Abstract
Aiming at the problem of transformer manufacturing enterprises bidding is lacking scientific theoretical guidance and low bid probability, in order to predict the next bid price, based on principal component analysis (PCA) and artificial neural network (ANN) pre-tender estimate forecast model is proposed. The model uses PCA to preprocess the original high dimensional data, select principal components (PC) as the radial basis function (RBF) neural network's input. PCA eliminates the correlation of the input variables, at the same time of simplifying the structure of ANN, improving the accuracy of the prediction model. The simulation results show the applicability of the pre-tender estimate forecast model.
Highlights
The electric power industry in China implement bidding system time is relatively short, which lacks power equipment bidding theory and bidding experience
The pre-tender estimate forecast have focused on using time series[1], fuzzy mathematics[2], gray fuzzy theory[3], game theory[4] to establish model
artificial neural network (ANN) is a hots pot field of artificial intelligence research and application, which is widely used in all kinds of the field of prediction due to its good nonlinear approximation performance, such as coal production forecast [8], the city built area forecast [9], traffic generation forecast [10], etc
Summary
The electric power industry in China implement bidding system time is relatively short, which lacks power equipment bidding theory and bidding experience. It is difficult to establish a deterministic model to predict the fluctuation of the bidding price, resulting in no basic data for reference for the bidding quotation of the power company, but more use of a small amount of experience reduces the probability of successful bids. At this stage, the pre-tender estimate forecast have focused on using time series[1], fuzzy mathematics[2], gray fuzzy theory[3], game theory[4] to establish model. The PCA-RBF pretender estimate forecast model is constructed, and base on bidding data of a certain type transformer in the State Grid Corporation of China, an empirical study is carried out
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