Although methods for predicting centralized PV power output can be referenced, distributed PV systems exhibit fundamental differences in system characteristics. Centralized PV power stations, due to their scale and geographic diversity, can achieve a certain level of output complementarity, thereby reducing overall output fluctuations. In contrast, distributed PV systems consist of small, dispersed units whose outputs are affected by local microclimatic conditions such as smoke and shadows, necessitating individual predictions. The geographic diversity of distributed PV systems leads to complex meteorological conditions with significant spatial heterogeneity and low correlation. Current methods are inadequate in handling and fully leveraging the spatiotemporal correlations between meteorological features and power output.Therefore, distributed PV power prediction methods need to be specifically designed to effectively capture and utilize these complex spatiotemporal features. Additionally, existing methods in model construction and power mapping processes fail to fully exploit the comprehensive situation and trends in the power generation dataset across different time scales and power cycle scales. They overlook important features in the sequence information, resulting in significant deviations between the predicted and actual power curves, thereby reducing prediction accuracy. Thus, developing short-term power prediction methods tailored for distributed PV systems to address these challenges is particularly urgent and important.
In summary, addressing the key challenges in short-term power prediction for distributed photovoltaic (PV) stations, this paper proposes a TPE-CBiGRU model prediction method based on multi-scale feature fusion. Feature fusion is performed at both the data and model layers, utilizing Convolutional Neural Networks (CNN) for spatial feature extraction, Bi-GRU networks for time series feature modeling, and an Add layer for feature fusion. Additionally, a Bayesian hyperparameter optimization method based on Tree-structured Parzen Estimator (TPE) is employed to fine-tune model parameters. The main contributions of this paper are as follows:
1. Comprehensive Feature Engineering Process: A comprehensive feature engineering process is proposed, integrating multi-scale features such as meteorological conditions, temporal characteristics, and the periodic features of PV power generation. Compared to models that only consider meteorological conditions, this integration improves the fitting degree R2 by 3.07%, enhancing the accuracy of short-term PV power prediction.
2. Bayesian Hyperparameter Optimization with TPE: The application of Bayesian hyperparameter optimization using TPE in the CBiGRU model is demonstrated. This method efficiently searches for the optimal hyperparameter combination, with a total experimental duration of 21 hours, 35 minutes, and 55 seconds, significantly reducing the time required compared to manual methods and improving the efficiency and accuracy of model optimization.
3. TPE-CBiGRU Model for Distributed PV Short-term Power Prediction: The TPE-CBiGRU model is proposed for short-term power prediction of distributed PV systems. By capturing spatial and temporal features using CNN and Bi-GRU networks, respectively, and fusing features through an Add layer, the model reduces the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by 26.11% and 35.64%, respectively. It also demonstrates higher prediction concordance across multiple time scales, validating the model’s effectiveness in uncovering intrinsic data relationships.
The structure of the remaining sections of this paper is as follows: The introduction section introduces the research background and significance of distributed photovoltaic (PV) power prediction. Next, the first part delves into the concept of multi-scale feature fusion, including a detailed description of feature fusion at both the data layer and the model layer. The second part elaborates on the TPE-CBiGRU short-term PV power prediction model, covering the model framework, parameter optimization strategies, and prediction process. The third part provides a case study analysis, including experimental platform configuration, data sources, selection of evaluation metrics, comparative analysis of parameter optimization methods, performance comparison of different prediction models, and validation of the effectiveness of model key components through ablation experiments. Finally, the conclusion section summarizes the research findings of this paper.

## Multi-scale Feature Fusion

In the field of distributed photovoltaic (PV) power generation, the unique geographic locations and operating mechanisms mean that power generation is influenced not only by meteorological conditions, such as solar irradiance and temperature variations, but also by temporal characteristics, such as seasonal changes and different times of the day. The dynamic interactions of these multi-dimensional features pose challenges to the stable operation of the grid while providing potential information sources for accurate PV power prediction. To fully exploit and effectively utilize this information, improving the accuracy and robustness of prediction models, this study proposes an innovative multi-scale feature fusion strategy.
Feature fusion is a critical technique in time series data analysis, aiming to merge features from multiple time scales or different sources into a unified feature set to enhance the model’s capability to represent data and improve prediction accuracy [30, 31]. This process includes steps such as feature extraction, transformation, fusion, and selection. By effectively integrating multi-source features, the model can better capture the spatiotemporal characteristics of the data. Additionally, feature fusion plays a noise reduction role in data analysis. By integrating information from multiple time scales or feature sources, it can reduce noise interference in the data, extracting more stable and reliable feature representations, thereby improving the performance and robustness of the model [32, 33]. Feature fusion has broad application prospects in time series prediction, signal processing, financial analysis, and other fields.
To fully exploit the key information in the data, this paper extracts and fuses feature information at both the data layer and the model layer:
Data Layer: First, based on resampling techniques, features related to time scales and periodic scales in the data are extracted to construct time feature matrices and periodic feature matrices. These feature matrices are then aligned to the same granularity and concatenated. Finally, they are merged with the original input data matrix to obtain the multi-scale feature input matrix at the data layer.
Model Layer: The data is input into CNN and Bi-GRU models, where high-dimensional spatial features are extracted by the CNN model and multi-dimensional temporal features are extracted by the Bi-GRU model. These feature layers are then combined using an Add layer to further integrate the feature information. This approach leverages the strengths of both CNN in capturing spatial dependencies and Bi-GRU in modeling temporal sequences, enhancing the overall prediction performance of the model.
Data-level Feature Fusion
The core of feature fusion at the data layer lies in constructing the time feature matrix and the periodic feature matrix. The time scale feature matrix includes time series features closely related to photovoltaic (PV) power, which can provide systematic time series data for model training. In this strategy, time features encompass year, season, month, date, hour, and minute. These time features comprehensively reflect the operating environment of the PV system and external influencing factors. Specifically, changes in the year reflect long-term trends and variations in the system environment, seasonal changes, and months reflect variations in daylight duration. Dates and hours reflect daily peak and off-peak electricity consumption periods, directly related to the power demand of the PV system.
Additionally, compared to centralized PV plants typically located in remote and open areas, distributed PV systems are often installed in urban environments, making them more susceptible to local microclimate factors such as chimney emissions, shadows from tall buildings, and other local microclimatic conditions. By introducing high time resolution (15 minutes) data features, the model can more accurately capture the subtle fluctuations in the environment of distributed PV generation, which is crucial for improving the accuracy of distributed PV power prediction models. These features are summarized in Table 2:
Table 2 Temporal Scale Features