## 1 Introduction  
In recent years, an increasing number of photovoltaic (PV) power generations have been connected to the distribution network. The use of new energy brings huge benefits to human beings. However, it also has negative impacts on the power grid while improving the environment. The PV power is greatly influenced by weather conditions and fluctuates with the change in irradiance. To ensure the quality of the power supply and safe operation of the power system, it is necessary to maintain equal amounts of power generation and power consumption in the power system. The power load forecasting is an indispensable mean of maintaining this dynamic balance. In addition, power load forecasting is also of great significance for the planning and scheduling of power systems and the planning of power maintenance. However, the fluctuations in PV power can cause fluctuations in the load when many PV systems are connected. Therefore, load forecasting will become more difficult. Generally, load forecasting can be divided into long-term forecasting, medium-term forecasting, short-term forecasting, and ultrashort-term forecasting [1]. Recently, many systematic and fruitful studies on traditional load forecasting have been conducted. The load forecasting methods mainly include the similar day prediction method [2; 3], time series prediction method [4; 5], expert system [6; 7], and regression analysis method [8; 9]. Artificial intelligence and machine learning algorithm are types of prediction methods that have rapidly developed in recent years. Support vector regression (SVR) [10], relevance vector regression (RVR) [11], artificial neural network (ANN) [12], deep neural network (DNN) [13], and their improved hybrid algorithms [14] have been applied in the field of load forecasting. In [PERSON] et al. [15], the random forest and multilayer perceptron are combined to predict the daily electrical load. In [PERSON] et al. [16], the wavelet and Kalman machines, Kohonen self-organizing map (SOM), multi-layer perceptron artificial neural network (MLP-ANN) and adaptive neuro-fuzzy inference system (ANFIS) are used to establish a hybrid three-stage forecasting model. In [PERSON] et al. [17], a short-term power load forecasting method with wavelet neural network (WNN) and an adaptive mutation bat optimization algorithm (AMBA) are proposed. [PERSON] et al. [18] propose a hybrid model that combines the empirical mode decomposition (EMD), minimal redundancy maximal relevance (mRMR), general regression neural network (GRNN), and fruit fly optimization algorithm (FOA). In [PERSON] et al. [19], a load forecasting model is proposed based on the complete ensemble empirical mode decomposition (EEMD) with adaptive noise and support vector machine (SVM), which is optimized by the modified grey wolf optimization (MGWO) algorithm. Since traditional load data are typically in hours as the smallest unit, short-term and ultrashort-term forecasts are often not strictly distinguished. With the large-scale access of distributed PV, the short-term load forecasting usually in hours cannot satisfy the requirements of the real-time safety analysis. For the real-time safety analysis of power systems and the reliable operation of economic dispatch, a more detailed ultrashort-term prediction is required. Considering the volatility of the distributed PV power generation and the real-time requirements of the ultrashort-term prediction, the PV power should also be considered a load and merged with the traditional load to form a net load [20; 21; 22]. Because the net load is a set of nonlinear time series with large volatility, in this paper, the phase space reconstruction of net load data is first performed to project the data into the moving point with certain regularity of the trajectory in the phase space. Then, the excellent nonlinear fitting ability of the deep neural network is used to fit the moving point trajectory to obtain the final prediction value.