. Short-term Power Prediction of Distributed Photovoltaic Systems Based on Multi-scale Feature Fusion using TPE-CBiGRU
Hongbo Zou1,2，Changhua Yang1*，Henrui Ma3,4，Suxun Zhu3，Jialun Sun5，Jinlong Yang1，Jiahao Wang3
1College of Electrical Engineering and New Energy, China Three Gorges University, Yichang, China
2Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station, China Three Gorges University, Yichang, China
3College of Energy and Electrical Engineering, Qinghai University, Xining City, Qinghai Province, China
4College of Electricity and Automation, Wuhan University, Wuhan, Hubei, China
5Zhangjiakou Power Supply Company of State Grid Jibei Electric Power Co.
*18385615363@163.com
Abstract: To address the key challenges of insufficient comprehensive extraction and fusion of meteorological conditions, temporal features, and periodic characteristics of power in short-term power prediction of distributed photovoltaic (PV) stations, a TPE-CBiGRU model prediction method based on multi-scale feature fusion is proposed. Firstly, multi-scale feature fusion of meteorological features, temporal features, and hidden periodic features in PV power is performed to construct model input features. Secondly, CNN and Bi-GRU are utilized to model the feature relationships between PV power and its influencing factors from spatial and temporal scales, respectively, and the spatial-temporal features extracted are fused through an Add network. Finally, the Bayesian hyperparameter optimization method is adopted to further optimize network parameters, achieving the prediction of single-station PV power. Validation using measured data from a certain PV station shows that the proposed method enhances the comprehensiveness of feature information extraction from both data and model layers, significantly improving the accuracy of short-term PV power prediction. Compared with other prediction models, the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are reduced by 26.11% and 35.64%, respectively, and the R-squared (R2) is increased by 3.07%.

## Introduction

By the end of 2023, China’s installed capacity of distributed photovoltaic (PV) systems reached 60,891.8 MW, with new capacity surpassing centralized PV for two consecutive years [1]. Distributed PV systems can be installed and utilized on the user side, reducing transmission losses associated with long-distance power delivery. However, the high penetration of distributed PV introduces new challenges for grid operation and management [2-4]. The randomness and volatility of distributed PV generation, especially during midday low and evening peak periods, exacerbate the difficulty of power system integration and supply, leading to the ”duck curve” phenomenon in the net load curve. This increases system uncertainty and the cost of full absorption, impacting the economic operation of the grid [5]. Furthermore, the large-scale grid integration of distributed PV systems promotes the transition of distribution networks towards gridization, activation, and automation, imposing higher requirements on power control, voltage regulation, and communication methods in distribution networks [6]. Additionally, as the proportion of distributed PV installations increases, the difficulty of maintaining power balance scheduling rises, and the demand for flexible resources becomes more urgent. Distributed PV systems, in comparison to conventional power sources, lack reliable substitute capabilities in supporting power and energy, further aggravating the system balance contradiction [7]. In this context, high-precision short-term power prediction technology for distributed PV is crucial for optimizing power system operation strategies, assisting power system scheduling, and reducing operational risks. It is key to improving grid planning and operational efficiency [8, 9]. Therefore, developing short-term power prediction methods for distributed PV to address these challenges is particularly urgent and significant.
Currently, in the field of photovoltaic (PV) power prediction, although substantial research has been conducted with significant progress, the focus has primarily been on power prediction for PV stations or PV clusters, as summarized in Table 1. Prediction methods are mainly divided into traditional methods and artificial intelligence (AI) methods. Traditional methods include physical modeling and statistical methods [10, 11]. Physical modeling typically involves creating models based on the physical characteristics of PV generators, meteorological conditions, and environmental factors, which align more closely with actual physical processes. For instance, reference [12] proposed a five-parameter PV system simulation model that combines algebraic and analytical techniques, which has been validated to predict the output power of PV stations under real conditions. Another study [13] improved the accuracy of PV station power prediction by forecasting solar radiation and temperature. While physical methods for PV power prediction can partially elucidate the system’s operating principles, their limitations—such as complex interaction relationships, difficulty in accurately obtaining parameters, high computational complexity, and sensitivity to environmental changes—restrict their reliability and accuracy in practical applications. Consequently, an increasing number of researchers are turning to statistical methods for modeling [14].
