## 1 Introduction
In the field of machine learning, the requirement for models to produce accurate values while receiving inaccurate input is referred to as a 'robust prediction' problem [19; 20; 21]. This concept has been applied to a number of fields such as automatic control [22; 23; 24] and traffic flow prediction [25; 26; 27]. In solar power generation, research on this topic can be roughly divided into three categories. The first category preprocesses and calibrates the inputs before model calculations to avoid values with excessive errors in the inputs. This prevents model calculations from producing results with excessive errors [28; 29; 30]. However, this approach requires identification of the errors before modeling, so that the model developers know how to preprocess and calibrate the values. Clearly, this is not suitable for the abnormalities that occur in sensors because we cannot know when and what types of errors will occur. This will also, of course, make it difficult to design a suitable processing method. The methods in the second category use broad standards to shield the impact of these errors on the model calculation results without knowing the type of error that has occurred [29; 31]. A variety of ways exist to implement such methods. For instance, researchers [31] set the upper and lower bounds of model loss functions as constants to suppress the impact of abnormal values on output results. Other researchers [29; 32] added random noise to training datasets so that the model would know how to process various types of noise from the outset. Nevertheless, we must understand that such methods require a broad standard to be set beforehand to serve as the basis of implementation. However, the input factorsthat solar power forecasting considers are all weather data, which by nature are extremely diverse and significantly influence one another. Thus, it would be difficult for us to find a broad standard to implement this concept. Finally, the methods in the third category avoid the influence of input errors on model outputs via ensemble modeling [1; 2; 33; 34; 35]. These methods claim that the information received by different models may vary but can all produce similar model output results. Even if some of the model inputs contain errors affecting some of the model outputs, the ensemble of models can weaken the effects of these output errors through the unaffected models, thereby achieving robustness. On the whole, this approach does not require investigating the possible types and range of errors. We therefore selected this as the primary approach for this paper. The greatest shortcoming of the third category of methods is that it increases the amount of model computations enormously, particularly when the existing models were all developed based on deep learning. We therefore propose a novel approach which involves pruning the deep learning model architecture to reduce the amount of computation in each sub-model in ensemble learning and thereby reduce the amount of computation in the overall ensemble learning model. In the past, reducing the computation associated with deep learning models was mostly achieved by pruning the model architecture, which involves first training a deep learning model and then analyzing the model parameters and removing the parameters with the least contribution to model outputs. The three techniques most commonly applied for pruning are shown in Figure 1. In order of development, they are (1) pruning model weights, (2) pruning entire hidden layers, and (3) pruning unnecessary input factors. Researchers who employed the first technique [36; 37; 38; 39] believed that if the weight between two layers of neurons in a model is close to 0, then no matter what the output of the previous layer of neuron is, the value is returned to 0 by the weight once it is passed on to the later layer of neurons. In other words, the outputs of the previous layer of neurons have no impact on the final outputs of the model. Thus, parameters with near-zero weights are pruned.