A Novel Dual-Channel Temporal Convolutional Network for Photovoltaic Power Forecasting

Abstract

A large proportion of photovoltaic (PV) power generation is connected to the power grid, and its volatility and stochasticity have significant impacts on the power system. Accurate PV power forecasting is of great significance in optimizing the safe operation of the power grid and power market transactions. In this paper, a novel dual-channel PV power forecasting method based on a temporal convolutional network (TCN) is proposed. The method deeply integrates the PV station feature data with the model computing mechanism through the dual-channel model architecture; utilizes the combination of multihead attention (MHA) and TCN to extract the multidimensional spatio-temporal features between other meteorological variables and the PV power; and utilizes a single TCN to fully extract the temporal constraints of the power sequence elements. The weighted fusion of the dual-channel feature data ultimately yields the ideal forecasting results. The experimental data in this study are from a 26.52 kW PV power plant in central Australia. The experiments were carried out over seven different input window widths, and the two models that currently show superior performance within the field of PV power forecasting: the convolutional neural network (CNN), and the convolutional neural network combined with a long and short-term memory network (CNN_LSTM), are used as the baseline models. The experimental results show that the proposed model and the baseline models both obtained the best forecasting performance over a 1-day input window width, while the proposed model exhibited superior forecasting performance compared to the baseline model. It also shows that designing model architectures that deeply integrate the data input method with the model mechanism has research potential in the field of PV power forecasting.

Two-Stage Neural Network Optimization for Robust Solar Photovoltaic Forecasting

“…Then, we constructed DL models for multistep-ahead forecasting, starting with robust time series forecasting models from the RNN family, including LSTM, Bi-LSTM, and GRU models [23]. In addition, we developed seven state-of-the-art models that incorporate attention mechanisms (Att) [67][68][69][70][71][72][73]. These steps allowed us to comprehensively compare the established benchmark models, known for their effectiveness in various domains, with our proposed model.…”

“…• Att-1D-CNN: Wu et al [70] proposed the Att-1D-CNN model, which uses a CNN and informer approach, and achieves accurate solar PV power generation prediction by analyzing data correlations, significantly improving the prediction accuracy over traditional models. • Att-TCN: Ren et al [71] introduced the Att-TCN model, a novel dual-channel TCN that combines a multi-head attention mechanism with TCN to extract spatiotemporal features, and demonstrated superior prediction performance for solar PV power generation forecasting.…”

Photovoltaic Power Prediction Based on Irradiation Interval Distribution and Transformer-LSTM

“…It is difficult to achieve better prediction results in practical applications; the data-driven method takes historical data as the research object, and combines data processing methods and deep learning algorithms to establish a photovoltaic power generation prediction model to achieve the accurate prediction of photovoltaic power generation [12]. The current common deep learning algorithms mainly include LSTM [13], gated recurrent units (GRUs [14]), recurrent neural networks (RNNs [15]), convolutional neural networks (CNNs [16]), etc. In [17], Wang, K. stated that the average value, the standard deviation, and the coefficient of variation of the total horizontal radiation variables were used as clustering features, and the fuzzy C-means (FCMs) clustering method was used to divide the historical data into sunny, sunny-tocloudy, and rainy days.…”