To improve the efficiency of photovoltaic (PV) power forecasting, the method of feature fusion combined with improved temporal convolutional network (TCN) is proposed. The correlation coefficient approach is utilized to examine the time series features, and the effective input for feature fusion is calculated. To increase the accuracy of generating power forecasting, the TCN expansion parameters and connection modes are adjusted. The proposed method is evaluated on two different power plant data sets in South China, and it is compared to the classical algorithms LSTM, GRU, 1D-CNN, and TCN, as well as diverse weather samples. The results reveal that the approach described in this paper achieves a decisive coefficient of 0.982 and outperforms other algorithms in terms of fitting ability. The training time of the model is only 30 s, and the prediction efficiency is greatly improved.