# TCNformer Model for Photovoltaic Power Prediction
## 1 Introduction  
At present, with the rapid development of perovskite solar cell technology [1; 2], the maximum efficiency [3] and stability [4] of photovoltaic power have been greatly improved. Photovoltaic power is increasingly important in the field of new energy. According to the data of the International Energy Agency (IEA), the growth rate of global photovoltaic installed capacity has reached as much as 49%. It is estimated that global photovoltaic power will reach 16% of the total power in 2050 [5]. At the same time, China is promoting the construction of a new power system with new energy as the principal part. Photovoltaic power using solar energy is an important branch of new energy and one of the important means for China to achieve the goal of carbon neutrality. After the large-scale integration of photovoltaic power stations into the energy network, the manner by which to accurately predict photovoltaic power and then accordingly dispatch the power grid has become an urgent problem to be addressed. Therefore, improving the prediction accuracy of photovoltaic power is significant for improving the operation efficiency of power stations themselves and for maintaining the stability of power grids. Many scholars in China and abroad have carried out a lot of research on the prediction of photovoltaic power. At present, the mainstream prediction methods focus on traditional random learning and deep learning methods. In the field of traditional random learning, literature [6] uses historical weather data and historical power data as inputs of a support vector machine (SVM) to build a short-term photovoltaic power prediction model, which has a higher level of accuracy than the traditional autoregressive model (AR) or the radial basis function (RBF) models. One study [7] proposed a model based on Support Vector Regression (SVR) and achieved better prediction performance. In the field of deep learning, recurrent neural network (RNN) structures, such as long short-term memory (LSTM), gated recurrent unit (GRU), and seq2 seq structural models, are widely used to analyze and predict time series data for such applications as stock price prediction [8], gold price prediction [9],traffic flow [10], voice classification [11], etc. The prediction of photovoltaic power can also be regarded as a kind of time series data prediction, so the above algorithms have been used to predict short-term global horizontal irradiance (GHI) or comprehensive solar loads [12; 13]. Furthermore, in order to ensure the accuracy as much as possible and reduce the training time, the GRU network has been applied to short-term photovoltaic power prediction [14], and the multivariable GRU model [15; 16; 17] has been used to predict solar irradiance or power. Some hybrid models have been applied in the field of photovoltaic power generation prediction, such as the combination of a deep learning model and a heuristic algorithm [18; 19], the combination of a deep learning model and a traditional random learning method [20; 21], the combination of multiple deep learning models [22; 23], etc. The seq2 seq structural model represented by the Transformer series model takes the photovoltaic power prediction problem as a experimental sample of its model, such as Autoformer and Informer [24; 25]. However, in these models, usually the photovoltaic power prediction data are only used for prediction; that is, the corresponding weather data is not fully used, and the time series features of the data are not fully extracted. Compared with traditional LSTM, GRU, and other models, the Transformer series seq2 seq model can avoid the problem of error accumulation and read longer input data [26], but it is still limited by the length of the input data. It is difficult for the seq2 seq series model to capture longer time series features. For this problem, [27] proposes long- and short-time series network (LSTnet) models. The Skip recurrent neural network (Skip RNN) structure is used to capture more long-term time series features.