Abstract
Countries all over the world are making their contribution to the common goal of energy saving and emission reduction. Solar energy is gaining more attention as a renewable energy source. Polysilicon is an important raw material for solar panels and the production of polysilicon is a vital part of the photovoltaic industry. Polysilicon production is a typical process industry enterprise, and its production process is continuous and highly energy intensive. Therefore, it is necessary to forecast and analyze the consumption of polysilicon production plants. To address the problem that it is difficult to predict future consumption based on historical data alone due to the time-series, massive, nonlinear, and complex nature of data in polysilicon workshops. This study proposes a combined workshop energy consumption prediction model based on Bayesian estimation of time-series decomposition and convolutional neural network (TSD-CNN). The method uses a time-series decomposition method to model the periodic and long-term trend components of the raw consumption data and uses a Bayesian estimation algorithm to optimally estimate the model parameters for each component. With the real-time collection of energy consumption data for equipment, the application of that method described above has successfully improved the accuracy of prediction, production management efficiency and safety warning capabilities in enterprises. Furthermore, it helps to provide decision support for energy conservation and consumption reduction studies. To verify the practicality and reliability of the algorithm in practical applications, experiments were conducted with the energy consumption data of the reduction furnace in the polysilicon production plant, and by comparing it with the commonly used regression methods SVM, LSTM and TSD, the results show that the combined prediction method has a greater improvement in the accuracy of energy consumption prediction.