## 1 Introduction
Reference [25] presented a data-driven method based on dimensionality reduction and mapping functions. However, distributed photovoltaic systems (DPVS) are not fully metered at present, and data-driven methods require a large amount of PV data. The work in [26] combined clear-sky solar generation models with physical PV panel models to estimate PV power. Reference [27] represented the total generation of distributed PV systems in an area using a virtual equivalent PV power plant model, where the model parameters were obtained by solving optimization problems. Model-based methods mainly estimate solar power generation based on model parameters and weather information [28], but their main drawback is the lack of data support for distributed PV generation forecasting, resulting in low accuracy and introducing larger errors when combined with the load. Due to the limitations in the accuracy of DPVS forecasting, direct modeling of the net load has higher accuracy compared to decoupled forecasting [29; 30]. As there is a strong correlation between irradiance and PV generation [31; 32; 33], irradiance can be directly used as an input in load forecasting models to reflect the impact of distributed PV generation on the load. However, incorporating a large amount of diverse meteorological data as inputs to the model increases the computational complexity [34] and affects the training efficiency [35]. Therefore, when adding meteorological information to the input variables of the forecasting model, it is necessary to combine the handling of multiple input features in order to optimize the forecasting model. This paper proposes a method for input feature reconstruction in the context of complex factors affecting regional loads under the influence of distributed photovoltaics. The method arranges the various influencing factors according to the sampling points and calculates the correlation at the sampling point granularity using the maximum information coefficient (MIC). The MIC values are then used as weights for reconstructing the original input matrix. Finally, the BI-LSTM neural network is used as the prediction model. Through testing on regional load instances, after completing load classification, key influencing features of the regional load with distributed photovoltaics are extracted. These influencingfeatures are sorted, filtered, and reconstructed. This allows the deep learning model to recognize load variation patterns and correct the impact of photovoltaic power generation. The experimental results demonstrate that after applying the proposed feature reconstruction method in this paper, accurate load forecasting results were achieved for the selected region. The deep neural networks used in the experiments yielded precise predictions. Furthermore, compared to methods that solely rely on correlation analysis, the proposed method exhibited a significant improvement in prediction accuracy. In particular, when dealing with regions that have a high penetration of distributed photovoltaic systems (DPVS), the proposed method effectively captures the impact of different variables at different time intervals on the load. This enables the deep neural networks to better track the fluctuations in such loads. This improvement is of great significance for enhancing the accuracy of load forecasting in regions with DPVS. The remaining sections of this paper first introduce the specific process of the proposed method, the data used in the study, and the evaluation metrics for the prediction model. Second, the changes in load curves caused by the integration of distributed photovoltaics are analyzed, and the Gaussian Mixture Model (GMM) method used is introduced. Third, the feature reconstruction strategy based on MIC is explained. Fourth, the BI-LSTM neural network used for prediction is briefly introduced. Finally, clustering analysis, feature reconstruction, and the prediction results are compared based on examples. The Section 7 summarizes the proposed method and evaluates the limitations of this study.