Quantifying Spatiotemporal Dynamics of Solar Radiation over the Northeast China Based on ACO-BPNN Model and Intensity Analysis

Abstract

Reliable information on the spatiotemporal dynamics of solar radiation plays a crucial role in studies relating to global climate change. In this study, a new backpropagation neural network (BPNN) model optimized with an Ant Colony Optimization (ACO) algorithm was developed to generate the ACO-BPNN model, which had demonstrated superior performance for simulating solar radiation compared to traditional BPNN modelling, for Northeast China. On this basis, we applied an intensity analysis to investigate the spatiotemporal variation of solar radiation from 1982 to 2010 over the study region at three levels: interval, category, and conversion. Research findings revealed that (1) the solar radiation resource in the study region increased from the 1980s to the 2000s and the average annual rate of variation from the 1980s to the 1990s was lower than that from the 1990s to the 2000s and (2) the gains and losses of solar radiation at each level were in different conditions. The poor, normal, and comparatively abundant levels were transferred to higher levels, whereas the abundant level was transferred to lower levels. We believe our findings contribute to implementing ad hoc energy management strategies to optimize the use of solar radiation resources and provide scientific suggestions for policy planning.

A method for indoor Wi-Fi location based on improved back propagation neural network

“…Thus, the correctness can even be guaranteed. The algorithm proposed in this paper mainly compares with the ACO-BPNN algorithm proposed by Li et al [23]. In order to make the performance of this algorithm more persuasive, the algorithm also compares with the classical BP algorithm and the γ -BPNN algorithm which combines the BP algorithm and momentum term.…”