the model, and a result that a certain relation (generally named weight) is multiplied by the polynomial function is used as model correction, so that forecasting precision can be improved to a great extent.

Description

technical field [0001] The invention belongs to the technical field of cigarette production, and in particular relates to an energy supply and demand prediction method based on a support vector machine. Background technique [0002] On the premise of ensuring stable supply and safe production, how my country's tobacco companies can efficiently use limited energy, optimize the structure of supply and demand, make full use of secondary energy, and reduce waste caused by the imbalance between energy supply and demand is a very important issue. Efficient dispatching of energy based on forecasted changing trends is crucial. At present, the energy forecasting of most domestic tobacco companies is short-term forecasting based on artificial experience, which requires relatively high experience for forecasters and lacks the support of forecasting models. Most studies on energy supply and demand focus on single energy or two energy supply and demand forecasts, and few include multipl...

AI Technical Summary

Problems solved by technology

Although the neural network model has a high nonlinear mapping ability and can approximate nonlinear functions with arbitrary precision, there are still some problems in actual calculations: ① The calculation process of backpropagation has a slow convergence speed, generally requiring hundreds of thousands of ② there is a minimum value of the energy function; ③ the number of hidden neurons and the selection of connection weights often rely on experience; ④ the convergence of the network is related to the structure of the network, etc.

Although the model is simple, the credibility of the model is quite high under the premise of determining the accuracy

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