Statistical methods include time series analysis, regression analysis, and machine learning, with commonly used techniques such as support vector machines (SVM)[15] and random forests (RF)[16]. These methods generally have lower computational costs and do not require complex physical modeling and simulation. For instance, reference [17] developed a hybrid model combining an Improved Whale Optimization Algorithm (IMWOA) with SVM (IMWOASVM) for short-term PV power prediction. The IMWOA optimizes the kernel function parameters and penalty coefficients in SVM, enhancing prediction accuracy. Another study [18] proposed an improved Markov Chain model, which effectively increased the accuracy and applicability of probabilistic PV power output prediction by integrating conditional probability structures of influencing factors, rough set theory-based factor selection, and K-nearest neighbor similar sample mining. However, distributed PV systems, composed of numerous small PV generation units located in various geographic locations, are more dispersed and flexible compared to traditional centralized PV stations. This dispersion results in significant local microclimate impacts on distributed PV systems. Traditional methods have limited capacity to handle such complex nonlinear issues, making it challenging to fully exploit the nonlinear feature mapping relationships between various characteristics and PV power in the data, thus hindering improvements in prediction accuracy [19].
With the rapid development of artificial intelligence technologies, deep learning has gained significant attention from researchers due to its powerful nonlinear fitting and generalization capabilities. It is now widely applied in various fields, including power system fault diagnosis and prevention [20], energy prediction and optimization [21], power market analysis and decision support [22], and renewable energy power prediction [23]. Common deep learning algorithms include Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRU). For instance, reference [24] proposed a multivariate ultra-short-term centralized wind farm power prediction method based on LSTM networks, enhancing prediction accuracy by selecting variables highly correlated with power using a distance analysis method. Another study [25] introduced a centralized PV power prediction model based on a dual-attention mechanism GRU network and similar sequence correction. This model enhances feature extraction by incorporating feature and temporal attention modules and improves prediction accuracy by correcting the prediction results with future values of similar power sequences. However, the aforementioned studies primarily considered meteorological features such as solar irradiance, wind speed, and wind direction, without deeply exploring the periodic characteristics of power, limiting further improvement in model fitting performance. Additionally, LSTM networks, while powerful, are complex, computationally intensive, and time-consuming to train, often leading to overfitting. Although GRU simplifies the structure, it still has limitations in capturing long-range dependencies and bidirectional long-term sequence features. In contrast, Bidirectional GRU (Bi-GRU) combines forward and backward GRU layers, better capturing the bidirectional dependencies in PV power time series, thereby improving prediction accuracy and model generalization capabilities while maintaining high computational efficiency.
Compared to manual parameter tuning, hyperparameter optimization algorithms offer significant advantages. For instance, reference [26] proposed an ultra-short-term centralized PV power prediction method combining the Whale Optimization Algorithm (WOA) and a parallel deep learning model (PMCI-PCNN-BiLSTM). This method improved prediction accuracy and model generalization by extracting temporal and spatial features from historical data through similar day clustering, multi-mode decomposition, and multi-channel input. Another study [27] introduced a PV power prediction method based on short-term power feature clustering, utilizing the K-ISSA-LSTM model, which optimizes the hyperparameters of the Long Short-Term Memory (LSTM) network by combining the K-means clustering algorithm and an improved sparrow search algorithm (ISSA).The Whale Optimization Algorithm (WOA) is known for its strong global search capability and fast convergence speed, but it has insufficient local search ability and is sensitive to parameters. The Improved Sparrow Search Algorithm (ISSA) enhances the ability to escape local optima by balancing exploration and exploitation, although it is more complex and has unstable convergence speeds. In contrast, the Bayesian hyperparameter optimization algorithm based on Tree-structured Parzen Estimator (TPE) constructs and optimizes a probabilistic model, efficiently exploring the hyperparameter space and achieving rapid convergence. It also offers automated tuning and strong robustness, making it significantly superior to WOA and ISSA in terms of efficiency, accuracy, and applicability in hyperparameter optimization.
Centralized and distributed photovoltaic (PV) systems exhibit significant differences in the spatial characteristics between meteorological features and power output. Centralized PV systems are concentrated at single sites where meteorological conditions are relatively uniform, and spatial features are simple, relying primarily on temporal variations for prediction. For example, reference [28] proposed a Dynamic Convolutional Generative Adversarial Network (DC-GAN) method based on satellite cloud image video frames, which combined radiative data from centralized PV stations, spatial correlations of distributed stations, and cloud cover characteristics to construct a prediction model, thereby improving prediction accuracy. Another study [29] presented a regional distributed PV output prediction method based on spatial correlation. This method equated distributed stations within a region to a virtual centralized station, using the Adaptive Neuro Fuzzy Inference System (ANFIS) to predict the output of the centralized station and then using polynomial fitting to predict the output of distributed stations.However, these methods focus on predicting the output of distributed PV stations within a certain region and do not address predictions for single distributed PV stations. The diverse geographical locations of individual distributed PV systems lead to significant differences in meteorological conditions, characterized by complex spatial heterogeneity and lower correlation. Therefore, it is crucial to handle and fully utilize the spatiotemporal feature correlations between meteorological characteristics and power output in individual distributed PV systems to enhance prediction accuracy.
Table 1 Comparison of forecasting methods