The Green Photovoltaic Industry Installed Capacity Forecast in China: Based on Grey Relation Analysis, Improved Signal Decomposition Method, and Artificial Bee Colony Algorithm

Abstract

Despite photovoltaics being a new type of green energy technology, the output of the photovoltaic industry has been declining year by year since 2018. Thus, China’s photovoltaic industry must adapt to transformations from original extensive growth to the pursuit of high-quality energy. In order to accurately predict the installed capacity of photovoltaics in China, based on an extensive literature review and expert consultation, this paper is the first to construct a set of influencing factors that affect the photovoltaic industry, and we selected the main influencing factors as the predictive model’s input through the grey correlation analysis method. Then, we provide a novel grid investment forecast named the CEEMD-ABC-LSSVM predictive model (a least squares support vector machine algorithm based on complete total empirical mode decomposition and an artificial bee colony algorithm). This algorithm is based on the traditional LSSVM algorithm. The ABC algorithm is used to optimize the parameters, while CEEMD decomposes the original time series to obtain multiple components. While maintaining the data information, the data are expanded and the training is fully carried out. Next, in the empirical analysis, by comparing the prediction results of LSSVM, ABC-LSSVM, and the EMD-ABC-LSSVM algorithm, we demonstrate that the CEEMD-ABC-LSSVM model has strong generalization capabilities and achieves good Chinese PV growth based on the predicted effects of the installed capacity. Finally, the CEEMD-ABC-LSSVM model was used to predict the installed PV capacity in China from 2019 to 2035. We find that China’s installed PV capacity will surpass 4000 GW around 2035. As this installed capacity will increase year by year, China’s PV industry development will maintain steady overall growth.

Forecasting of China’s solar PV industry installed capacity and analyzing of employment effect: based on GRA-BiLSTM model

“…In the case of missing data, the method based on SVR can effectively deal with the problem of missing data (Luo et al 2018 ; Li et al 2018 ). To predict the future solar installed capacity, Liu et al ( 2020 ) tested the applicability of the CEEMD-ABC-LSSVM model to this kind of problem through the historical solar PV installed capacity data. At the same time, based on good accuracy, the model was further used to predict the future solar PV installed capacity growth.…”

Forecast of Short-Term Electricity Price Based on Data Analysis

“…e result of GCA is stable and fast, and GCA has good performance in removing irrelevant data. Many scholars have researched GCA [24][25][26]. erefore, this paper uses GCA to select important samples and their important features.…”

Forecasting of China’s Solar PV Industry Installed Capacity and Analyzing of Employment Effect: Based on GRA-BiLSTM Model

“…In the case of missing data, the method based on SVR can effectively deal with the problem of missing data (Luo, 2018). To predict the future solar installed capacity, Liu et al(2020) tested the applicability of the CEEMD-ABC-LSSVM model to this kind of problem through the historical solar PV installed capacity data. At the same time, based on good accuracy, the model was further used to predict the future solar PV installed capacity growth.…”