## 1 Introduction
This approach has been shown to effectively reduce the amount of computation via experimental simulations. However, some researchers [40; 41; 42] have discovered that such methods were not helpful in practice because pruning some of the weights in a deep learning model causes their weight matrices to become sparse. Many existing deep learning models are computed using GPUs in the real world; at present, GPUs will fill in missing values with 0 when calculating sparse matrices and then treat them as complete matrices, which means that the amount of computation has not been reduced. A number of researchers [43; 42] therefore proposed pruning entire hidden layers would be more effective than simply pruning several weights. The sum of the weights in each hidden layer is calculated, and hidden layers with a weight sum below a certain threshold are regarded as excessive and thus are removed. Clearly, this approach can prevent GPUs from performing unnecessary calculations, ultimately reducing computation. However, it has been said that this approach merely treats the symptoms but does not cure the disease. Early on in the development of deep learning theory, many stressed that all of the factors that could be collected into the network should be included before using the massive network architecture to clarify the relationships between each factor and the model outputs. However, while conducting explainability analysis of models in recent years, many researchers found that many input factors of deep learning models are actually of no help to the output, and including these useless input factors comes at great cost to architecture size and computation. For this reason, [PERSON]. [44; 45; 46; 47; 48] proposed the framework shown in Figure 2 to establish models with high precision but reduced computation costs. In this framework, researchers similarly define, establish, and train a suitable deep learning model, extract the key features identified by the trained model, and construct a model using only these key features and lightweight model. The lightweight models established using this framework have much fewer input factors and highly reduced computation, but their accuracy is not that much lower than that of the original deep learning model. This was the approach selected for the current paper. We designed a novel methodology based on the concept depicted in Figure 2 to perform robust predictions for solar power generation at extremely low cost. Figure 3 displays the operating framework of our novel methodology. In the first phase, we similarly establish and train a deep learning model. Next, we analyze the parameters of the trained model to rank the factors according to their importance to solar power forecasting. In the second phase of the methodology, we identify multiple factor combinations with the least number of testing in the importance ranking from the first phase. Next, we establish lightweight models for each of the factor combinations so that they can all generate near-optimal prediction results. Finally, we perform ensemble calculations of the outputs of the lightweight models. These results are robust, as the lightweight models each use different factor combinations, so when errors occur in some of the factors, they will only affect a small number of the lightweight models and not all of them. Furthermore, once we perform ensemble calculations of the lightweight model outputs, the errors in some of the factors in the small number of lightweight models will be further weakened, thereby achieving robustness. Figure 1: Three existing methods for pruning deep learning model architecture include (1) pruning model weights, (2) pruning entire hidden layers, and (3) pruning unnecessary input factors. Figure 2: Flow chart of factor reduction for deep learning architecture pruning.